" ., 

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; 

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/tH£E 

·Preliminary Technical Support 

Document: Energy Efficiency 

Standards for Consumer Products: 

. Clothes Washers (TSD) 

October 1998 

U.S .. Department of Energy 

Assistant Secretary, 

Energy Efficiency & Renewable Energy 

Office of Codes and Standards 

Washington, DC 20585 
This document was prepared for the Department of Energy 

by staff members of the 

Lawrence Berkeley National Laboratory 

and 

Arthur D. Little, Inc. 
• 

TABLE OF CONTENTS 

CHAPTER 1: INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1. 

1.1 Overview .·... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1 

1.2 Structure of the Document . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-3 

CHAPTER 2: ANALYTICAL FRAMEWORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1 

2.1 Background ................... ·............................... 2-3 

2.2 Preliminary Market and Technology Assessment ....................... 2-3 

2.2.1 Market Assessment ....................................... · 2-3 

2.2.2 . Technology Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3 

2.2.3 Preliminary Base Case Shipments Forecast ..................... 2-4 

2.3 Screening Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 

2.3.1 Product Classes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 

2.3 .2 Baseline Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4 

2.3.3 Design Options/Efficiency Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5 

2.3.4 Proprietary Designs ....................................... · 2-5 

2.4 Engineering Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5 

2.4.1 Energy Savings Potential and Manufacturing Costs. . . . . . . . . . . . . . . 2-5 

2.5. Life-Cycle Cost arid Payback Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6 

2.5. l LCC Spreadsheet Model ....................... ·. . . . . . . . . . . . 2-6 

2.5.2 Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 

2.5.2.1 Retail Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-7 

2.6 Preliminary National Impacts Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8 

2.6.1 National Energy Savings (NES) Spreadsheet Model .............. 2-8 

2.6.2 Shipments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9 

2.6.3 Indirect Employment Impacts .. '. ............................ 2-9 

2.7 · Consumer Analysis............................................ 2-10 

2.7.1 Purchase Price Increases .................... ; . . . . . . . . . . . . . 2-10 

2.7.2 Consumer Participation .................................... 2-10 

2.8 Manufacturer Impact Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10 

2.8.1 Industry Cash Flow ...................................... 2-12 

2.8.2 Manufacturer Sub-Group Analysis .......................... 2-12 

2.8.3 Interview Process ................. ·.... . . . . . . . . . . . . . . . . . . 2-12 

2.9 Competitive Impact Assessment ................................. ·. 2-13 

2.10 Utility Analysis............................................... 2-14 

2.11 Environmental Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15 

2.12 Regulatory Impact Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15 

CHAPTER 3: PRELIMINARY MARKET & TECHNOLOGY ASSESSMENT ......... 3-1 

3.1 Introduction ................................... ·. . . . . . . . . . . . . . . . 3-1 

3.2 Product Classes . ~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 
3.3 Manufacturers and Market Shares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 

3.4 Historical Shipments. . . • . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . 3-2 

3.5 Retailers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3 

3.6 Price Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4 

3.7 Product Characteristics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4 

3.8 Market Saturation . . . . . . . . . . . . . . . . . . . . . . . . . . . • . . . . . . . . . . . . . . . . . . 3-7 

3.9 Voluntary Programs (to increase water efficiency I promote horizontal 

axis machines) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8 

3.10 Fuel Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-8 • 

3.11 Industry Cost Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9 

3.12 Technology Assessment ......... .' .... ·. . . . . . . . . . . . . . . . . . . . . . . . . . 3-11 

3.13 Energy Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11 

CHAPTER 4: ENGINEERING ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 

4.1 Introduction ................................................. ; 4-1 

4.2 Product Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 

4.3 Baseline Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1 

4.4 Cost-Efficiency Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2 

4.4.l Characterizing Uncertainty ................................. 4-2 

4.4.2 Variability in costs between Manufacturers ..................... 4-3 

4.4.3 Manufacturer assumptions for Supplying Data ........... : ...... 4-4 

4.5 Data Aggregation .................................... : . . . . . . . . . 4-5 

. 4.6 Retail Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11 

4. 7 Proprietary Design .............................. ·. . . . . . . . . . . . . . 4-11 

CHAPTER 5: MANUFACTURING COST ASSESSMENT .......... : ........ : ..... 5-1 

5.1 Jntroduction ..... ·....................... . . . . . . . . . . . . . . . . . . . . . . 5-1 

5.2 Program Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 

5.3 Phase I Objectives . . . . • . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1 

5.4 Assumptions ................................................ · .·. 5-2 

5.4.1 Baseline Definition ........................ ~. . . . . . . . . . . . . . 5-2 

5.4.2 Manufacturing Assumptions ................................ 5-2 

5.4.3 Conventions for Manufacturing Cost Estimates .................. 5-3 

5.5 Approach ........................ _ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4 

5.5.1 Bill of Materials ......................................... · 5-4 

5.5.2 Fabrication Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 

5.5.3 Assembly Processes . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . 5-7 

5.5.4 Baseline Manufacturing Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . 5-7 

5.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8 

5.7 Variability and Uncertainty in Manufactllring Cost ..................... 5-10 

5.7.1 Key Parameters .... ".................................... 5-10 

5.7.2 Sensitivity Analysis...................................... 5-10 

II 
.. 

5.7.3 Monte Carlo Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14 

5.8 Conclusion and Phase II Program Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15 

CHA.PTER 6: MARK-UPS FOR PRICE DETERMINATION . . . . . . . . . . . . . . . . . . . . . . . 6-1 

6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .. . 6:-1 

6.2 Retail Mark-ups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2 

6.2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 6-2· 

6.2.2 Retail Price Information . '. ........................... : . . . . . 6-3 

6.2.2.1 Consumer Expenditure Surveys . . . . . . . . . . . . . . . . . . . . . . . 6-3 

6.2.2.2 Dealerscope Merchandising Statistical & Marketing Reports . 6-4 

6.2.2.3 ELCAP .Clothes Washer Sales Information . . . . . . . . . . . . . . 6-4 

6.2.3 Manufacturer Price Information ............................. 6-5 

6.2.3. l Current Industrial Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5 

6.2.3.2 AHAM Fact Book ... ·. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-6 

6.2.4 Retail Mark-ups from Manufacturer and Retail Price Information . . . . 6-6 

6.2.5 Retail Mark-ups Estimated from SECs Financial Statements ....... 6-8 

6.3 Sales Tax ............................... · . . . . . . .. . . . . . . . . . . . . . . 6-9 

6.4 Manufacturer Mark-ups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10 

6.4.1 Background ........................ ·................... 6-10 

6.4.2 Manufacturer Price and Mark-up for the Baseline Standard 

Clothes Washer ........................................ : 6-10 

6.4.3 Manufacturer Price and Mark-ups for Different Efficiency Levels ... 6-12 

6.4.3.1 Pre-ANOPRAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-12 

6.4.3.2 Post-ANOPR Analysis ...................... ·. . . . . . 6-12 

CHAPTER 7: LIFE-CYCLE COST and PAYBACK PERIOD ....................... 7-1 

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1 

7.1.1 General Approach for LCC and Distribution PBP ................ 7-1 

7.2 Life-Cycle Cost (LCC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2 

7.2. l Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-2 

7.2.2 LCC Inputs............................................. 7-3 

7 .2.2. l Purchase expense · . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-3 

7 .2.2.2 Lifetime ........................... ; ..... .' . . . . . . . 7-4 

7.2.2.3 Cycles per year ................... ~ ..... · . . . . . . . . . . 7-5 

7.2.2.4 Energy per cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6 

7.2.2.5 Electricity rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-7 

7.2.2.6 Gas rate .................................... , . . 7-12 

7.2.2.7 Oil prices..................... . . . . . . . . . . . . . . . . . . 7-17 

7.2.2.8 Waterpercycle .................................. 7-18 

7.2.2.9 Water and wastewater removal rates .................. 7-19 

7.2.2.10 Discount rate....... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-20 

7.2.2.11 Fuel use of water heater and clothes dryer .............. 7-23 

7.2.2.12 Year to start date . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-24 

iii 
7.2.2.13 Base case design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-24 

7.2.2.14 Standard case design . . . . ... . . . . . . . . . . . . . . . . . . . . . . . . 7-24 

7.2.3 LCC results and preliminary output. . . . . . . . . . . . . . . . . . . . . . . . . . 7-25 

7.3 Distribution Payback Period ............................ , . . . . . . . . 7-28 

7.3.1 Metric .................... ."........ . . . . . . . . . . . . . . . . . . . . 7-28 

7.3.2 Inputs . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . . . ... . . . . 7-28 

7.3.2 Preliminary Results . : . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-29 

7.4 Rebuttable PBP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-30 

7.4.1 Metric... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-30 

7.4.2 Inputs ....................... -. . . . . . . . . . . . . . . . . . . . . . . . . 7.31 

7.4.3 Preliminary Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.32 

7.5 User Instructions for Spreadsheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-32 

CHAPTER 8: NATIONAL ENERGY IMPACTS ................................ 8-1 

8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -8-1 

8.1.1 Methodology and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 

8.2 National Energy/Water Savings (NES) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1 

8.2.1 Definition .......... ~ ..................... : . . . . . . . . . . . . . . 8-1 

8.2.2 NES Inputs ........................ : . . . . . . . . . . . . . . . . . . . . 8-3 

8.2.2.1 Annual Energy Consumption (A£C) .............. 8-3 

8.2.2.2 National Energy Savings (NES) . . . . . . . . . . . . . . . . . . 8-4 

8.2.2.3 Source Energy Conversion (src_conv) ............. 8-4 

8.2.2.4 Stock of Clothes Washers by Vintage (STOCKv) ..... 8-5 

8.2.2.5 Cycles per Year (IlcyciJ . . . . . . . . . . . . . . . . . . . . . . . . . 8-6 

8.2.2.6 Energy per Cycle (UEC) ............ -: . . . . . . . . . . 8-6 

8.2.2. 7 Annual Water Consumption (A WC) . . . . . . . . . . . . . . 8-8 

8.2.2.8 Water per Cycle (UWC) . . . . . . . . . . . . . . . . . . . . . . . 8-8 

8.2.2. 7 Shipments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-9 

8.2.2.8 . Voluntary Programs... . . . . . . . . . . . . . . . . . . . . . . . 8-16 

8.2.2.9 Elasticities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-16 

8.2.3 Preliminary Results ...................... ~ . . . . . . . . . . . . . . . 8-17 

8.3 Net Present Value (NPV) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17 

8.3.1 NPV Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-17 

8.3.2 NPV Inputs.................. . . . . . . . . . . . . . . . . . . . . . . . . . . 8-18 

8.3.2.1 Discount Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-19 

8.3.2.2 Present Value of Costs (PVC) .......... -. . . . . . . . 8-19 

8.3.2.3 Present Value of Savings (PVS) ................. 8-20 

8.3.2.4 Total Equipment Cost ............... , . • . . . . . . 8-20 

8.3.2.5 Total Operating Cost Savings . . . . . . . . . . . . . . . . . . 8-21 

- 8.4 Preliminary Output for NES & NPV. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-21 

8.4.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-21 

8.4.2 Base Cas~ Shipments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-22 

iv 
8.4.3 National Energy Savings and Net Present Value from Possible 

Standards .... , . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-22 

8.5 ., User Instructions for Spreadsheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-24 

8.6 Indirect Employment Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-25 

8.6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-25 

8.6.2 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-26 

8.6.3 Methodology ...... : ........................ : ........... 8-26 

CHAPTER9: MANUFACTURER IMPACT ANALYSIS ......................... 9-1 

9."l Introduction ........... .- . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1 

. 9 .2 Phase 1: Industry Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1 

9.2.1 Tax Rate ..................... · ................ ; . . . . . . . . . 9-2 

9.2.2 Working Capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-3 

9.2.3 Selling, General and Administration (SG&A) ................... 9-3 

9.2.4 Research and Development (R&D) ........................... 9-4 

9.2.5 Depreciation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-5 

9.2.6 Capital Expenditures............ . . . . . . . . . . . . . . . . . . . . . . . . . . 9-6 

9.2.7 Cost of Capital/Discount Rate........ . . . . . . . . . . . . . . . . . . . . . . . 9-6 

9.3 Phase 2: Industry Cash Flow............. . . . . . . . . . . . . . . . . . . . . . . . . 9-11 

9.4 Phase 3: Subgroup Analysis and Manufacturer biterviews ............... 9-13 

CHAPTER 10: CONSUMER ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-1 -

10.1 Consumer Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-1 

10.1.1 Focus Groups and Conjoint Analysis ...... ; . . . . . . . . . . . . . . . . . . 10-2 

10.2 Consumer Sub-Group Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . · 10-2 

10.2.1 Life-Cycle Cost Analysis (LCC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-2 

CHAPTER 11: UTILITY IMP ACT ANALYSIS .......... : . . . . . . . . . . . . . . . . . . . . . 11-1 

11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11-1 

11.2 . Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11-2 

11.3 Results .................................................... 11-2 . . 

CHAPTER 12: ENVIRONMENTAL ASSESSMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-1 

12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-1 

12.2 Methodology . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-1 

· 12.3 Results . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-2 

CHAPTER 13: REGULATORY IMPACT ANALYSIS ........................... 13-1 

13.l Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-1 

13.2 Methodology .......... '. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-1 

APPENDIX A: APPROACH FOR UNCERTAINTY AND VARIABILITY ............. A-1 

A. l Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 

v 
A.2 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 

A.3 Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .•. . . . . . . . . . . . . . . . . . . A-1 

A.4 Approaches to Uncertainty and Variability ........................... A-2 

APPENDIX B: CLOTHES WASHER MANUFACTURERS ........................ B-1 

APPENDIX C: PRELIMINARY LIFE-CYCLE-COST AND PAYBACK PERIOD 

RESULTS ................................ · ....... , . . . . . . . . . . C-1 

C. l Life-Cycle Cost Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1 

C.2 Payback Period Results ....................... , .... ." ............ : C-11 

APPENDIX D: NATIONAL ENERGY SAVINGS (NES) SPREADSHEET OUTPUT .... D-1 

APPENDIX E: GOVERNMENT REGULATORY IMPACT MODEL (GRIM) .......... E-1 

E.1 Introduction and Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-1 

E.2 Model Description . . . . . . . . . . . . . . .............................. ·. E-1 

E.3 Income Statement ............. ~ .................... : . . . . . . . . . . . E-6 

E.4 Major Assumptions . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E-7 

APPENDIX F: CONSUMER RESEARCH REPORT .............................. F-1 

APPENDIX G: MANUFACTURER INTERVIEW GUIDE ......................... G-1 

G.1 Introduction .................................................. G-1 

G.2 Current Organizational Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G-1 

G.3 Industry Infrastructure .......................................... G-1 

G.4 Manufacturer Cash Flow Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G-2 

G.5 Competitive Impacts Assessment· ..................... -. ............ G-3. 

G.6 Employment Impacts Assessment .................................. G-4 

G. 7 Manufacturing Capacity lmpa,cts Assessment ......................... G-4 

APPENDIX H: LIST OF ACRONYMS AND ABBREVIATIONS .................... H-1 

vi 
Table 2.1 

Table 3.1 

Table 3.2 

Table 3.3 

Table 3.4 

Table 3.5 

Table 3.6 

Table 3.7 

Table 3.8 

Table 3.9 

Table 3.10 

Table 3.11 

Table 3.12 

·Table 3.13 

Table 3.14 

Table 4.1 

Table 4.2 

Table 4.3 

Table 4.4 

Table 5.1 

Table 5.2 

Table 5.3 

Table 5.4 

Table 5.5 

Table 5.6 

Table 6.1 

Table 6.2 

Table 6.3 

Table 6.4 

Table 6.5 

Table 6.6 

LIST OF TABLES 

Comparison of 1994 and 1998 Non-Regulatory Alternatives to Standards .. 2-16 

Market Share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1 

Industry Unit Shipments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-2 

Washer Shipments by Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-3 

Washer Sales by Type of Outlet . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . 3-3 

Clothes Washer Unit Sales by Price Range . . . . . . . . . . . . . • . . . . . . . . . . . . . 3-4 

Wa5her Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-5 

Washer Shipment by Capacity...................................... 3-5 

Brand Names, Price Level and Market Share of Major Manufacturers . . . . . . 3-6 

Washer Market Share by Brand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-7 

Market Saturation by Fuel Type among Housing Units with 

Clothes Washers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-9 

Information from the 1992 Census of Manufactures (MC91-I-36B) ........ 3-9 

Summary of SEC 10-K reports of Appliance Manufacturers . . . . . . . . . . . . . 3-10· 

Product Characteristics of Clothes Washers Tested .................... 3-12 

Summary of Appendix J and J1 Test Results . . . . . . . . . . . . . . . . . . . . . . . . . 3-12 

Energy-use Data....................... . . . . . . . . . . . . . . . . . . . . . . . . 4-5 

Aggregated Cost Data using "Most Likely" Values . . . . . . . . . . . . . . . . . . . . . 4-7 

Industry Cost Distributions for 5%, 10%, 15%, 20% and 25% 

Improvements applied to an existing V-axis Clothes Washer .............. 4-9 

Industry Cost Distributions for 35%, 40%, 45% and 50% 

Improvements applied to a H-axis Clothes Washer . . . . . . . . . . . . . . . . . . . . 4-10 

Major Manufacturing Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 

Key Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8 

Cost Components for Baseline V-axis Clothes Washer ($/Unit) ........... 5-8 

Overhead Cost of Baseline Clothes Washer . . . . . . . . . . . . . . . . . . . . . . . . . . 5-9 

Fixed Costs (millions) for Baseline V-axis Clothes Washer ............... · 5-9-

Monte Carlo Assumptions for Key Parameters . . . . . . . . . . . . . . . . . . . . . . . 5-14 

Retail Price of Clothes Washers fyom 1986 through 1994 obtained from 

the Consumer Expenditure Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-4 

Retail Price of Clothes Washer from 1986 through 1995 estimated from 

Dealerscope Merchandisings Annual Stati~tical Reviews ...... : . . . . . . . . . 6-4 

Shipment Weighted Retail Prices of Clothes Washers from 1994 

through 1997 based on ELCAP Retail Sales Data ...................... 6-5 

Manufacturer Prices of Clothes Washers from 1986 through 1996 

estimated from Current Industrial Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . 6:-5 

Manufacturer Prices of Clothes Washers from 19 86. through 1996 

estimated using data from the AHAM Fact Book . . . . . . . . . . . . . . . . . . . . . . 6-6 

Manufacturer Mark-ups for Clothes Washers from 1986 through 1994 

using Retail Price from the Consumer Expenditure Surveys and 

Manufacturer Prices from the Current Industrial Reports . . . . . . . . . . . . . . . . 6-7 

vii 
Table 6.7 

Table 6.8 

Table 6.9 

Table 7.1 

Table 7.2 

Table 7.3 

Table 7.4 

.Table 7.5 

Table 7.6 

Table 7.7 

Table 7,.8 

Table 8.1 · 

Table 8.2 

Table 8.3 

Table 8.4 

Table 8.5 

Table 8.6 

Table 8.7 

Table 8.8 

Table 8.9 

Table 8.10 

Table 9.1 

Table 9.2 

Table 9.3 

Table 9.4 

Table 9.5 

Table 9.6 

Table 9.7 

Table 9.8 

Table 9.9 

Table 9.10 

Table 11.1 

Table 12.l 

Table 13.l 

TabkD.1.1 

Table D.1.2 

Retail Mark-ups estimated from Different Data Sources ............... .'. 6-7 

Gross Margins for Retailers of Electronics, Home Office Equipment 

. and Major Appliances ................. .' . . . . . . . . . . . . . . . . . .. . . . . . . . . 6-9 

Manufacturing Cost of a Baseline Clothes Washer estimated 

from the Industry Cost Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-11 

Quantities in LCC Spreadsheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-3 

Summary of Nominal Interes~ Rates ............................... 7-20 

Distribution of Discount Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-22 

Shares of Water Heaters and Dryers by Fuel Type .................... 7-23 

Summary ofLCC Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-27 

Summary of Payback Period Results ................................. 7-30 

Rebuttable Presumption Payback in Years . . . . . . . . . . . . . . . . . . . . . . . . . . 7-32 

Quantities and Locations in Life-Cycle Cost Spreadsheet ................ 7-34 

National Energy/Water Saving Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-3 

Variables accounted for by different forecast approaches . . . . . . . . . . . . . . . 8-15 

Percent of Annual Clothes Washer Shipments having Energy Consumption 

Equivalent to Horizontal Axis Washers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-16 

Net Present Value Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-18 

Assumptions Used for Generating Preliminary National Impacts . . . . . . . . . 8-21 

Preliminary National Energy Savings Results (2003 to 2030 cumulative) ... 8-22 · 

NES Results: 20% Improvement (MEF = 1.021) ..................... 8-23 

Efficiency Level: Energy and Water Savings (20% Improvement)· ........ 8-23 

National Energy Saving Quantities and their Locations in the National Energy 

Savings Spreadsheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-25 

Net Present Value Quantities and their Locations in the National Energy 

Savings Spreadsheet ......................... ·.... . . . . . . . . . . . . . . 8-25 

Average Income Tax Rates ....................................... 9-2 

Average Working Capital as a Percentage of Revenues .................. 9-3 

Average SG&A Expense as a Percentage of Revenues . . . . . . . . . . . . . . . . . . 9-4 

Average R&D Costs as a Percentage of Revenues ..................... 9-5 

Average Depreciation as a Percentage of Revenues .............. · ...... 9-5 

Average Capital Expenditures as a Percentage of Revenues . . . . . . . . . . . . . . 9-6 

Cost of Debt and Debt Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-7 

Average Rates of Return (1990-96) and Risk PremiU1lls (1926-94) 

on Some Securities (figures in percent per year) ....................... 9-9 

Average Beta Values for Two Appliance Manufacturers . . . . . . . . . . . . . . . . 9-10 

Comparison of Financial Data ........................ ·. . . . . . . . . . . . 9-11 

Example Output Table ofNEMS-NAECA Results for Utility Analysis ..... 11-3 

Example Output Table ofNEMS-NAECA Results for Environmental 

Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12-2 

Non-regulatory Alternatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13-1 

5% Efficiency Level: Cost and Net Present Values ..................... D-2 

5% Efficiency Level: Energy and Water Savings ....................... D-2 

viii 
Table D.1.3 

Table D.1.4 

Table D.1.5 

Table D.1.6 

Table D.1.7 

Table D.1.8 

Table D.1.9 

Table D.1.10 

Table D.1.11 

Table D.1.12 

Table D.1.13 

Table D.1.14 

Table D.1.15 

Table D.1.16 

TableD.1.17 

Table D.1.18 

10% Efficiency Level: Cost and Net Present Values .................... D-3 

10% Efficiency Level: Energy and Water Savings ...................... D-3 

15% Efficiency Level: Cost and Net Present Values .................... D-4 

15% Efficiency Level: Energy and Water Savings ...................... D-4 

20% Efficiency Level: Cost and Net Present Values ...... · .............. D-5 

20% Effic~ency Level: Energy and Water Savings ...................... D-5 

25% Efficiency Level: Cost and Net Present Values ............... : .... D-6 

25% Efficiency Level: Energy and Water Savings ....................... D-6 

35% Efficiency Level: Cost and Net Present Values .................. .- . D-7 

35% Efficiency Level: Energy and Water Savings ...................... D-7 

40% Efficiency Level: Cost and Net Present Values .................... D-8 

40% Efficiency Level: Energy and Water Savings ...................... D-8 

45% Efficiency Level: Cost and Net Present Values .................... D-9 

45%· Efficiency Level: Energy and Water Savings ...................... D-9 

50% Efficiency Level: Cost and Net Present Values ................... D-10 

50% Efficiency Level: Energy an.d Water Savings ..................... D-10 

ix 
Figure 2.1 

Figure 2.2 · 

Figure 3.1 · 

Figure 3.2 

Figure 4.1 

Figure 4.2 

Figure 4.3 

Figure 5.1 

Figure 5.2 

Figure 5.3 

Figure 5.4 

Figure 5.5 

Figure 5.6 

Figure 5.7 

Figure 5.8 

Figure 5.9 

Figure 5.10 

Figure 5.11 

Figure 6.1 

Figure 6.2 

Figure 7.1 

Figure 1.2 

Figure 7.3 

Figure 7.4 

Figure 7.5 

Figure 7.6 

Figure 7.7 

Figure 7.8 

Figure 7.9 

Figure 7.10 

·Figure 7.11 

Figure 7.12 

Figure 7.13 

Figure 7.14 

Figure 8.1 

Figure 8.2 

Figure 8.3 

Figure 8.4 

Figure 8.5 

LIST OF FIGURES· 

Clothes Washer Rulemaking Framework ............................ 2-2 

Flow diagram of the manufacturer impact analysis methodology .......... 2-11 

Water heater and dryer fuel use ........ ~... . . . . . . . . . . . . . . . . . . . . . . . 3-8 

Washing machine industry cost structure as a percentage of revenues ...... 3-10 

Illustrative input distributions. for incremental manufacturing costs ......... 4-3 

Industry cost distribution obtained by sampling each manufacturers 

cost distributions ..................................... ·. . . . . . . . . 4-7 

Cost-Efficiency graph for clothes washers. . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11 

Full production cost ........................................ ~ . . . 5-3 

Manufacturing cost assessment approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4 

Structured bill of materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-5 

Fabrication process cycle time estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6 

Fabrication process equipment and labor requirements .................. 5-6 

Sensitivity analysis on labor rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-11 

Sensitivity analysis on actual plant output. . . . . . . . . . . . . . . . . . . . . . . . . . . 5-12 

Sensitivity analysis on total investment cost . . . . . . . . . . .. . . . . . . . . . . . . . . 5-12 

Sensitivity analysis on equipment depreciation life. . . . . . . . . . . . . . . . . . . . . 5-13 

Sensitivity analysis on maintenance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14 

Monte Carlo simulation of aggregated baseline cost . . . . . . . . . . . . . . . . . . . 5-15 

Average costs and mark-ups for the baseline clothes washer .............. 6-1 

Retail mark-ups estimated from different data sources .................. 6-8 

Percent of clothes washers surviving since year of original purchase ........ 7-5 

Percent of washer owners by the number of washer cycles per year ........ 7-6 

Energy use per cycle vs. modified energy factor (met) . . . . . . . . . . . . . . . . . . 7-7 

Distribution of 1997 residential electricity prices . . . . . . . . . . . . . . . . . . . . . . 7-8 

Alternative electricity price trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7:-12 

Distribution of 1997 residential gas prices . . . . . . . . . . . . . . . . . . . . . . . . . . 7-13 

Alternative gas price trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-16 

·Alternative oil price trends ............. ·. . . . . . . . . . . . . . . . . . . . . . . . . 7-1 7 

Water use per cycle vs. MEF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . 7-18 

Distribution of net water and sewer charges in 1994 (expressed in 1997$) . . 7-19 

Distribution of Consumer Discount Rates . . . . . . . . . . . . . . . . . . . . . . . . . . 7-22 

Frequency chart ofLCC differences for 25% efficiency improvement level .. 7-26 

Cumulative chart ofLCC differences for 25% efficiency improvement level. 7-27 

Distribution of PBPs for 25% efficiency improvement level ............. 7-29. 

ARIMA model sales forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-10 

Muti-variate time series fit sales forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-11 

Saturation/Lifetime model sales forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-12. 

Accounting model sales forecast . . . . . . . . . . . . . . . . . . . . . . . . .... ·. . . . . 8-14 

Visual comparison of forecasting models .... ; . . . . . . . . . . . . . . . . . . . . . . 8-15 

x 
Figure 8.6 

Figure 9.1 

Figure 9.2 

Figure C.l 

Figure C.2 

Figure C.3 

Figure C.4 

Figure C.5 

Figure C.6 

Figure C.7 

Figure C.8 

Figure C.9 

Figure C.10 

Figure C.11 

Figure C.12 

Figure C.13 

Figure C.14 

Figure C.15 

Figure C.16 

Figμre C.17 

Figure C.18 

Figure C.19 

Figure C.20 

Figure C.21 

Figure C.22 

Figure C.23 

Figure C.24 

Figure C.25 

Figure C.26 

Figure C.27 

Figure D.1 

Figure D.2 

Figure D.3 

Figure D.4 

Figure D.5 

Figure D.6 

Figure D.7 

Figure D.8 

Figure D.9 

Net Present Annual Values ofa Clothes Washer Standardv with a 20% 

Efficiency Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-23 

Using GRIM to perform an industry cash flo.w analysis ................. 9-12 

Summary of financial inputs to GRIM .......... : . . . . . . . . . . . . . . . . . . . 9-12 

5% Efficiency Level: Frequency Chart ofLCC Differences ............... C-2 

5% Efficiency Level: Cumulative Chart ofLCC Differences .............. C-2 

10% Efficiency Level: Frequency Chart ofLCC Differences .............. C-3 

· 10% Efficiency Level: Cumulative Chart ofLCC Differences ............. C-3 

15% Efficiency Level: Frequency Chart ofLCC Differences .............. C-4 

15% Efficiency Level: Cumulative Chart ofLCC Differences ............. C-4 

20% Efficiency Level: Frequency Chart ofLCC Differences ........ ~ ..... C-5 

20% Efficiency Level: Cumulative Chart ofLCC Differences ............. C-~ 

25% Efficiency Level: Frequency Chart ofLCC Differences .............. C-6 

25% Efficiency Level: Cumulative Chart of LCC Differences ............. C-6 

35% Efficiency Level: Frequency Chart of LCC Differences .............. C-7 

35% Efficiency Level: Cumulative Chart of LCC Differences ............. C-7 

40% Efficiency Level: Frequency Chart of LCC Differences .............. C-8 

40% Efficiency Level: Cumulative Chart ofLCC Differences .............. C-8 

45% Efficiency Level: Frequency Chart of LCC Differences . . . . . . . . . . . . . . C-9 

45% Efficiency Level: Cumulative Chart ofLCC Differences ............. C-9 

50% Efficiency Level: Frequency Chart of LCC Differences. . . . . . . . . . . . . C-10 

50% Efficiency Level: Cumulative Chart ofLCC Differences ............ C-10 

5% Efficiency Level: Distribution of Payback Periods .................. C-11 

10% Efficiency Level: Distribution of Payback Periods. . . . . . . . . . . . . . . . . C-11 

15% Efficiency Level: Distribution of Payback Periods. . . . . . . . . . . . . . . . . C-12 

20% Efficiency Level: Distribution of Payback Periods ................. C-12 

25% Efficiency Level: Distribution of Payback Periods. . . . . . . . . . . . . . . . . C-13 

30% Efficiency Level: Distribution of Payback Periods ................. C-13 

40% Efficiency Level: Distribution of Payback Periods ................. C-14 

45% Efficiency Level: Distribution of Payba~k Periods ................. C-14 

50% Efficiency Level: Distribution of Payback Periods ................. C-15 

5% Efficiency Level: Net Saving ................................... D-2 

10% Efficiency Level: Net Saving .................................. D-3 

15% Efficiency Level: Net Saving .................................. D-4 

20% Efficiency Level: Net Saving .................................. D-5 

25% Efficiency Level: Net Saving. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-6 

35% Efficiency Level: Net Saving. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-7 

40% Efficiency Level: Net Saving ......... · .................... .' .. : . D-8 

45% Efficiency Level: Net Saving .................................. D-9 

50% Efficiency Level: Net Saving .... : . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1 O 

xi 
CHAPTER 1: INTRODUCTION 

ll.1 OVERVIEW 

Part B of Title III of the Energy Policy and Conservaticm Act, Public Law 94-163, as 

amended by the National· Energy Conservation Policy Act, Public Law 95-619, the National 

Appliance Energy Conservation Act, Public Law 100-12, the. National Appiiance Energy 

ConservationAmendme~ts of 1988, Public Law 100-357, and the Energy Policy Act of 1992, Public 

Law 102-486, (the Act or EPCA), created the Energy Conservation Program for Various Consumer 

Products other than Automobiles. 42 U.S.C. §6291-6309. 

The National. Appliance Energy Conservation Act of 1987 amended the Act to impose 

prescriptive standards (design· feature requirements) for cIOthes washers as part of the energy 

conservation program for consumer products. EPCA, §325(g); 42 U .S.C. §6295 (g). The design 

feature requirement that clothes washers shall have an unheated rinse option was effective for 

· appliances manufactured on or after January 1, 1988. The Act required the Department to conduct 

a rulemaking by January 1, 1990, to detemrine if the above mentioned standards should be amended. 

The Act provided that any amendment to the standards would apply to products manufactured three 

years after the rulemaking. The Final Rule was issued on May 14, 1991, and is effective .for products 

manufactured on or after May 14, 1994, (hereinafter referred to as the May 1991 Final Rule). 56 FR 

22279. The Act also. requires the Department to conduct a subsequent rulemaking no later than five 

years after the date of publication of the previous final rule. 

Before the Department detemrines whether or not an energy conservation standard is 

economicallyjustified, it must first solicit comments on the proposed standard. EPCA, §325 (p ), 42 

U.S.C. §(;295 (p). Any new or amended standard is required to be designed so as to achieve the 

maximumimprovement in energy efficiency that is technologically feasible and economically justified. 

EPCA, §325(0)(2), 42 U.S.C. §6295 (o)(2). After reviewing comments on the proposal, the 

Department must then determine that the benefits of the standard exceed its burdens based to the 

grt!atest extent practicable, on a weighing ofthe'following seven factors: 

(1) The economic impact of the standard on the manufacturers and on the consumers of 

the products subject to such standard; 

(2) The savings in operating costs throughout the estimated average life of the covered 

product in the type (or class) compared to any increase in the price, initial charges, or maintenance 

expenses for the covered products that are likely to result directly from the imposition of the standard; 

(3) The total projected amount of energy, or as applicable, water, savings likely to result 

directly from the imposition of the standard; 

( 4) Any lessening of the utility or the performance of the covered products likely to result 

from the imposition of the standard; 

1-1 
(5) The impact of any lessening of competition, as determined in writing by the Attorney· 

General, that is likely to result from the imposition of the standard; 

( 6) The need for national energy and water conservation; and 

(7) Other factors the Secretary considers relevant. 

The Department initiated a clothes washer rulemaking to determine if the standards (design 

feature requirements)· imposed by the Act should be amended. The Department published an 

Advance Notice of Proposed Rulemaking (ANOPR) (53 FR 17712, May 18, 1988), a Notice of 

Proposed Rulemaking (NOPR) (54 FR 32744, August 9, 1989), and the May 1991 Final Rule. The 

May 1991 Final Rule mandated performance-based energy conservation standards for clothes 

washers. The standards specified a minimum energy factor (EF) for two of the five classes of clothes 

washers (top-loading1

standard and top-loading compact). The energy conservation standards in the. 

May 1991 Final Rule are effective for products manufactured on or after May 14, 1994. 

On November 14, 1994, the Department issued an ANOPR to begin the second review of 

energy efficiency standards for clothes washers, dishwashers and clothes dryers. The methodologies 

presented in this report outline analytical approaches designed to evaluate the economic impacts 

according to the criteria in the Act. It includes the approach for an engineering analysis of the 

manufacturing cost and clothes washer performance data; for forecasting the number and efficiency 

of products sold, for determining prices and operating expenses; for determining change in 

investment, revenues, and costs to manufacturers of the products; for calculating the costs and 

benefits to consumers, and the nation as a whole; and for assessing the environmental impacts of the 

proposed standards. 

The analytical approaches outlined in this report are provided in order to solicit comments 

from stakeholders on the proposed approaches to be used in this rulemaking. Alternative approaches 

· can be explored or proposed approaches can be modified and refined. 

Also included in this preliminary Technical Support Document (TSD) are preliminary results 

for the Life-Cycle Cost (LCC) and National Energy Savings (NES) Analyses. Data from the 

Engineering Analysis is also provided. 

1-2 
; 

1.2 STRUCTURE OF THE DOCUMENT 

This preliminary TSD consists of thirteen Chapters and eight Appendices. 

Chapter 1 Introduction: outlines the structure of the document. 

Chapter 2 Analytical Framework: describes the new rulemaking process step-by-step. 

Chapter 3 Preliminary Marketing and Technology Assessment: characterizes the relevant 

product markets and existing technology options including prototype designs. 

Chapter 4 Engineering Analysis: contains detailed energy use and cost information. 

Chapter 5 Manufacturing Cost Assessment: determines the manufacturing cost by physically 

taking apart washers. 

Chapter 6 Mark-ups for Price Determination: establishes a range of mark-ups from 

manufacturing cost to retail price. 

Chapter 7 · Life-CycleCost and Payback Period: describes the effects of standards on individual 

purchasers and users of the appliances. It compares the life-cycle cost of appliances 

and other measures of consumer impact with and without the proposed standards. 

Chapter 8 National Energy Impacts: describes national forecast of energy consumption, 

efficiencyof new units, units installed in households and annual appliance sales in the 

absence (or presence) of new regulations. Indirect employment impacts are also 

evaluated. 

Chapter 9 Manufacturer Impact Analysis: describes the financial impact on manufactures. 

Chapter 10 Consumer Analysis: describes the affect on the utility and performance of more 

efficient washers as experienced by consumers. Evaluates the attributes of washers, 

or what consumers value in a washing machine. Also evaluated is the effect of 

standards on subgroups such as different household income levels. · 

Chapter 11 Utility Impact Analysis: determines the decrease in electricity and gas needed. For 

electricity it determines the primary energy source for electricity generation. 

Chapter 12 Environmental Assessment: determines the reduction in emissions due to higher 

efficiency standards. 

Chapter 13 Regulatory Impact Analysis: analysis impact of non-regulatory alternatives to 

standards. 

1-3 
Appendix A Approach for Uncertainty and Variability 

Appendix B List Of Clothes Washer Manufacturers 

Appendix C Life-cycle-cost and payback results 

Appendix D National Energy Savings results ·. 

Appendix E Government Regulatory Impact Model (GRIM) 

Appendix F Consumer Research Report 

Appendix G Manufacturer Interview Guide 

Appendix H List of Acronyms and Abbreviations 

1-4 
CHAPTER 2: ANALYTICAL FRAMEWORK 

In prescribing new or amended energy efficiency appliance standards, the Departmenfmust 

achieve the maximum improvement in energy efficiency that is technologically feasible and 

economicallyjustified. This section provides a description of the analytical framework to be used by 

tht~ Department in developing standards and assessing the impacts. It discusses the major components 

of the analyti~al framework and the interrelationships among the components that ensure consistency 

throughout the analyses. A diagram of the analytical framework is shown in Figure 2.1. 

The analyses performed are: 

• A Preliminary Market and Technology Assessment to characterize the relevant product markets 

and existing technology options including prototype designs. (Subtask 1 in Fig. 2.1) · 

• A Screening Analysis to review each technology option and determine if it is practicable to 

manufacture, install and service, would adversely impact product ·utility or product availability, 

or would have adverse impacts on health and safety. (Subtask 2 in Fig. 2:1) 

• An Engineering Analysis to determine the maximum technologically feasible energy efficiency 

level and develop cost-efficiency relationships to show the manufacturer cost of achieving 

increased efficiency. (Subtask 3 in Fig. 2.1) 

• A LCC and Payback Analyses to calculate the savings in operating costs throughout the estimated 

average life of the covered product compared to any increase in the price, initial charges, or 

maintenanceexpenses for the product likely to result directly from the imposition of the standard. 

(Subtask 5 in Fig. 2.1) 

• A Preliminary National Impacts Ailalysis to assess the aggregate impacts at the national level of 

consumer payback, net present value (NPV) of total consumer LCC, national energy and water 

savings and national employment. (Subtask 8 in Fig. 2.1) 

• A Consumer Analysis to evaluate variations in household characteristics (e.g., energy prices, 

appliance use behavior, installation costs) that might cause a standard to impact particular 

consumer sub-populations such as low-income households differently than the overall population. 

(Subtask 10 in Fig. 2.1) 

• A Manufacturer Impact Analysis (MIA) to estimate the financial impact of standards on 

manufacturers and to calculate impacts on competition, employment, and manufacturing capacity. 

(Subtask 11 in Fig. 2.1) 

• A Utility Analysis to estimate the effects of proposed standards on electric and gas utilities. 

(Subtask-_12 in Fig. 2.1) 

• An Environmental Analysis to provide estimates of changes in emissions of pollutants (sulfur and 

nitrogen oxides and carbon dioxide). (Subtask 13 in Fig. 2.1) 

• A Regulatory Impact Analysis to present major alternatives to proposed standards that could 

achieve substantially the same regulatory goal at a lower cost. (Subtask 14 in Fig. 2.1) 

2-1 
Aooroac·hes 

lnduatry Review 

Ouantit.lltiYe Anatyai1 

Qualitative Anatyait- Interviews 

Cost Modeling Approach 

Etf1caency·Level Approach 

Market Survey Approach 

Expert Forecaata 

Key-In outs 

Identify Finns/ Producta 

Hialorical tnformation 

Mar1<et/ Firm Segmentation 

Exishng Technology'Options 

Prototype De1ign1 

I 

I 

1. 

I 

I 

I 

: EfficiencylPerfontlance 

i I --·---·-----··-··-··r··---· 

Energy/Water mes I 

I 

I 

-O•-••••-""u"" I : I 

Analysis 

Preliminary Market 

& Technology 

Characterization 

Identification & 

Screening Analysis 

Engineering 

Analysis 

ti i ... ~ &! ; ~ 

Life-cycle Cost & 

Payback Analyses 

11 I !.5 i 

Key-Outputs 

• Prehm1nary Bllse Case 

• Shipment Forecast 

•Average Paybaek with Uncertainty 

• Rebuttable Paybacl< 

·I ·Average Life.cycle with Uncertainty 

• Energy savings 

j ·---· : Ex:::pe:::,:rt,::F;::ore:.::;:ca::•:::'":._ ____ ..r..i~i-_.--(;~:::":::::l i~_l!!!_~.~!J ..... -.... • Water uvings 

• Net Present Values 

Models-REM I 

• Hou1ing demand 

• Htat.oricml ahipmontl 

• Product uturation 

• Voluntary initia~vea 

ANO PR 

lntetvfeWI 

GRIMAnatvtis 

I 

NOPR 

FINAL RULE 

.._...., _ _.. •Bue Caae l 

: • Other effteienq'ea ! 

t

. ,·,1 1:! . !:':~"':.. : l 

i !.~ i i j Revised Pre-ANOPR l l ,i.! !~~··~~~~~ype~ap~h-ica----1.--.!!~..;----~~~J:.n~:~~~:~~:::::r----,.·_··_··_··_··~·,!>----,..--":~~~i~~-""'d<~D~~~~~~~~i:~~~~;~n.._~ 1 l I 

: : · ! Manufacturer Prtees 1 : _.r--------------,-..... .. L~:=.:~::~::=.:~.:=:~::=:.:~::= 

Aver11ge <-----··-··-··-··•·-·-cl-- Coa,. •S-C... I ; 

• Standard Level Cue• I : • Efficiency Distribution I 1 for •11 C&les I ~ 

Coot (C), Price (P). 

Quantily (Q), Financial (F) 

I ; 

~ I 

I 

I 

I 

Manufacturer Impact 

· Analysis 

(Industry profile, 

Industry cash flow. 

Sub-group analysis) 

Industry Cash-flow wilt\ Uncertainty 

I DOJ Competitive l 

1---------•ol 11mpact Assessment 

Sub-group Cash-flows 

I ..... ..-----------'!.-_,..;.--------------, __ N~EM::s~ _________ _..._ __ ~ Environmental i-----~E:::m::i::;u::i::"'.::..---

I 

I· 

Utility Impacts Analysis 

Regulatory Impact Analysis 

: 

.. Revise Analysis 

Fuel Use 

Figure 2.1 Clothes Washer Rulemaking Framework 

2-2 

'-" 
2.1 BACKGROUND 

In September 1995, the Department announced a formal effort to consider further 

improvements to the process used to develop appliance efficiency standards, calling on energy 

efficiency groups, manufacturers, trade association, state agencies, utilities and other inter~sted 

parties to provide input to guide the Department. On July 15, 1996, the Department published a Final 

Rule: Procedures for Consideratio.n ofNew or Revised Energy Conservation Standards for Consumer 

Products (hereinafter referred to as the Process Rule). 61 FR 36974. The Process Rule outlines the 

procedural improvements identified bythe interested parties. The process improvement effort also 

• included a review of the: 1) economic models; 2) analytical tools; 3) methodologies; 4) 

non-regulatory approaches; and 5) prioritization of future rules. The Department will also take into 

account uncertainty and variability by doing scenario or probability analysis. For a more detailed 

discussion see Appendix A. 

The clothes washer standards rulemaking is the first rule. to be developed under the Process 

Rule. The Department developed the analytical framework for the clothes washer rulemaking in 

response to stakeholder concerns regarding the application of the Process Rule to the clothes washer 

rulemaking. This analytical framework was presented during a clothes washer workshop held on July 

23, 1997. It describes the methodology, the analytical tools and the relationship between the various 

analyses. The following sections provide a general description of the different analytical components 

. of the rulemaking framework. 

2.2 PRELIMINARY MARKET AND TECHNOLOGY ASSESSMENT 

The preliminary market and technology assessment characterizes the relevant product markets 

and existing technology options including prototype designs. 

2.2.1 Market Assessment 

When initiating a standards rulemaking, the Department develops information on the present 

and past industry ,structure and market characteristics of the product(s) concerned. This activity 

consists of .both quantitative and qualitative efforts to assess the industry and products based on 

public.b' available information. Issues to be addressed include: 1) manufacturer market share and 

characteristics; 2) trends in the number of firms; 3) the financial situation of manufacturers; 4) 

existingnon-regulatory efficiency improvement initiatives; and 5) trends in product characteristics 

and retail markets. The information collected serves as resource material to be used throughout the 

rulemaking. · 

2.2.2 Technology Assessment 

Information relative to existing technology options and prototype designs are used as inputs 

to the screening analysis. In consultation with interested parties, the Department develops a list of 

2-3 
design options for consideration. All technologically feasible design options are candidates in this 

initial assessment. 

2.2.3 Preliminary Base Case Shipments Forecast 

The Department develops a base case forecast of product shipments in the absence of new 

standards. This forecast requires an assessment of the impacts of past and existing non-regulatory 

efforts by manufacturers, utilities and other interested parties. DOE considers information on the 

actual impacts of such initiatives to date, and also considers information presented regarding the 

possible impacts that any existing initiatives might have in the future. Such information could include 

a demonstration of the steps manufacturers, distribution channels, utilities or others will take to 

realize.such voluntary efficiency improvements. 

The base case shipments forecast is used as input to the national impacts analysis, in which 

a forecast of annual shipments and their weighted average energy efficiency is needed to the year 

2030. 

2.3 SCREENING ANALYSIS 

The screening analysis reviews various technologies with regard to whether they: a) are 

impracticable to manufacture, install and service; b) have an adverse impact on product utility or 

product availability; and c) have adverse impacts on health and safety. The screening analysis 

establishes product classes, baseline units, and efficiency levels (or combinations of design options) 

for further analysis. 

2.3.1 Product Classes 

Product types are divided into classes using the following criteria: a) the type of energy used; 

b) capacity; and c) performance-related features that affect consumer utility or efficiency. Different 

energy efficiency standards will apply to different product classes. In general, classes are defined 

using information obtained in discussions with appliance manufacturers, trade associations, and other 

interested parties. 

2.3.2 Baseline Units 

In order to analyze design options for energy efficiency improvements, the Department defines 

a baseline unit. For each product class, the assumed baseline unit is a unit that minimally exceeds the 

existingstandard. To determine the characteristics of the baseline unit in this screening analysis, the 

Department will gather Information from trade organizations, manufacturers, and consultants with 

expertise in specific product types. 

2-4 
2.3.3 Design Options/Efficiency Levels 

Following the development of an initial list of design options. during the technology 

a.ssessment and the screening analysis, the Department, in consultation with interested ·pa.rties, will 

· select appropriate efficiency levels (or combinations of design options) for data collection. 

2 .. 3.4 Proprietary Designs 

·•· In its analysis, the Department considers all design options that are commercially available or 

present in a working prototype, including proprietary designs. Proprietary designs are fully 

considered in the Department's engineering and economic analyses. 

2.4 . ENGINEERING ANALYSIS 

The engineering analysis first determines the maximum technologically feasible energy 

efficiency level and then develops cost-efficiency relationships to show the manufacturer costs of 

achieving increased efficiency. 

2.4.1 Energy Savings Potential and Manufacturing Costs 

The engineering analysis estimates the energy savings potential of the individual or 

combinations of design options not eliminated in the previous screening analysis. The Department, 

in consultation with stakeholders, uses the most appropriate means available to determine energy 

consumption, including an overall system approach or engineering modeling. Ranges and 

uncertainties in performance are established. The energy savings measures developed in the 

engineering analysis are combined with end-user costs in the LCC analysis. 

The engineering analysis involves adding individual or combinations of design options to the 

baseline unit. A cost-efficiency relationship is developed to show the manufacturer cost of achieving 

increased efficiency. The efficiency levels corresponding to various design option combinations are. 

determined from manufacturer data submittals and from DOE engin~ering calculations. 

The Act requires that, in considering any new or amended standards, the Department must 

consider those that "shall be designed to achieve the maximum improvement in energy efficiency that 

the Secretary determines is technologically feasible and economically justified." EPCA, 

§325(1)(2)(A), 42 U .S.C. §6295(1)(2)(A). Therefore an essential role of the engineering analysis 

consists of identifying the maximum technologically feasible level. The maximum technologically 

foasible level is one that can be reached by the addition of efficiency improvements and/or design 

options, both commercially feasible and in prototypes, to the baseline units. The Department believes 

that the design options comprising the maxir~1um technologically feasible level must have been 

physically demonstrated in at least a prototype form to be considered technologically feasible. 

2-5 
Three methodologies can be used to generate the manufacturing costs n~eded for the 

engineering analysis. These methods include: 1) the desigri-option approach, reporting the 

incremental costs of adding design options to a baseline model; 2) the efficiency-level approach, 

reporting relative costs of achieving energy efficiency improvements; 3) and the cost-assessment 

approach which requires a "bottoms-up" manufacturing cost assessment based on a detailed bill of 

materials. The Department considers public comments in determining the best approach for a 

rulemaking. 

If the efficiency-level approach is used, the Department will select appropriate efficiency levels 

for data collection on the basis of: 1) energy savings potential identified from engineering models; 

2) observation of existing products on the market; and/or 3) information obtained for the technology 

assessment. Stakeholders will be consulted on the efficiency level selection. 

Th~ use of a design-option approach provides useful information such as the identification of 

potential technological paths manufacturers could use to achieve increased product energy efficiency. 

It also allows the use of engineering models to simulate the energy consumption of different design 

configurations under various user profiles and applications. However, the Department recognizes 

that the manufacturer cost information derived in the design-option approach does not reflect the 

variabilityin design strategies and cost structures that can exist between manufacturers. Therefore, 

the Department may derive additional manufacturing cost estimates from other approaches developed 

in consultation with interested parties. 

2.5 LIFE-CYCLE COST (LCC) AND PAYBACK ANALYSES 

In determining economic justification, the Act directs the Department to consider a number 

of different factors, including theeconomic impact of potential standards on consumers. The Act 

also establishes a rebuttable presumption that a standard is economically justified if the additional 

product costs attributed to the standard are less than three times the value of the first year energy cost 

savings. EPCA, §325( o )(2)(B)(iii), 42 U .S.C. §6295 ( o ){2)(B)(iii). 

To consider these requirements the Department calculates changes in LCC to the consumers 

that. are likely to result from the proposed standai-d and two different simple payback periods: 

distributions of payback periods, and a payback period (which follows the test procedure without 

variation) calculated for purposes of the rebuttable presumptionclause. The effect of standards on 

individual consumers includes a change in operating expense (usually decreased) and a change in 

purchase price (usually increased). The net effect is analyzed by calculating the change in LCC as 

compared to the base case .. Inputs to the LCC calculation include the installed consumer cost 

(purchase price plus installation cost), operating expenses (energy, water, sewer, and maintenance 

costs), lifetime of the appliance, and a discount rate. 

2.5.1 LCC Spreadsheet Model 

The LCC analysis is conducted using a spreadsheet model developed in Microsoft Excel for 

Windows95, combined with Crystal BallTM (a commercially available software program). The Model 

2-6 
---- ---------

uses a Monte Carlo simulation to perform the analysis considering uncertainty and variability. The 

spreadsheet is organized so that ranges (distributions) can be entered for each input variable needed 

to perform the calculations. 

Based on the results of the LCC analysis, DOE selects candidate standard levels for a more 

detailed analysis. The range of candidate standard levels typically includes: (I) the most energyefficientcombination of design options or most energy-efficient level; (2) the combination of design 

options or efficiency level with the lowest LCC; and (3) the combination of design options or 

efficiencylevels with a payback. period of not more than three years. Additionally, candidate standard 

levels that incorporate noteworthy technologies or fill in large gaps between efficiency levels of other 

c:andidate standards levels may be selected. 

Payback is calculated based on the same inputs used forthe LCC analysis (with the difference 

that the values are based only on the first year the standard takes affect). The output is a distribution 

of payback periods. The mean payback period is also reported. Additional information is available 

in the LCC spreadsheet but is not reported in the this preliminary TSD. This data includes charts of 

cash flow taking into account the changing annual fuel prices. 

The payback for purposes of the rebuttable presumption clause is calculated on the LCC 

spreadsheet but without using any distributions or Crystal BalfM. Payback periods are first calculated 

. between the new standard level and each washer efficiency being sold in the year 2003. The 

paybacks are then weighted and averaged according to the percentage of each washer efficiency sold 

before a new standard is enacted. Rather than distributions, single point values for the inputs are 

used. These values (including cycles per year, electric fuel source, etc.) will correspond to those 

outlined in the DOE test procedure, Appendix JI. The result is a single payback value and not a 

distribution. The payback is calcuiated for theexpected effective year of the standard (e.g., 2003). 

2.5.2 Price 

Manufacturer and retail prices are needed to determine how manufacturing costs and 

associated margins, associated with standards, are passed-through from manufacturers to consumers, 

as this has an impact on both consumers and manufacturers. Consumer and manufacturer economics 

are linked and inversely related. For this reason, retail prices used for the LCC analysis need to be 

reconciled with manufacturer prices. Chapter 6 provides a detailed description of the approach used 

for determining industry-average prices and mark-ups. 

2.5.2.1 Retail Price 

Retail prices are needed for a base case, absent new standards, and for all efficiency levels to 

be considered. Several approaches can be used to obtain these retail prices. Potential approaches 

include: 1) conducting a survey of existing prices on the market; 2) surveying manufacturers and 

other market experts to predict consumer willingnessto pay higher prices; and 3) applying various 

2-7 
mark-ups over manufacturing costs. Consistent with the Process Rule, and building on the estimates 

generated by various approaches, retail prices would be described within a range of uncertainty. 

2.6 PRELIMINARY NATIONAL IMPACTS ANALYSIS 

The national impacts analysis assesses the net present value (NPV) of total consumer LCC, 

energy (and water, if appropriate) savings and indirect employment impacts. A. preliminary 

assessment of the aggregate impacts at the national level isconducted. for the ANOPR. Analyzing 

impacts of Federal energy-efficiency standards requires a comparison of projected U.S. residential 

energy consumption with and without standards. The base case, which is the projected US residential 

energy consumption without standards, includes the mix of efficiencies being sold at the time the 

standard becomes effective. Sales projections together with efficiency levels of the washers sold, are 

important inputs to determine the total energy consumption due to clothes washers under both base 

case and standards case scenarios. The differences between the base case and standards case 

provides the energy and cost savings. Depending on the analysis method used, the sales under a 

standards case projection may differ from those of a base case projection. 

The Department estimates national energy and water, if applicable, consumption for each year 

beginning with the expected effective date of the standards. National annual energy and water 

savings are calculated asthe difference between two projections: a base case and a standards case. 

Analysis includes estimated energy savings by fuel type for electricity, natural ga~d oil. Energy 

consumption and savings are estimated based on site energy (kWh of electricity, million Btu of natural 

gas or oil used in the home), then the electricity consumption and savings are converted to source. 

energy. 

Measures of impact reported include the riet present value (NPV) of total consumer LCC, 

national energy savings (and water savings, if appropriate) and indirect employment impacts. Each 

of the above are determined for selected trial standarci levels. These calculations are done by the use 

of a spreadsheet tool called the National Energy Savings (NES) Spreadsheet Model, which has been 

developed for all the standard rulemakings and tailored to each specific appliance rulemaking. 

2~6.1 National Energy Savings (NES) Spreadsheet Model 

In order ·to make the analysis more accessible and transparent to all stakeholders, a 

spreadsheet model was developed using Microsoft Excel in Windows 95 to calculate the national 

energy (and water savings, if appropriate) and the national economic costs and savings from new 

standards. Input quantities can be changed within the spreadsheet. Unlike the LCC analysis, in the 

NES Spreadsheet, distributions are not used for inputs or outputs. Sensitivities can be demonstrated 

by running different scenarios. 

2-8 
2.6.2 Shipments 

One of the more important components of any estimate of future impact is shipments; 

Forecasts of shipments for the base case and the standard case need to be obtained as an input to the 

. NES. The most basic method for forecasting future shipments is a simple saturation-based method 

which assumes saturations remain unchanged and solves for a growth rate in shipments sufficient to 

keep saturations constant in light of population growth. There are several factors that can make this 

estimate inaccurate. These factors include possible changes in: the number of households, saturation 

levels, appliance lifetimes, prices (including operating costs), and consumer decisions about whether 

.. to repair rather than replace an appliance. Because of these complexities, and to improve on the 

forecasts, four different statistical models were studied. These are described in detail in Chapter 8 

of this document. 

:Z.6.3 Indirect Employment Impacts 

The July 1996 Process Rule includes employment impacts among the factors to be considered. 

in selecting a proposed standard. The Department will estimate the impacts of standards on 

employment for appliance manufacturers, relevant service industries, energy suppliers, and the 

c~conomy in general. Employment impacts are separated into indirect and direct impacts. Direct 

employirent impacts would result if standards lead to a change in the number of employees at 

manufacturing plants and related supply and seniice firms. Direct impacts will be discussed in the 

section on manufacturer impact analysis. Indirect impacts are impacts on the national economy other 

1han in the manufacturing sector being regulated. Indirect impacts may result from both expenditures 

shifting among goods (substitution effect), and income changing, which will lead to a change in 

overall expenditure levels (income effect). 

Indirect employment impacts from standards are defined as net jobs eliminated or created in 

the general economy as a consequence of increased spending on purchase price of appliances and 

reduced household spending on energy. New appliance standards are expected to increase the 

purchase price of appliances (retail price plus sales tax, and installation). The same standards are also 

c::xpected to decrease energy consumption, and therefore reduce household expenditures for energy. 

Over time, the increased purchase price is paid back through energy savings. The savings in energy 

expenditures may be spent on other items. Using an input/output model of the U.S. economy, this 

analysis seeks to estimate the effects on different sectors, and the net impact on jobs. National 

impacts will be estimated for major sectors of the U.S. economy. Public and commercially available 

data sources and software will be utilized to estimate employment impacts. At least three scenarios 

will be analyzed to bound the range of uncertainty in future energy prices. All methods and 

documentation will be made available for review. 

2-9 
2. 7 CONSUMER ANALYSIS 

The consumer analysis evaluates impacts to any identifiable groups or consumers, such as 

·consumers of different income levels, who may be disproportionately affected by any national energy 

efficiency standard level. 

The Department could evaluate variations in regional energy prices;water and sewer prices, 

variations in energy use and variations in installation costs that might affect the net present value of 

a standard to consumer sub-populations. To the extent possible, DOE obtains estimates of the 

variabilityin each input quantity and considers this variability in its calculation of consumer impacts. 

The analysis is structured to answer questions such as: How many households are better-off with 

standards and by how much? How many households are not better off and by how much? The 

variability in each input quantity and likely sources of information are discussed with stakeholders. 

Variations in energy use for a particular appliance can depend on factors such as: climate, 

type of household, people in household, etc. Annual energy use can be estimated by a _calculation 

based on an accepted test procedure or it can be measured directly in the field. The Department 

performs sensitivity analyses to consider how differences in energy use will affect sub-groups of 

consumers. The impact on consumer sub-groups will be determined using the LCC spreadsheet 

model. 

2.7.1 Purchase Price Increases 

The Department will be sensitive to purchase price increases to avoid ·negative impacts to 

identifialie population groups such as low-income households. Additionally, the Department will 

assess the likely impacts of purchase price increases on product sales and fuel switching. 

2. 7.2 Consumer Participation 

The Department seeks to inform and involve consumers and consumer representatives in the 

process of developing standards. This includes notification of consumer representatives during the 

rulemaking process and where appropriate, seeking direct consWiler input. 

2.8 MANUFACTURER IMPACT ANALYSIS 

The manufactt.i.rer analysis estimates the financial impact of standards on manufacturers and 

calculates impacts on competition, employment, and manufacturing capacity. 

Prior to initiating the detailed manufacturing impact analysis the Department will prepare an 

approach document and have it available for review. While the general framework will serve as a 

guide, the Department intends to tailor the methodology for each rule on the basis of stakeholder 

commeμts. The document will outline procedural steps and outline issues ·for consideration. Three 

2-10 
.. 

important elements of the approach consist of the preparation of an industry cash-flow, the 

d1~velopment of a process to consider sub-group cash-flow, and the design of an interview guide. 

The policies outlined in the Process Rule required substantial revisions to the analytical 

framework to be used in performing manufacturer impact analysis for each rulemaking. In the 

approach document, the Department will describe andobtain comments on the methodology.to be 

used in performing the manufacturer impact analyses. The manufacturer impact analyses will be 

conducted in three phases (see Figure 2.2). Phase 1 consistsoftwo activities, namely, preparation 

of an industry characterization and identification of issues. The second phase has as its focus the 

larger industry. In this phase, the GRIM will be used to perform an industry cash flow analysis. 

Phase 3 involves repeating the process de·scribed in Phase 2 (the industry cash-flow analysis) but on 

different sub-groups of manufacturers. Phase 3 also entails calculating additional impacts on 

competition, employment, and manufacturing capacitr. 

ANO PR 

Screening Analysis 

l§,I.ll.!§41!.!.f@i@b------·· 

i!J@ilfitlllf¥1titttil't.1b ' .. -----·· 

' ' 

Trial Efficiency Levels 

IUfOI.!.fll!.,!.ft;§l;WfMb•,_----·· 

Indirect Employment Impacts 

Impacts on Utilities 

Impacts on the Environment 

and Energy Security 

Manufacturer Impact Analyses 

Estimate Manufacturer Costs 

Estimate Prices and Shipments 

Cash Flow Analysis 

Impacts on Competition 

Impacts on Employment 

lmpac~s on Manufacturing 

Figure 2.2 Flow diagram of the manufacturer impact analysis methodology 

2-11 
2.8.1 Industry Cash Flow 

A change in standards affects the analysis in three distinct ways. Increased levels of standards 

willrequire additional investment, will raise production costs, and will affect revenue through higher 

prices and, possibly, lower quantities sold. To quantify these changes the Department performs an 

industry cash flow analysis using the GRIM. Usually this analysis will use manUfacturing costs, 

shipments forecasts, and price forecasts developed for the other analyses. Financial information, also 

required as an input to GRIM, will be developed based on publicly available data and confidentially 

submitted manufacturer information. · 

The GRIM analysis uses a number of factors- annual expected revenues; manufacturer costs 

such as cost of sales, selling and general administration costs, taxes, and capital expenditures related 

to ~epreciation, new standards, and maintenance- to arrive at a series of annual cash flows beginning 

from before implementation of standards and continuing explicitly for several years after 

implementation. The measure of industry net present values are calculated by discounting the annual 

cash flows from the period before implementation of standards to some future point in time. 

Appendix E provides a description of GRIMs operating principles. 

2.8.2 Manufacturer Sub-Group Analysis 

Using industry ·~verage" cost values is not adequate for assessing the variation in impacts 

among sub-groups of manufacturers. Smaller manufacturers,· niche players or manufacturers 

exhibitinga cost structure largely different from industry averages could be more negatively impacted. 

Ideally, the Department would consider the impact on every firm individually. In highly concentrated 

industries this may be possible. In industries having numerous participants, the Department will use 

the results of the industry characterization to group manufacturers exhibiting similar characteristics. 

The financial analysis of the 'prototypical" firm performed in the Phase 2 industry analysis can serve 

as a benchmark against which manufacturer sub-groups can be analyzed. 

The manufacturing cost data collected for the engineering analysis will be used to the extent 

practical in the sub-group impact analysis. To be useful, however, this data should be disaggregated 

to reflect the variability in costs between relevant sub-groups of firms. 

The Department will conduct detailed interviews with as many manufacturers as is possible 

to gain insight into the potential impacts of standards. During'these interviews, the Department will 

solicit the information necessary to evaluate cashflows and to assess competitive, employment and 

capacity impacts. Firm-specific cumulative burden will also be considered. 

2.8.3 Interview Process 

The revised rulemaking process provides for greater public input and for improved analytical 

approaches, with particular emphasis on earlier and more extensive information gathering from 

interested parties. The· proposed three-phase manufacturer impact analysis process will draw on 

2-12 
multiple infomiation sources, including structured interviews with manufacturers and a broad 

cross-section ofinterested parties. Interviews may be conducted in any and all phases of the analyses 

as determined in Phase 1 of MIA. 

The interview process has a key role in the manufacturer impact analyses, since it provides 

an opportunity for interested parties. to privately express their views on important issues. A key 

characteristic of the interview process is that it is designed to allow confidential information to be 

considered in the rulemaking decision. · 

The initial industry characterization will collect information from relevant industry and market 

publications, industry trade organizations, company financial reports, and product literature. This 

information will aid in the development of detailed and focused questionnaires, as needed, to perform 

all phases of the manufacturer impact analyses. It is the intention of the Department that the contents 

of questionnaires and the list of interview participants be publicly vetted prior to initiating the 

interview process. 

The Phase 3 (sub-group analysis) questionnaire will solicit information on the possible impacts 

of potential efficiency levels on manufacturing costs, product prices, and sales. Evaluation of the. 

possible impacts on direct employment, capitalassets, and industry competitiveness will also draw 

heavily on the information gathered during the interviews. The questionnaires will solicit both 

qualitative and quantitative information. Supporting information will be requested whenever 

applicable. 

Interviews will be conducted according to DOE procedures. Interviews will be scheduled 

well in advance in order to provide every opportunity for key individuals to be available for comment. 

Although a written response to the questionnaire is acceptable, an interactive interview process is 

preferred because it helps clarify responses and provides the opportunity for additional issues to be 

identified. 

Interview participants will be requested to identify all confidential information provided in 

writing or orally. Approximately two weeks following the interview, an interview summary will be 

provided to give participants the opportunity to confirm the accuracy and protect the confidentiality 

of all collected information. All the information transmitted will be considered, when appropriate, 

inDOEs decision-making process. However, confidential information will not be made available in 

the public record. · 

DOE will collate the completed interview questionnaires and prepare a summary of the major 

issues and outcomes. The Department will seek comment on the outcome of the interview process. 

2 .. 9· COMPETITIVE IMPACT ASSESSMENT 

Legislation directs the Department to cons.iderany lessening of competition that is likely to 

result from standards. It further directs the Attorney General to gauge the impacts, if any, of any 

lessening of competition. DOE will make an effort to gather and report firm-specific financial 

2-13 
informationand impacts. The competitive analysis will focus on assessing the impacts to smaller, yet 

significant, manufacturers. The assessment will be based on manufacturing cost data and on 

information collected from interviews with manufacturers, consistent with Phase 3 of the . 

manufacturer impact analyses. The Department of Justice (DOJ) has offered to help in drafting 

· questions to be used in the manufacturer interviews. These questions will pertain to the assessment 

of the likelihood of increasesin market concentration levels and other markei conditions that could 

lead to anti-competitive pricing behavior. The manufacturer interviews will focus on gathering 

information that would help in assessing asymmetrical cost increases to some manufacturers, 

increased proportion of fixed costs pc)tentially increasing business risks, and potential barriers to 

market entry (proprietary technologies, etc.). 

2.10 UTILITY ANALYSIS 

The utility analysis estimates the effects of proposed standards on electric and gas utilities. 

The Department proposes .to use a version of Energy Information Administrations (EIA) 

. widely recognized National Energy Modeling System (NEMS) for the utility and environmental 

analyses. NEMS is a large multi-sectoral partial equilibrium model of the U.S. energy sector that has 

been developed over several years by the EIA primarily for the purpose of preparing the Annual 

Energy Outlook (AEO). NEMS produces a widely recognized baseline forecast forthe U.S. through 

2020 and is available in the public domain. The version ofNEMS to be used for appliance standards 

analysis will be called NEMS-NAECA, and will be based on the AE098 version witji minor 

modifications.' · 

NEMS offers a sophisticated picture of the effect of appliance standards since its scale allows 

it to measure the interactions between the various energy supply a.ild demand sectors and the 

economy as a whole. In addition, the scale ofNEMS permits analysis of the effects of standards on 

both the electric and gas utility industries: 

To analyze the effect of standards, NEMS-NAECA is first run exactly .as it would be to 

produce an AEO forecast, then a second runis conducted with residential energy usage reduced by 

the amount of energy (gas, oil, and electricity) saved due to appliance standards for the appliance 

being analyzed. The energy savings input is obtained from the NES spreadsheet. Outputs available 

are the same as those in the original NEMS model including residential energy prices, generation and 

installed capacity (and in the case of electricity, which primary fuel is used for generation). 

1 EIA approves use of the name NEMS only to describe an AEO version of the model without any modification to code 

or data. Since, in this work, there will be some minor code modifications and the model will be run under various policy scenarios 

that deviate from AEO assumptions, DOE proposes use of the name NEMS-NAECA for the model as used here. 

2-14 
· 2.11 ENVIRONMENT AL ANALYSIS 

The Environmental Assessment is required pursuant to the National Environmental Policy Act 

of 1969 (NEPA)( 42 U.S.C. 4321 et seq.), regulations of the Council on Environmental Quality ( 49 

CFR parts· 1500-1508), the Department regulations for compliance with NEPA ( 10 CFR part 1021) 

and the Secretarial Policy on the National Environmental Policy Act (June 1994). 

The main environmen~l concern addressed is emissions from fossil fuel-fired electricity 

·generation. Power plant emissions include oxides of nitrogen (NQ) and sulfur (S02), as well as 

'.• carbon dioxide (CC>i). The first two are major causes of acid precipitation, which can affect humans 

by reducing the productivity of farms, forests and fisheries, decreasing· recreational opportunities and 

degrading susceptible buildings and monuments. NOx istlso a precursor gas to urban smog and is 

particularly detrimental to air quality during hot, still weather. CQ emissions contribute to raising 

the global temperature via the 'greenhouse effect." The long-term consequences of higher 

temperatures may include perturbed air and ocean currents, perturbed precipitation patterns, changes 

in the gaseous equilibrium between the atmosphere and the biosphere, and the melting of some of the 

ice now covering polar lands and oceans, causing a rise in sea level. 

The · Department proposes to use EIA s widely recognized NEMS for the appliance 

environmental analyses. The version ofNEMS to be used for appliance standards analysis will be 

calledNEMS-NAECA, and will be based on the AE098 version with minor modifications. NEMSNAECA is run exactly the s~e as the original NEMS except that residential energy usage is reduced 

by the amount of energy (gas, oil, and electricity) saved due to appliance standards for the appliance 

being analyzed. The input of energy savings are obtained from the NES spreadsheet. For the 

environmentalanalysis, the output is the forecasted physical emissions. The net benefits of a standard 

will be the difference between emissions estimated by the AE098 version ofNEMS-NAECA and 

those it estimates with a standard in place. 

2 .. 12 REGULATORY IMPACT ANALYSIS 

DOE will be preparing a draft regulatory analysis pursuant to E.O. 12866, 'Regulatory 

Planning and Review," which will be subject toreview under the Executive Order by the Office of 

InformationandRegulatory Affairs (OIRA)58FR51735 (October4, 1993). Eight major alternatives 

were identified by DOE as representing feasible policy options to achieve consumer product energy 

efficiency. Each alternative will be evaluated in terms of ability to achieve significant energy savings 

at a reasonable cost and will be compared to the effectiveness of the rule. 

Under the Process Rule policies, the Department is committed to continually explore nonregulatory alternatives to standards. In Table 2.1 below is a discussion of what was examined in 1994 

and what is being proposed for this rulemaking. The Department will seek comments on this 

approach. 

2-15 
Table 2.1 Comparison of 1994 and 1998 Non-Regulatory Alternatives to Standards 

Alternatives Examined in 1994 Alternatives to Examine in 1998 

-No action -No new regulatory action 

-Consumer tax credits -Consumer tax credits 

-Manufacturer tax credits -Manufacturer tax credits 

-Performance standards -Perfom1ance standards 

-Consumer rebates -Rebates 

-Prescriptive standards 

-Voluntary standards -Voluntary energy efficiency targets 

-Enhanced labeling and consumer education 

-Mass Government purchases 

-Early Replacement 

2-16 
CHAPTER 3: MARKET AND TECHNOLOGY ASSESSMENT 

3.1 INTRODUCTION 

This chapter provides a profile of the clothes washer industry in the United States. The 

preliminary market and technology assessment presentedin this chapter is developed from publicly 

availableinformation. This information will be helpful in identifying the major manufacturers and 

their product characteristics which form the basis for the engineering and the life-cycle-cost analysis. 

Present and past industry structure and industry financial information will help the Department in the 

process of conducting the manufacturer impact analysis. 

3.2 PRODUCT CLASSES 

The Association of Home Appliance Manufacturers (AHAM) defines a compact washer as 

''Combination of cabinet width of24 inches or less and a washer tub capacity ofless than 2.0 cubic 

feet and/or a dryer drum capacity of less than 4.4 cubic feet" [I]. Using the AHAM definition, 

Appliance Magazine Statistical Review shows for 1995 compact washer sales accounted for 2.8% 

(200,000) of the total residential washer market[2]. For the year 1996, washer imports were 2.3% 

of domestic sales. Imports tend to be compact horizontal axis machines (H-axis) [3]. 

3.3 MANUFACTURERS AND MARKET SHARES 

There are five major manufacturers of clothes washers in the United States. Their market 

shares have remained fairly steady in the last seven years, as can be seen from Table 3.1. Each 

manufacturer offers multiple brand names. Some of the brand names come from independent 

appliance manufacturers which have been acquired over time. 

Manufacturer 

Whirlpool 

Maytag 

General Electric 

Electrolux 

(Frigidaire) 

Raytheon/ Goodman 

(Speed Queen) 

Table 3.1 Market Share 

Appliance Magazine Appliance Magazine 

1990 1995 

[4] [5] 

51% 52% 

18% 20% 

16% 15% 

12% 9% 

3% 4% 

3-1 

Appliance Magazine 

1997 

[6] 

52% 

19% 

17% 

7% 

5% 
3.4 HISTORICAL SHIPMENTS 

Clothes washer shipments in the U.S. have been increasing at an average rate of 2.15% over 

the last 10 years. Table 3.2 provides industry unit shipments and industry dollar sales of clothes 

washers for the ten year period 1987-1996. 

Year 

1987 

1988 . 

1989 

1990 

1991 

1992 

1993 

1994 

1995 

1996 

Table 3.2 Industry Unit Shipments [7] 

Shipments Estimated Industry 

(Domestic+ Dollar Sales 

Exports) (Manufacturer 

(in OOO's) dollar value in $mil) 

5,998 

6,190 

6,252 

6,192 1,175.2 

6,197 1,783.5 

6,515 1,898.0 

6,793 1,986. l 

7,035 2,106.3 

6,901 2,090.2 

7,129 2,049.0 

Shipments 

(Exports) 

(in OOO's) 

247.6 

403.3 

385.3 

601.2 

692.7 

760.8 

769.7 

894.2 

824.6 

917.6 

In recent years the sales of horizontal axis washers have been increasing. Traditionally 

horizontal axis machines have accounted for no more than 1 or 2 percent market share. Recent 

publicallyavailable data shows a horizontal axis market share of 5 .4 % (see Table 3 .3 ). This data may 

not include all major retailers and therefore has a margin of error. In the last year sales have picked 

up due to U.S. companies entering the horizontal axis market. High efficiency washer sales have been 

encouraged by regional rebate programs often combined with a rating system developed by the 

Consortium for Energy Efficiency (CEE). Other voluntary programs have encouraged the sales of 

resource efficient clothes washers. [8] The Energy Star program also promotes high efficiency 

washers. Major U .s: manufacturers offering H-axis machines at the time of this TSD (October 1998) 

are as follows: Maytag, Electrolux (Frigidaire), and General Electric. Goodman Manufacturing 

(Amana) also had a horizontal axis machine on the market but at the time this document was written 

was not in production. Whirlpool recently introduced an energy efficient vertical axis (Y-axis) 

machine on the market. General Electric offers a horizontal axis machine made by another 

manufacturer. ., 

3-2 
-. 

Table 3~3 Washer Shipments by Access [9] 

3.5 RETAILERS 

Access 

Top Load/Vertical 

Front Load/ Horizontal 

First Quarter 1998 

94.6% 

5.4% 

, Distributers are virtually non-existent in this market. Manufacturers deal directly with retail 

outlets or buying groups formed by smaller appliance stores (see Table 3.4). (10] 

Table 3.4 Washer Sales b~ Type of Outlet [11) 

Outlet 1997 Market Share 

Mass Merchandiser 32.6 

Appliance Store 28.8 

CE Superstore 10.4 

Department Store 7.9 

Furniture Store 5.0 

Discount Dept. Store 3.7 

Home Center 3.7 

Hardware Store 1.1 

Home Builder 1.0 

Warehouse Club 1.0 

Kitchen Remodeler 0.2 

Catalog Showroom 0.1 

Landlord 0.1 

Mail Order/Direct 0.1 

Other 4.3 

3-3 
3.6 PRICE DISTRIBUTION 

Table 3.5 lists the clothes washer unit sales in the U.S. by price range. Clothes washer retail 

or consumer prices are required for conducting the life-cycle-cost analysis and manufacturer prices 

are required for identifying the financial impact of standards on manufacturers. Chapter 6 of this 

document provides detailed information on both retail and manufacturer prices and markups . 

• 

Table 3.5 Clothes Washer Unit Sales by Price Range [12] 

Price Range Market Share 1997 

. Under$200 1.2% 

$200-299 6.3% 

$300-399 38.2% 

$400-499 31.7% 

. $500-599 13.7% 

$600-699 5.6% 

$700-799 0.7% 

$800 and above 2.6% 

3. 7 PRODUCT CHARACTERISTICS 

Clothes washers of different capacities and configurations are sold in the market., A survey 

by Elrick and Lavidge [13] forthe year 1995, provided information in Table 3.6 below. More recent 

data, in Table 3.7, shows the market share between 16-pound and 18-pound & over capacities. 

3-4 

; 
" 

:. 

Table 3.6 Washer Characteristics (1995) 

Features Units Sold Average Price 

Type 

Regular Side by Side 97.6% $373 

Door 

Top Load 99.9% $384 

Front Load 0.1% $668 

Capacity 

Large Load (15-18 lbs.) 35.6% $355 

Extra Large Load (19+ lbs.) 64.1% $401 

Table 3.7 Washer Shipment by Capacity [14] 

Capacity 1997 Market Share 

16-pound 19% 

18-pound and over 81% 

Table 3.8 provides the brand names used by the different manufacturers and the price level 

for a given brand. Manufacturers also build dryers, usually marketed as a set washer with matching 

dryer. See Appendix B for a list of manufacturers and addresses. 

3-5 
Table 3.8 Brand Names, Price Level and Market Share of Major Manufactures (15] 

Parent Company Brand Price Level 

Whirlpool Sears/Kenmore Varied· 

Whirlpool Mid-priced 

Roper Value 

Kitchen Aid High 

Maytag Maytag High 

Admiral Mid-low 

Magic Chef Value 

Norge/Crosley Value 

General Electric General Electric High/Mid. 

Hot Point Value 

RCA Mid 

Electrolux Frigidaire Mid-High 

White Westinghouse Value 

Kelvinator Value. 

Tappan Mid 

Gibson Value 

Raytheon1 Amana Mid 

Speed Queen· Value 

Source: DealerScope, "Statistical Survey and Report:, May 1996, P.60 

Quality levels taken from Smith Barney Whirlpool/Maytag Company Report 1117196. 

1 Raytheon sold the Amana brand to Goodman Holdings, Inc. and licences the Speed Queen brand to them. 

3-6 
Table 3.9 Washer Market Share by Brand f161 

Brand 1997 Market Share(%) 

Sears/Kenmore 25.6 

Whirlpool 20.4 

Maytag 17.1 

General Electric 12.4 

Amana 4.5 

Frigidaire 4.3 

Roper 3.3 

Hotpoint 2.8 

White-Westinghouse 2.1 

Admiral 1.3 

Speed Queen 1.2 

Gibson 1.0 

Kitchen Aid 1.0 

Magic Chef 0.5 

RCA 0.5 

Montgomery Ward 0.4 

Kelvinator 0.3 

Asko 0.2 

Crosley 0.2 

Other 1.9 

3.8 MARKET SATURATION 

The saturation of clothes washers varies with the type ofhousing and income level.The 1990 

Census gives a clothes washer penetration of 76%. Appliance Magazine gives a saturation for 1997 

of 79.7%.[17] 

3-7 
3.9 VOLUNTARY PROGRAMS (TO INCREASE WASHER EFFICIENCY /PROMOTE 

HORIZONTAL AXIS MACHINES) 

The Department researched volwitazy programs. Research included regional programs that 

encouraged the buying of high efficiency clothes washers through rebates, education and 

demonstration projects. National efforts such as Energy Star are also sununarized. The entire report 

is located in Appendix F. · 

40 

35 

c 30 

0 

; 

I! 25 

.a 

;J 20 

-c 

GI 15 ~ GI 

0.. 10 

5 

0 

Eled. Oil 

Water Heater Fuel 

Figure 3.1 Water Heater and Dryer Fuel Use 

3.10 FUEL USE 

Saturation of Fuel Type 

Beet 

Or}9' 

Fuel 

Although clothes washers are all electric powered, most of the energy used by clothes washers 

is hot water from a water heater. This for the most part is either gas or electric water heaters. The 

reduction in drying clothes due to greater moisture extraction in the clothes washer is also a 

contribution to clothes washer energy savings and is therefore also shown in the chart below.[18] 

Figure 3 .1 below is for households having a clothes washer and is taken from EIA 199 5 Databook 

which uses data collected in 1993. 

3-8 
• 

~ 

Table 3.10 Market Saturation by Fuel Type Amone Housine Units with Clothes Washers 

Water Heater Fuel Percent Dryer Fuel 

Type Percent Electricity Gas None 

piped gas 52.97 31.14 17.3 4.53 

electricity 38.81 35.3 0.49 3.03 

fuel oil 4.36 3.33 0.18 0.85 

!pg 3.32 2.53 0.79 

other 0.46 0.36 0.05 0.04 

none 0.08 0.01 0.07 

3.11 INDUSTRY COST STRUCTURE 

Based on publicly available information from thtCensus of Manufactures (Table 3.11) and 

the Securities and Exchange Commission (SEC) 10-K reports filed by publicly owned manufacturers 

(summarized in Table 3.12), the washing machine industry cost structure was developed. 

Table 3.11 Information from the 1992 Census of Manufactures (MC91-I-36B) 

Census Year Cost of Materials as a Cost of Payroll for Cost of Total Payroll 

percent of value of production workers as a (production+admin.) as a 

shipments percent of value of percent of value of 

shipments shipments 

(percent) (percent) (percent) 

1992 52 10:2 12.7 

1987 54 12.3 15.3 

1982 53 12.3 15.8 

3-9 
Table 3.12 Summary of SEC 10-K reports of appliance manufacturers 

Appliance Manufacturers 1992-1996 % of Sales 

Net Sales $52,381.7 100% 

Cost of Sales $38,877.8 74.2% 

Selling, General & Admin. $9,574.0 18.3% 

Research & Development $953.7 1.8% 

Depreciation $1816.3 3.5% 

Amortization, Tooling $0.0 0.0% 

Amortization, Intangibles $154.8 0.3% 

Income from Cont. Operations $3143.0 6.0% 

Income before Interest, Taxes, Extr. $2952.8 5.6% 

Items 

Net Income $1094.2 2.1% 

Capital Expenditures $2,368.0 4.4% 

The resulting cost structure is shown in Figure 3.2. This a composite for the industry as a 

whole; no single firm may have these results. In developing the cost structure for the washing 

machine industry, it is assumed that the washing machine industry is a microcosm of the total major 

-appliance industry. - · 

R&D 

Income _from 

Operations 

s:o% 

1.8% ..--.=== 

11.7% 

10.2% 

Material 

52.0% 

Figure 3.2 Washing machine iiidustry cost structure as a percentage of revenues 

3-10 

-. 
A detailed financial analysis of the clothes washer industry to identify key financial inputs like 

cost of capital, working capital, depreciation, capitalexpenditures etc. is presented in Chapter 9 of 

this document. 

3.12 TECHNOLOGY ASSESSMENT 

The efficiency of some clothes washers currently on the market have been improved by adding 

design options such as: 

• adaptive fill control, 

temperature sensing to set wash temperatures more precisely and to lower the water 

temperature (to allow lower water temperatures), 

• features that promote greater moisture removal for the final spin cycle; such as higher spin 

speeds, longer spin speeds and 

• horizontal axis designs (also referred to as tumble wash designs). 

More detail on clothes washer technology is available in two DOE reports: Draft Report on 

the Preliminary Engineering Analysis for Clothes Washers [19] and Draft Report on Design Options 

for Clothes Washers [20] .. · 

3.13 ENERGY EFFICIENCY 

The test procedure for which this rulemaking will be based on will only take effect when a 

new standard is enacted. Therefore, efficiency data publicall)available is still based on the current 

test procedure referred to as Appendix J. In order to get an idea of current efficiencies on the market 

using the future test procedure metric of JI and how they correlate to current test procedure ratings 

using EF, nine clothes washers were tested to the new test procedure Appendix J 1. A summary of 

the washers tested are shown in Table 3.13. Test results for Appendix J and JI are shown in Table 

3.14. 

3-11 
Table 3.13 Product characteristics of clothes washer tested 

Clothes Washer Type Capacity Warm Rinse Extra Rinse 

(cu.ft.) 

A Y-axis 3.20 No No 

B Y-axis 3.20 Yes No 

c Y-axis 2.45 Yes· Yes 

D H-axis 2.85 Yes No 

E Y-axis 3.08 Yes No 

F Y-axis 3.28 No Yes 

G Y-axis 3.02 No No 

H H-axis 2.54 Yes No 

I Y-axis 2.93 Yes No 

T a e . bl 3 14 : S ummaryo f A .ppen d" ix J d an Jl t t es resu It s 

Clothes Washer EF % Efficient MEF % Efficient 

( cu.ft./kWh/ overNAECA ( cu.ft./k Wh/ overAHAM 

cycle) minimum cycle) baseline 

(EF=l.18) (MEF=0.817) 

A 1.44 18.1 1.05 22.1 

B 1.50 21.3. - -

c 1.160 0 0.822" 0.6 

D 2.339 49.6 1.516 46.1 

E 1.166 0 0.946 13.6 

F 1.326 11.0 1.159 29.5 

G 1.149 0 1.098 25.6 

H 3.929 70.0 1.470 44.4 

I 1.141 0 0.851 4.0 

ITS, 1998, Energy Efficiency Testing on Clothes Washers, Report No. 198004015-00 l, Intertek Testing 

Services, Cortland, NY. 

3-12 
JREFERENCES 

1. Conversation with Mary K. Matimore, Manager Market Data, AHAM, 7 July 1997. 

2. Appliance Magazine, January 1997, p. 38 

3. Association of Home Appliance Manufacturers (AHAM), 1997 Major Appliance Industry 

Fact Book. 

4. Appliance Magazine, September 1990 

5. Ibid. 

6. Appliance Magazine, September 1998 

7. Association of Home Appliance Manufacturers. 1997.1997 Major Appliance Industry Fact 

Book Chicago, IL. 

8. Revised Draft Report on Consumer Research for Clothes Washers; April.3, 1998, Comment 

#85, Docket# EE-RM-94403 

9. Ibid. [Dealerscope referenced Intellect ASW] 

IO. EPRI, Distribution Analysis: THELMA, P. 1-4. 

11. Dealerscope, June 1998, North American Publishing Company, p. 23 

12. Ibid. 

. 13. Elrick and Lavidge, 1996. One page memo - Washer Sales Percentage of Units Sold by 

Features. 

14. Dealerscope, June 1998, North Am~rican Publishing Company, p. 23 

15. EPRI, Marketing Strategies for Horizontal Axis Washers, December 1996, p.2-8, EPRI TR107612, Prepared by Dove Associates, Inc. Boston, Massachuse_tts. 

16. Dealerscope, June 1998, North American Publislting Company, p. 23 

17. Appliance Magazine, September 1998, p. 72 

18. EIA, RECS 1995 · 

19. Draft Report on the Preliminary Engineering Analysis for Clothes Washers, DOE Comment 

55B, Docket No. EE_RM_94-403, November 15, 1996 

3-13 
20. Draft Report on Design Options for Clothes Washers, DOE Comment 55C, 

Docket No. EE-RM-94~403, November 15, 1996 

. 3-14 
CHAPTER 4: ENGINEERING ANALYSIS 

4.1 INTRODUCTION 

The Engineering Analysis determines the maximum technologically feasible energy efficiency 

leveland develops cost-efficiency relationships to show the manufacturer costs of achieving increased 

efficiency. As described later in this section, manufacturers were asked to provide clothes washer. 

performance and manufacturing cost information for conducting the engineering analysis. Data was 

-~ collected utilizing the efficiency-level approach. The clothes washer manufacturers remarked that this 

approach was the most suitable approach for showing the manufacturing cost of achieving increased 

efficiency and also provided.a high degree of confidentiality of design. This approach consists of 

letting the manufacturers supply cost and corresponding efficiency data using any design options or 

design option combinations they choose. The collected information describes manufacturer specific 

costs of achieving predetermined efficiency levels. This format has the benefit of being entirely 

compatible with data requirements for manufacturer impact analysis. 

4.2 PRODUCT CLASSES 

For this rulemaking the Department is proposing the following classes: Compact (less than 

2.0 ft. 3 capacity) and Standard (2.0 ft. 3 or greater capacity). Based on current information, the 

Department believes that there is no basis for separate product classes for H- and Y-axis clothes 

washers. Recent and near-term product offerings, and working prototypes of H- and Y-axis clothes 

washers demonstrate large energy savings while maintaining important product features. The 

Department is proposing to eliminate certain previously defined classifications (Semi-Automatic 

Top-Loading, Front-Loading and Suds Saving) because they do not offer any added utility which is 

inherently less energy efficient and therefore would require protection from the energy conservation 

standards. In the May 1991 Final Rule, these classes were not subject to minimum energy 

c:onservation standards because they represented a small portion of the market, and there was 

i:nsufficientinformation to analyze them. However, the 1988 standard requiring an unheated rinse . 

option is still applicable to these classes. The D~partment has further reviewed this topic and believes 

that these products should be subject to the minimum energy conservation standards applicable to 

either compact or standard clothes washers. 

4.3 BASELINE UNIT 

A baseline unit is the starting point for analyzing design options for improving energy 

efficiency. In this engineering analysis, the baseline unit represents a typical model with an energy 

efficiency. no lower than the minimum required by the amended National Appliance Energy 

Conservation Act (NAECA). New minimum standard for clothes washers (top loading, 1.6 cu.ft. 

4-1 
or greater cap.acity) was set at Energy Factor (EF) = 1.18 cu.ft/kWh per cycle and became effective 

on May 14, 1994. 

The Department issued two new test procedures during the course of this rulemaking: 

Appendices '1"and '1 l "62 FR 45484 [ 1]. (See Section l.B.2. on Test Procedure). The Engineering 

Analysispresented in this document is based on the Appendix JI test procedure. This test procedure 

calculates a MEF (Modified Energy Factor) descriptor. Unlike its EF predecessor, the MEF uses 

remaining moisture content (RMC) to account for energy saved due to lower drying times and 

temperature use factors (TUFs). Using cloth loads and different water temperatures are among the 

many other substantive differences between the J and J 1 test procedures. Given these different testing 

methods and variables, there is no computational relationship between the EF and MEF descriptors. 

In 9rder to determine the MEF value for the baseline unit, clothes washer manufacturers were 

asked to take a representative clothes washer with an EF as close as possible to 1.18 (current 

minimum EF) and perform the new J 1 procedure. If no clothes washer was available with an EF 

value close to 1.18, they were asked to adjust the water volume, machine energy, and/or hot water 

volume to obtain an EF of 1.18. Five manufacturers (Amana, Frigidaire, GEA, Maytag and 

Whirlpool)submitted data to the Association of Home Appliance Manufacturers (AHAM). AHAM 

mathematically averaged these values to derive an industry average MEF value of 0.817 for the 

baseline unit (based on an EF=l.18) [2]. 

4.4 . COST-EFFICIENCY DATA COLLECTION 

Manufacturing cost and efficiency data was collected utilizing the efficiency level approach, 

in cooperation with AHAM. The manufacturers provided cost and corresponding efficiency data 

using any design options or design option· combinations they choose. The collected information 

describes manufacturer specific costs of achieving predetermined efficiency levels in addition to the 

energy-use characteristics. AHAM members provided costs and energy data for the following levels: 

Baseline, 5%, 10%, 15%, 20% and 25% improvement applied in an existing Y-axis clothes washer 

and improvements of35%, 40%, 45%, and 50% applied in a H-axis clothes washer. Manufacturers 

believethat the disclosure of their individual costs is not practical given the proprietary nature of the 

information. Hence they agreed to provide detailed cost information to AHAM which would then 

aggregate this data and provide 'industry" costs to DOE. 

4.4.1 .Characterizing Uncertainty 

In the manufacturing cost information previously supplied by manufacturers (Comment#38, 

AHAM), a confidence level was provided to characterize the probability of achieving the costs 

reported. Building on this framework, DOE proposed modification to the most recent data collection 

so that the cost estimates reported would be bound by a range. DOE requested manufacturers to 

provide a range of costs associated with achieving each efficiency level. Separate ranges were 

requested for variable and fixed costs. 
For example, for characterizing variable costs, each manufacturer was asked to provide its 

best point estimates ("most likely" values) for incremental material, labor and overhead costs for 

achieving a particular efficiency level. These disaggregated costs were then added to arrive at the 

"most likely" total variable cost for that manufacturer. In addition manufacturers were asked to 

indicate their maximum and minimum total variable costs to meet the efficiency level. Triangular 

distributions created using minimum, maximum, and "most likely" values were used to characterize 

the uncertainty associated with the manufacturing cost estimates. Similar procedure was adopted for 

fixed costs. Figure 4.1 shows illustrative input distributions for incremental manufacturing costs. 

This figure assumes that data is provided by three manufacturers. 

Efficiency 

Level1 

Efficiency 

Level2 

0 

0 

2 3 4 

Incremental Cost ($) 

Incremental Cost ($) 

5 6 7 

7 

Manu~ Mkt 

8 acturer Share 

A 50% 

B 30% 

c 20% 

8 

Figure 4.1 Illustrative input distributions for incremental manufacturing costs 

4.4.2 Variability in Costs between Manufacturers 

Legend 

-

-·-· 

--

The clothes washer manufacturing costs previously submitted to DOE (Comment#38, 

AHAM) demonstrated large variability in some cases. This variability was expressed using the 

standard deviation in previous data submittals. DOE will not be using standard deviation figures to 

conduct its subgroup analysis since these may mask serious cost impacts, particularly to smaller firms. 

The Department requested disaggregated cost data from all manufacturers. Cost distributions, both 

variable and fixed, for the industry for achieving a particular efficiency level were obtained by 

applying shipment-based weights to each manufacturers cost distribution. This approach captures 

the richness of the data provided by manufacturers and ensures that the final cost distributions 

incorporate each individual manufacturers cost distributions thereby accounting for variability in the 

data submittals. 

4-3 

Table 4.1 Energy-use Data 

Product Characterisics Energy Use Water Use 

Percent MEF TotalEne~ Clothes Hot Machine ... _J:?.l::Y.~~ .. - Water Water ··------·----· ------ -- ... ______ ···--··-··---····- ···································- . ····-······ 

lmprovemen wl dryer Container Water Energy Enerizy Use Use 

CU. ft./kWhfcyclE kWh/cycle cu.ft. kWh/cycle kWh/cycle kWh/cycle ga/./cyc gal.I cu ft. 

[I] [2] [3] [4) [SJ [6) [7] [8] . [9] 

Baseline 0.817 3227 2.847 1.587 0209 1.43 39.181 13.779 

5 0.860 3.165 2.817 1.543 0209 1.413 38.613 13.732 

10 0.908 3.017 2.822 1.408 0209 1.4 38.613 13.71 

15 0.961 2.833 2.832 1216 0209 1.407 38.621 13.67 

20 1.021 2.739 2.893 l.113 0218 1.408 38.446 13.342 

25 1.089 . 2292 2.866 0.715 0.304 1273 26.6 9.22 

35 1257 1.866 2.749 0.462 0.133 127 21.03 7.661 

40 1.362 1.859 2.749 0.462 0.133 1263 21.03 7.661 

45 1.485 1.651 2.736 0.429 0.114 1.107 . 23.405 8.57 

50 1.634 1.574 2.736 0.413 0.114 1.047 23.405 8.57 

For data in columns [3), [4], [5), [6],[7),[8], and [9], companies were asked to approximate 

the energy component that they would use to develop the MEF shown in column [2). Where this 

data includes multiple products with varying clothes container capacities, for instance, a shipment 

weighted average within the product mix could be used. 

4.5 DATAAGGREGATION 

Arthur D. Little prepared a spreadsheet to aggregate cost data obtained from various 

manufacturers. AHAM used this spreadsheet to aggregate the cost data obtained from the 

manufacturers. This aggregated data was then presented to DOE. This procedure helped address 

concerns of manufacturers concerning the proprietary nature of the manufacturing costs and their 

disclosure . 

. The aggregation spreadsheet takes each manufacturers total variable (labor, material and 

overhead) and total fixed (tooling, investment, R&D, and marketing) costs characterized using 

triangular distributions and aggregatesthem applying shipment-based weights to arrive at the total 

variable and fixed cost distributions respectively for the industry for achieving each efficiency level. 

The aggregation spreadsheet was developed using Microsoft Excel and Crystal BaIIM [3] software 

and has the ability of treating custom distributions also. 

The following procedure is used for arriving at the industry incremental total variable cost 

distribution for a particular efficiency level from individual manufacturers' cost submittals: 

4-5 
0 

• 

• 

Each manufacturers incremental material, labor and tooling costs are added to arrive at the 

manufacttirers "most likely" incremental total variable cost. 

The "most likely" incremental total variable cost along with the minimum and maximum 

values of incremental total variable costs, as reported by manufacturers, are used to create 

triangular distribution. for the incremental total variable cost. 

Samples are drawn from each manufacturers incremental total variable cost distributions to 

create an industry incremental total variable cost distribution. The number of times a 

particular manufacturers cost distribution is sampled relative .to others is dependent on its 

relative market share (when compared to other manufacturers). 

Figure 4.2 graphically illustrates individual manufacturers' incremental total variable cost 

distributions and the industry total incremental variable cost distribution thus obtained (here it is 

assumed that the industry is made up of two manufacturers with eachhaving a 50% market share). 

The final cost distributioncan also be presented using percentile values-. 0, 10, 25, 50, 75, 

90 and I 00 percentiles. Crystal BallTM software was used for specifying individual distributions and 

for sampling them to arrive at the final distribution. Crystal BallTM provides the flexibility of reporting 

the final distribution graphically or by specifying percentile values. AHAM provided aggregated 

industry cost data using percentile values. 

This procedure was used for arriving at the industry incremental total variable cost distribution 

for each effici.ency level. Industry incremental total fixed cost distributionare also obtained using 

the same steps as for the variable cost. In addition, shipment weighted averages of the most likely 

costs, both fixed and variable, were also calculated for each efficiency level as point values for 

fo.cremental costs. 

4-6 

-. 
Manufacturer A 

Efficiency Manufacturer B 

Level X 

0 1 I ~ ~ 4 5 6 I t Q I I 

I I I I Incremental Cost ($) I I I 

I _,_, _, _, _, ;e-1 

~I ;;e I :;e I :ie1 ;;e1 ;;el o I 

~I ~I ~I 01 ,,, I 01 81 -, :-1 -i ~, !:::.1 !!?..1 ~I 

I I 

~ :s 

ftl 

.D 

e 

Q. Total Rel. 

Cll Probability > ;:: = 1.0 ftl 

Cii 

a::: 

1.00 2.75 4.50 6.24 8.00 

Incremental Cost ($) 

Figure 4.2 Industry cost distribution obtained by sampling each manufacturers cost 

distributions 

The results after aggregation are presented in Tables 4.2 through 4.4. Table 4.2 gives the 

industry cost data using "most likely" values. Tables 4.3 and 4.4 present detailed cost distributions 

for total variable, total fixed, and incremental total manufacturing costs using percentile values. 

Table 4.2 Aggregated Cost Data using "Most Likely" Values 

Product Characterisics Variable Costs Total Total Incremen. No.of 

Percent MEF Cost Per Unit Variable Fixed Mfg. Cost Respondents 

Improvement Materials Labor Overhead Costs Costs 

Cll. ft.lk:Wltlcycle ($per Unit) ($per Unit) ($per Unit) ($per Unit) 

[I) (2] (12] (13) [14) (JS] [22] (23] 

Baseline 0.817 

5 0.860 $0.01 $0.01 $0.00 $0.02 $0.07 $0.09 5 

10 0.908 $0.25 $0.04 $0.01 $0.30 $0.61 $0.91 5 

IS 0.961 $3.18 $0.10 $0.04 $3.32 $1.01 $4.33 s 

20 l.021 $10.71 ($0.02) ($1.80) $8.89 $6.21 $15.10 5 

2S l.089 $36.14 $8.61 $0.09 $44.84 $19.29 $64.13 . 5 

3S 1.257 $86.73 $10.94 $7.78 $105.4S $22.72 $128.17 . s 

40 1.362 $87.15 $10.94 $7.78 $105.87 $22.72 $128.S9 s 

4S 1.485 $12S.S8 $1S.52 $14.SI $155.61 $24.67 $180.28 4 

so 1.634 $132.40 $15.68 $14.51 $162.59 $24.67 $187.26 4 

4-7 
For column [ 12], companies were asked to indicate incremental costs associated with 

materials that would be needed to achieve the particular standards levels in $ per washer unit. 

For column (13), companies were asked to indicate the incremental cost oflabor associated 

with the new technology to reach the particular standard levels in $ per washer unit. 

For column [ 14], companies were asked to indicate the incremental overhead cost associated 

with this standard level. On column (14), for example, additional electricity to produce new 

components may be considered as overhead. 

For each of these columns, entries on the baseline line are not necessary. ·However, 

companies that may h,ave variable cost increases necessary to bring all production up to th~ minimum 

ofMEF=0.817 were asked to include this cost in the 5% improvement line. 

4-8 
i 

~---.,....-----~-------- ------ ---------· 

Table 4.3 Industry Cost Distributions for 5%, 10%, 15% 20% and 25% Improvements 

applied to an existing V-axis Clothes Washer 

Product Characterisics Total Total Incremental 

Percent Percentile MEF Variable · Fixed Manufacturing 

Improvement Costs U) Costs (q) Costs 

(b) cu. ft./kWhlcycle ($per Unit) ($per Unit) ($per Unit) 

Baseline 0.817 

5 100 (Max.) 0.860 $0.24 $0.87 $1.J l 

90 $0.01 $0.18 $0.18 

75 $0.01 $0.01 $0.02 

50 $0.01 $0.01 $0.02 

25 $0.01 $0.01 $0.02 

IO . $0.01 $0.01 $0.02 

0 $0.00 $0.00 $0.00 

IO IOO <Max.) 0.908 $6.75 $10.84 $12.84 

90 $1.26 $1.09 $2.71 

75 $0.49 $0.30 $0.96 

50 $0.01 $0.06 $0.07 

25 $0.01 $0.05 $0.06 

10 $0.01 $0.01 $0.02 

0 $0.00 $0.00 $0.00 

15 100 (Max.) 0.961 $40.00 $10.84 $46.19 

90 $3.96. $4.36 $14.08 

75 $0.76 . $0.30 $0.97 

50 $0.65 $0.15 $0.80 

25 $0.58 $0.12 $0.72 

10 $0.01 $0.01 $0.02 

0 $0.00 $0.00 $0.00 

20 100 (Max.) 1.021 $70.00 $20.60 $79.45 

90 $27.78 $15.63 $38.48 

75 $14.13 $11.75 $27.74 

50 $0.72 $0.33 $1.06 

25 $0.65 $0.26 $0.95 

IO ($0.53) $0.24 $0.90 

0 ($12.20) $0.21 $0.80 

25 100 <Max.) l.089 $83.28 $27.63 $102.63 

90 $70.40 $25.36 $93.97 

75 $67.24 $24.04 $90.85 

50 $63.46 $22.14 $86.26 

25 $15.11 $14.39 $29.37 

IO ($0.30) $14.07 $16.13 

0 ($12.20) $4.85 $1.28 

4-9 
Table 4.4 Industry Cost Distributions for 35o/o, 40°/o, 45% and 50°/o Improvements 

applied to a H-axis Clothes Washer 

Product Characterisics · Total Total Incremental 

Percent Percentile MEF Variable Fixed Manufacturing 

Improvement Costs {j) Costs (q) Costs 

{b) cu. ft./kWh/cyc/e ($per Unit) ($per Unit) ($per Unit) 

Baseline 0.817 

35 100 (Max.) 1.257 $280.00 $101.60 $381.60 

90 $228.80 $25.28 $254.01 

75 $118.63 $23.29 $143.34 

50 $69.06 $21.41 $90.56 

25 $63.59 $19.44 $85.30 

IO $60.38 $17.20 $81.74 

0 $52.03 $15.06 $67.43 

40 100 (Max.) 1.362 $290.00 $101.60 $391.60 

90 $229.28 $25.28 $254.44 

75 $117.17 $23.30 $143.57 

50 $69.05 $21.41 $90.54 

25 $63.73 $19.44 $85.28 

10 $60.44 $17.15 $81.74 

0 $52.03 $15.06 $67.43 

45 100 (Max.) IA85 $300.00 $101.60 $401.60 

90 $239.76 $28.23 $266.26 

75 $227.29 $23.70 $251.73 

50 $128.38 $19.89 $153.71 

25 $79.38 $18.02 $97.37 

10 $69.25 $16.95 $87.36 

0 $52.30 $15.06 $68.26 

50 100 (Max.) 1.634 $300.00 $101.60 $401.60 

90 $239.46 $28.29 $266.11 

75 $227.31 $23.76 $251.70 

50 $132.89 $19.89 $157.87 

25 $95.45 $18.00 $113.81 

10 $84.95 $16.94 $102.98 

0 $66.16 $15.06 $82.12 

4-10 
The AHAM data submittal was used to develop a cost-efficiency graph. This graph is 

presented in Figure 4.3 

AHAM Clothes Washer Data 

450 

400 4-

;;; 350 ...... 

en 

!!? 300 

-- • ~ 250 • • )( 4-100% 

(.) • 90% 

:! 200 X75% 

c ><50% 

cu &25% E 150 )( )( )( 

f - • 10% 

(,) • .. •0% .E 100 

4- ~ >< • ·SWA ,..... 

• • • 50 4-

0 • .... CIC ..... ..... O> .... N Ll) ~ 

..... co 0 co N co LO co co <""> CIC co O> 0) C! C! C'! ~ -.i: <'! d d d d ..... ..... 

(Baseline) (5%) (10%) (15%) (20%). (25%) (35%) (40%) (45%) (50%) 

Modified Energy Factor 

ll?igure 4.3 Cost-Efficiency graph for clothes washers· 

4.6 RETAILPRICE 

The results of the Engineering Analysis are summarized in the Cost-Efficiency graph using 

percentilevalues, as presented in Figure 4.3. Manufacturer and retail markups, discussed in Chapter 

6 of this document, will be applied to these total incremental cost distributions to determine the 

purchase price (i.e. price to the consumer) of clothes washers. These consumer prices will theme 

used along with energy-use data to perform LCC analysis. 

4l. 7 PROPRIETARY DESIGN 

At the November 1996 workshop, it was acknowledged that Whirlpool had four patented 

proprietary prototype designs which include both V- and H-axis platforms. Whirlpool indicated that 

these were working prototypes. In a letter dated November 29, 1996, DOE communicated the 

patents numbers of Whirlpools designs to the workshop participants. (DOE, No. 57). During the 

4-11 
workshop, Whirlpool asked that the designs be included in the rulemaking analysis. Whirlpool also 

indicated that it would be appropriate to conduct an independent study to estimate the manufacturing 

costs of the new designs. (Whirlpool, No. 55L at 169). Whirlpool did not see the practicality of each 

manufacturer estimating the cost of the Whirlpool designs, since estimates by other manufacturers 

would only be based on patent information and could not be expected to produce consistency in 

approach or a high degree of accuracy. (Whirlpool, No. 69 at 4). · 

Maytag commented that the Whirlpool designs needed to be subjected to a full and complete 

engineering and cost analysis by DOE. Maytag requested that all manufacturers be given the 

opportunity to participate in this process since the cost of applying these designs to a manufacturers 

own basic washer design varies greatlyfrom manufacturer to manufacturer. (Maytag, No. 64 at l ). 

GEA also stated that the analysis needed to be expanded to cover the designs disclosed by Whirlpool. 

_ It further stated that only a revised method focusing on the technical know-how, manufacturing 

capabilitiesand economic strengths of individual manufacturers would permit the proper evaluation 

of the impacts on ·~typical manufacturers." (GEA, No. 63 at 7). 

In response the Department conducted a 'tear-down" manufacturing cost assessment of one 

of the V-axis Whirlpool prototype. The main objective of the manufacturing cost assessment, is to 

quantify the differential manufacturing costs o#>roducing high efficiency clothes washers based on 

( l) the Whirlpool proprietary V-axis design, and (2) commercially available V- and H-axis designs. 

See Chapter 5 for further details. 

. 4-12 
REFERENCES 

1 

I. U.S. Department of Energy (DOE) 1997. Energy Conservation Program for Consumer 

Products: Test Procedure for Clothes Washers and Reporting Requirements for Clothes 

Washers, Clothes Dryers, and Dishwashers. 

2. Association of Home Appliance Manufacturers (AHAM). December 2, 1997. Letter from 

Wayne Morris (Technical Director--Regulatory Affairs, AHAM) to Bryan Berringer 

(Program Manager, Clothes Washer Rulemaking, U.S. DOE). 

3. Crystal BalfM. 

4-13 
CHAPTERS: MANUFACTURING COST ASSESSMENT 

5.1 INTRODUCTION 

At the November 1996 clothes washer 'workshop, it was acknowledged that Whirlpool has 

four patented proprietary designs which include both V- and H-axis designs. In order to include 

Whirlpools proprietary V-axis designs in the rulemaking analysis the Department of Energy decided 

to conduct a 'tear-down" manufacturing cost assessment of the Whirlpool Y-axis prototype. Arthur 

D. Little (AOL) was chosen tp conduct this study. This chapter presents the Phase I of this study. 

5.2 PROGRAM OBJECTIVE 

The main objective of this project is to quantify the differential manufacturing costs of 

producing high efficiency clothes ·washers based on ( 1) the Whirlpool proprietary V-axis design, and 

(2) commercially available H-axis designs. The overall project consists of two phases: 

Phase I provides a detailed and objective cost estimate for two state-of-the-art, high volume, 

V-axis washer as a baseline for further analysis. The major objective of this phase is to obtain 

stakeholder comment on the costing methodology and baseline costs. 

Phase II develops a differential cost estimate for the proprietary Y-axis design and for two 

commerciallyavailable H-axis clothes washer_, relative to the baseline clothes washer evaluated in 

Phase I. 

This chapter describes the final results of Phase I. Several manufacturing plant visits were 

· conducted along with process equipment and supplier interviews, sensitivity analysis on critical cost 

parameters, and incorporation of stakeholder suggestions from the March 11-12, 1998, workshop. 

5.3 PHASE I OBJECTIVES 

• Develop a clear and consistent manufacturing cost assessment methodology for clothes washers. 

• Provide detailed manufacturing cost estimate of a high production volume 'typical" clothes 

washer for a baseline estimate. 

• Report the results of the analysis in a way that preserves confidentiality of data. 

• Obtain input from all stakeholders as to the validity of the methodology and on the 

'reasonableness" of the baseline manufacturing cost estimates. 

5-1 
5.4 ASSUMPTIONS 

5.4.1 Baseline Definition· 

Two clothes washers were selected to be used in the baseline cost assessment. They are 

among the top selling machines as reported in a recent market survey by Intellect, Inc. ·Two machines 

were disassembled, rather than one, in order to reflect product differences between manufac~urers in 

the baseline V-axis washer, and to then aggregate the actual component costs to maintain 

confidentiality. Each clothes washer was analyzed and costed separately. The costs were then . 

averaged at the sub-assembly level and by category. Since different manufacturers have significantly 

differentdesigns, sub-assemblies are defined by function (i.e. suspension, tub and basket, transmission 

and 111otor, etc.), rather than by specific components. In Phase II, the differential costs will be 

calculated by estimating thecosts of the proprietary Y-axis design, and two commercially available 

H-axis designs. This will be done on an add and omit basis using each baseline V-axis machine. 

From this analysis, an aggregated differential cost will be generated. Once again, the results will be 

aggregated at the sub-assembly level and by category. In addition, each machine will be tested to 

obtain its energy consumption, according to DOE test procedure, Appendix J 1. 

The following is a list of the key characteristics describing the baseline clothes washers: 

Baseline Cloth~s Washer Characteristics: 

• 27" overall width 

• 3 wash/rin~e temperature settings 

• Unheated rinse 

• 3 Load Sizes Settings 

• 2 wash/~pin speeds 

• 8 or more wash cycles 

• Super capacity wash basket 

5.4.2 Manufacturing Assumptions 

Key assumptions used in the Phase I manufacturing cost assessment include: 

• An annual clothes washer production volume of 1.5 million units. Future analysis· will include 

separate baseline cost estimates for a smaller and larger volume manufacturer. 

• Capacity utilization at 95 percent. (Sensitivity to capacity utilizationwill be conducted in order 

to understand the impact on the baseline cost.) . 

• Investment for a ':greenfield" site, including all new equipment, tooling, installation, and building. 

• Full or semi-automation of the manufacturing facility fonriajor fabrication processes (painting, 

stamping, welding etc.). 

• Complete manual final assembly to reflect the need for flexibility as multiple models are built. 

It is understood that the cost of specific models (or individual manufacturers) will vary from 

5-2 

,• 
this baseline, depending on elements such as the products precise characteristics, actual 

manufacturing processes, and the produc~ mix in the factory (multiple clothes washer and dryer 

products). However, as this set of assumptions will remain constant for the Phase ~I analysis, the 

differential cost estimation methodology reduces the impact of plant-specific characteristics. 

5.4.3 Conventions for Manufacturing Cost Estimates 

The cost categories used to summarize the manufacturing costs developed in Phase I closely 

parallel the cost categories used in the AHAM data collection. Direct material, direct labor and 

overhead (including investment depreciation) comprise the fulfproduction costs. Other cost elements 

(Sales General & Administration, R&D, interest, etc.) are grouped under the non-production cost -

category. TQgether these costs make up the full cost of the product as shown in Figure 5.1. The 

manufacturing cost assessment will develop the full production cost estimates. Non-production costs 

will be added based on a top-down analysis of industry structure·. 

· Full Production Cost 1 Non.Production Cost 

1 Tax Reform Act of 1986, essentially, requires companies to measure cost of goods sold as the full production cost of the goods so 

Figure 5.1 Full production cost 

5-3 
5.5 APPROACH 

The manufacturing cost assessment methodology used is a detailed, component-focused, 

activity based technique for rigorously calculating the manufacturing cost of a product (direct 

materials, direct labor and some overhead costs). This methodology has been successfully applied 

by Arthur D. Little to products at various stages of development, from early R&D to production. 

Figure 5.2 shows the three major steps in generating a baseline manufacturing cost. 

Raw Fabrication Finished 

Materials Materials - Processes - Bill of - Assembly 

Materials Purchased Parts Processes - Figure 5.2 Manufacturing cost assessment approach 

5.5.1 Bill of Materials (BOM) 

The first step in the baseline cost assessment was the creation of a complete and structured 

billofmaterials from the physicaI tear down of two high volume V-axis clothes washers. To conduct 

the tear down, two Y-axis clothes washers were purchased and completelydismantled. Each part 

was then characterized according to weight, dimensions, material and quantity. A separate BOM for 

each clothes washer was created. 

The BOM incorporates all materials, components, and fasteners with estimates of raw material 

costs or purchased part costs, as appropriate. Assumptions on the sourcing of parts and in-house 

fabrication were based.on previous industry experience, recent infolJllation in trade publications and 

discussions with major clothes washer manufacturers. To improve our understanding of the 

industrys current manufacturing practices, interviews and plant visits were conducted at major 

clothes washer manufacturing facilities. These visits focused on observing and characterizing current 

manufacturing practices for the majority of the parts in the baseline clothes washers. For internally 

fabricated parts, manufacturing processes were identified on the BO:M for each component. 

Cost estimates for raw materials and purchased components were drawn from Arthur D. 

Littles manufacturing databases and supplemented with information obtained from manufacturer and 

supplier sources. Adjustments to the cost estimates for purchased parts and raw materials were 

made, as appropriate, to include price discounts typically available at the purchase volumes in 

question. Figure 5.3 is a section from the structured bill of materials of one baseline clothes washer. 

This section of the structured· bill of materials lists the part number (arbitrarily defined during 

. disassembly), description, material category, sourcing category (fabricated or vended), quantity, 

dimensions, weight, percent of scrap in processing, and estimated material cost. 

5-4 
UNI I I I llub & tsaSKet A11SemD1y I 

Part Flnol ou 1388.00 y OD or Welrbl (lb•) &crap M1ceri1I 

No. Dcscrlptin 1800.00 Coterory ' • Widlb Tltkou Leartb Steel Otber ".4 Cost, S 

1.01 "l>b "'°"" 1 22.5 0.09 22.5 5.520 J."'<1~1:l't':t;f: $3.31 

1.03 Water Level 11ox · r"""'c 1 2.1:. O.Oo:> 1.t:> 0.066 ,1,;<::.:?}i$,~'.S:~f. I0.04 

1.02 "l>b to S.spensoon Sear Ribber 1 2.6 0.3 1.22:> 0.066 ~"1~~Q~,%~,f. S0.03 

1.01 Bleach tube Ribber 1 0.6 o.w :>.2 0.036 ~i@.Ml~~li. S0.02 

1.30 Ba cket kEv 

1.201 BaSl<l>t I fnamld Seel I I 1 I 20.5 I 0.055 I 18 I 13.500 I I 15% I $4.13 

1 n1 Lint Collector I l'laSlic I I 1 I I I I I u ... ,~ l~i~.&.;:~t• I0.29 

1.211 Bani Balancing IU\gw/WBter In it I Plastic I I 1 I 20.5 I 0.09 I 2 I I 1.400 l'lil<ffi'.l~lll S0.84 

1.SOA Fabnc 01spenaer • lDp t"laStic 3.81 0.05 1.6 0.108 ~~ S0.05 

~stic 1 3 u.u:. "·" O.u74 -~~~~ 0.03 

1."'1C Fabnc Oispen•r· Bollom F'laslic 1 4 u.06 5 0.148 ~~1~£ 0.07 

1.51 Clutch Cover lastic a.z:> 0.11:> 0.9 0.030 g~;··-f.!t~ 0.02 

1.52 Clutch Cover Seal rubber 3.15 0.16 0.010 ~~ 0.01 

1.75 Lower Aoltator Any 

UICI Bottom Ao1tator 1:1eamg t'lastic l 2.~:. u.2s v.w1o 'Rl'!r.'MG Ml.U1 

1.59 Bottom Allitator Plastic 1 13.5 0.15 1a 1.816 ~ft=~;bf.: S0.82 

1.58 '-'>pelf Lower Agitator 11eanng Plastic 1 2.6 U.U:> 0.1 ·~ 0.016 ~ ... ~~~~~; I0.01 

1.57 '-'>per Agitator Plastic 1 3.75 0.1 11.:> 0.934 Wi<SW. S0.42 

1.15 Aaltator Clutch Aacv 1.'.~~~:-~~:f,~~L-? .. ~·f;;:.;~~J;~~~:.~M>1t.'1~1t,.~~lli~~"f~~·~~~J.v:~r·~'(~~:,::-1\=;~t~T.ft:?fa:~~l'f~~%-~~a:s.N!f-0f£:·ffa 

1.85 Aaltatar Clutch "'-•- 1.561 Clutch Body I rwmic I I 1 I 2.83 I o.08 I 2.26:. I I 0.0:>2 H~=~=/1{.!'i~I S0.02 

1.551 Ratchet Sops I I 4 I 0.4 I u.1:> I 0.9 I I 0.016 I }1:0.~!~'t~1 I0.01 

1.541Cluteh Bottom Cover I nasllC I I 1 I 1.75 I u.o:. I o.~ I I O.uua 1t~~~'.j?.;1 SO.OU 

Sub-Totals 22 13.50 10.79 S10.12 

Figure 5.3 Structu.red bill of materials 

5.5.2 Fabrication Processes 

After each detailed BOM was completed, the major manufacturing processes were identified. 

These processes are listed in Table 5.1. 

Table 5.1 · Major Manufacturing Processes 

m011~~4f~~~W'5%t~<eftSS~efF.abiiCatiODV~~~ft~j~~~!£4tZ9-ff.5~,~~~~&1.$£ifilS1fili{~ti~l!?ill~ft7!i~S%~SSeiiiblflJOlilihg,£fug~f:!~-t~; 

Fixturing Washing Adhesive Bonding 

Stamping/Pressing (Large, Medium, Small) Powder Coating Spot Welding 

Brake Forming Enameling Seam Welding 

Cut & Shearing Deburring Integral Fastener (i.e. clinching) 

Machining Other Fasteners 

Injection Molding (Large>1500 ton, Small<800 ton) Press Fitting 

Casting In5pection & Testing 

Information on equipment and tooling costs, typical processing cycle times, and materials used 

for fabrication were obtained from the ADL manufacturing databases. Equipment suppliers were 

contacted for further details concerning equipment capabilities and processing parameters (cycle 

times, scrap rates, etc.). The fabrication process cycle times were directly entered in the BOM and 

linked to the specific parts being fabricated. Each part had a processing time and labor cost assigned 

based on an overall labor rate. 

For this analysis $24.00 was used as the average fully burdened labor rate based on typical 

hourly wages and benefits for union employees. Sensitivity analysis was conducted on this important 

5-5 
parameter and is presented in Section 5. 7 .2. This parameter was investigated because labor rates can 

vary considerably, depending on geographic location, availability of skilled labor, and level of union 

represen~tion. 

Figure 5.4 contains an excerpt of the non-assemblyprocessing times (seconds per process) 

associated with each component listed on the left. This section of the model has all the processes · 

listed in Table 5.1, but for brevity a single page was presented in Figure 5.4. Figure 5.5 is from the 

equipment summary section, which includes total equipment and summarized labor requirements for 

each fabrication process. 

1.00 I I I tTub & Baskat Assembly 

Part Flnal &SI 1388.00 

No. Ducrlnlion 1800.00 

1.01 1Ub 

1.03 Water Level Bi:u 

1.02 lUb to S.spension Seal 

1.07 . Bleach tube 

1.49 Un_,. A111tator Assv 

1.50A Fabric Dispenser-Top 

1.SOE Fabric Dispenser - Cun 

1.SOC Fabric Disoenser-Bottom 

1.51 Clutch Cover --~~~~··¥~~~ -~~ttk~$ ~~~}.7.-~ ¥-~it~ ~~~r~~- ·~~~~ Mi..~"l'~ i.~~~: 

. 1.52 Clutch Cover Seal ~~ ~"'"'M"""° ~·~ · ~?ff.'i'MWi.J!! 'IJii$:!.G1%'"' 

1.75 Lower Aaltator ~ 

1.85 Aaitator Clutch Assv 

1.561 Clutch Ebdv 

1.551 Ratchet Sops 

1.541Clutch Ebttom Caver 

Sib-Totals . 62.0 4.0 10.0 30.0 3.0 

Figure 5.4 Fabrication process cycle time estimates 

Figure 5.5 Fabrication process equipment and labor requirements 

5-6 
In addition to the process time and _labor costs associated with-fabrication, labor times for 

assembly associated with each part is included. 

5.5.3 Assembly Processes 

In the final step of the cost a5sessment, assembly times and associated direct labor costs were 

estimated. Assembly cycle times were primarily derived from reversing the disassembly of the 

selected tear-down clothes washers. The assembly sequence was built into the structured BOM, 

culminating with final assembly, testing and packaging. 

Once the cost estimates for each machine was finalized, a detailed summary was prepared for ' 

relevant components, subassemblies and processes. 

The structured bill of materials and manufacturing process summaries provide detailed direct 

material, direct labor, indirect material, and investment costs. Indirect labor costs were estimated by 

subtracting the direct labor hours, determined from the bottoms-up analysis, from the current ave.rage 

total labor hours per unit (1.8 hours). The current average labor content was derived from a top 

down analysis of several household laundry equipment manufacturing plants and can be confirmed 

with data from the 1992 Census of Manufacturers [ 1]. Although the 1992 Census would indicate an 

average labor content in the household laundry equipment segment to be 2.1 labor-hours per unit, we 

believe 1.8 hours is more accurate, as a savings of 0.3 hours per unit, or approximately 14% for the 

industry over the past 6 years, could be expected. This generated an indirect labor-hour per unit, 

which was multiplied by the average labor rate ($24) in order to estimate the indirect labor cost. The 

average direct labor hour per unit was 1.3 hours; therefore the indirect labor content was calculated 

to be on average 0.5 labor hours per unit. The indirect labor is included in the overhead estimate, 

along with indirect materials, utilities, depreciation, maintenance, taxes, and insurance. The direct 

labor cost total is approximately $31.20 per unit, while the indirect labor costs are estimated to be 

$11.96 per unit at the fully burdened $24 per hour rate. · 

5.5.4 Baseline Manufacturing Assumptions 

Table 5.2 lists the key manufacturing assumptions used in the baseline manufacturing cost 

assessment. 

-S-7 
T a bl e 52 . K ey As sumptlons -.:.~ 

~ •' ,,... ... 

'WI·~~ ~·· 

~~Pt'-~~~~~~~ ~E~~mr~,.~.~~~~;r~w~11~ "" ' 

Work Days per Year 249 davs 

Avera2e Shifts Der davs 2.0 shifts• 

Runtime per Shift 8 hrs 

Lot Siz.e 1 workdav 

Eauipment Uptime 90% 

Assembly Downtime 20% 

Wa2es $24 per hour 

Capital Recovery Rate 0%/not used in analvsis 

Eauipment Depreciation Life 10.1 vears 

Total Plant Deoreciation (includes building and conveyor) 17.1 years 

Maintenance 4% ofEauipment Cost 

Utilities 1. 7% of Materials Cost 

Taxes 0.9% ofFactory Cost 

Insurance 0.8% ofFactorv Cost 

Capacitv Utilization 95% 

*Fabrication, 2.5 Shifts, Assembly 1.0 Shifts 

These financial and operational assumptions are based on general industry practices, 

determined from manufacturer interviews, 1992 U.S. census data, and internal Arthur D. Little 

sources. 

5.6 . RESULTS 

Table 5.3 presents a summary of the full production cost, built up from material, direct labor 

. artd overhead costs, for the baseline V-axis clothes washer. These costs have been aggregated based 

on the two machines ·that were dismantled. 

Table 5.3 Cost Components for Baseline V-Axis Clothes Was~er ($/Unit) 

~~;Qc?JX~!suii:Weffii>fi~~~~~¢~fJi1'.YM:aie'fiil1it@ti:Dite~\t.a6or.".:7rf0rov.effi'~ll'<f;ii;(i;ts,~iFUiiffirb"'dlfc'.ti6'tiz;w5f11tJ~~ 

Tub & Basket $17.64 $8.56 $11.99 $38.18 

Transmission/Motor $35.78 $7.38 $15.09 $58.26 

Suspension $5.57 $2.52 $3.99 $12.08 

Pumps, Controls, and Valves $33.13 $3.38 $3.67 $40.18 

Cabinet $18.10 $5.50 $13.85 $37.45 

Misc. & Final Assembly $10.75 $3.90 $1.77 . $16.42 

~~1.'*i#r1Xcid,~~'fr'Jl:~~1#~".\rr'f¥f's"'t2if9·:;:r:~~~f0¥"'~1·'!:?<;~..J\.%~5"'0~1!;'6°~f@t~6iFW&*-~2·"0"2<,'i'~,.<.fr~~ ~gJ,,'-l[)t8 ~~:.:.x.»~ ...... ~<;.:;m. 4'(:W:;.:~;<.<;;$u~..>.~~ ....... ..,M'·" , • ':.:f~.-;-:"9~il~~ .... ~:ww.v;.;~ :~--~-... ~;$7J:g[_')_~ . "1 1.J~i;,:;,,..,.~~~!'l-,.·-.v'=""'",..,....;,.~-.;J , ~{,,.,.,.~ .. :<¥ ... ~"-'>«:{,.'-::.~·-··~ t2 

A detailed breakdown of the overhead costs and the key assumptions for each cost category 

are presented in Table 5.4. These costs are estimated based on the bottoms up cost analysis and 

typical values for the appliance manufacturing industry. Taxes are not directly rela~ed to a product 

design, therefore no change between the baseline and any higher efficiency washers is anticipated. 

Also, maintenance, utilities, and insurance are primarily related to equipment quantity and cost of 

investment and therefore are indirectly driven by product designs. We have included these costs to ~ 

estimate the production cost of the baseline but anticipate that variation with future higher efficiency 

designs will have a secondary effect on differential cost. 

5-8 
Table 5.4 Overhead Cost of Baseline Clothes Washer 

~1IkWI~~i€0S11Ci~goeyr#?Sit~~~r~~~!~~TCOset~'l~~r£~i~i~1~~3~;@h~t~~118s~\1~::t:\~iBi0~2if~W~~ifJY~ 

Indirect Labor $11.96 Teardown analysis 

Indirect Materials' $17.45 Teardown analysis 

Maintenance $3.25 4.0% of total yearly capital investment 

Utilities $2.35 1.7% of material costs 

Depreciation $11.90 Teardown analysis 

Taxes ·$1.82 0.9% of full production cost 

Insurance $1.62 0.8% of full production cost 

~oYir<b-~%1111*~~~~-~~~~~@J!lr~~~Yk~:,<4~fffe1:l:~~11~T~1tE~itliii7~1~~Ji~~~ 

Although the overhead costs are slightly higher in this analysis than the top-down industry 

analysis, much of this difference cari be attributed to the significant investment in a greenfield site. 

The allocation of direct and indirect labor can 3Iso vary with any single companys individual 

practices.· Fixed investments in equipment and tooling have been estimated based on the requirements 

derived from the component cycle times in production volumes. Production volumes, in conjunction 

with equipment process times, will determine the numberof pieces of equipment necessary and the 

plant investment costs. For the sizingofthe plant and the number of pieces of equipment, we have 

assumed an up-time of 90%. Table 5 .5 shows the investment for a plant capacity of 1.5 million units 

per year. 

Table 5.5 Fixed Costs millions for Baseline V-Axis Cloth~s Washer 

.. ,s11i>assemDJiesTf!itftt:;~'Wikl&.~J.:~~r<E9·wp1ii'ent'-&~t<1oling1\I~ui.iding:a.1111tco11veiot:":i\Jr"tL1ltr;toi8iiDvesimenf~';'.~' 

Tub & Basket . $36.58 $34.85 $71.43 

Transmission/Motor $37.82 $36.04 $73.86 

Suspension $7.35 $7.01 $14.36 

Pumps, Controls, and Valves $3.56 $3.39 $6.96 

Cabinet $30.16 28.74 $58.90 

Misc. & Final Assembly $0.42 0.40 $0.82 19".,.i.'""'"'c,;.y&c!tltttl··"·'·"*",*'•V*"""*-M"'~,.,qw.w,._1,,zt~·:i,c:ts9"ih~lli'""'"~i,£.e"'JiW,·,$"''l'O' v,,34'flW·'1··li'"•"'·"·"·'!.~!'t!~~~2""'L'1;~illi···~:i,"i!l;:rr.'Cim ~~'Q1.cu:./~.:.f~h~~Vit~~lnfuli,~t_Wf4~*llfa.ti',i.~~.;; · .~. on&~G~Wi~~-NJltJ . . 1~ ... ~~- ~ on.tl~<&<iillt<~j.p· ~~x~z:m _on~t:T~ 

The building investment currently assumes 1,000,000 square feet at $100/sq. ft. and 10. 7 miles 

of conveyor at an average price $185/foot. A multiplier of 1.6 (additional 40% for installation and 

20% for auxiliary equipment) was used on the purchased equipment costs to calculate the total 

equipment investment. Because of the high volume production,hard tooling is included in indirect 

material costs and is amortized on a yearly basis. We have assumed that the useful life of the 

equipment and building on average is 17.l years (which agrees with the 1992 Census of· 

Manufactureri; [ 1]) and we used a simple straight line depreciation assumption to calculate the yearly 

amortized per unit cost. These assumptions can vary greatly, and have a significant effect on the total 

investmentand the depreciation element of the overhead costs. Because of this, these assumptions 

will be the subject of further sensitivity analysis. Also we have not included the cost of capital in the 

products full production cost, since this cost is an interest which is considered in the estimate of the 

non-production cost. For future analysis, the methodology can apply a capital recovery factor (CRF) 

to the total investment in order to estimate the yearly cost of capital, but at this time we have not 

included it in the baseline units full production cost. 

The full cost of the product, which includes SG&A, R&D, ~d interest costs (Figure 5.1 ), will 

5-9 
be arrived at using the industry cost structure developed in Section 3 .10 of this document. This 

manufacturing cost assessment will estimate the direct materials, direct labor and overhead for a 

baseline unit which, according to the industry cost structure, represents approximately 77% of the 

full cost of a product. 

5.7 VARIABILITY AND UNCERTAINTY IN MANUFACTURING COST 

Since the current cost assessment represents only a fraction of the baseline clothes washer 

products available, and the. estimates have inherent variability and uncertainty, we have conducted 

extensive sensitivity and cost simulations to understand the impact of this variability and uncertainty 

on the baseline manufacturing cost. This analysis will provide us with a greater understanding of the · 

range of probable costs, and to identify key cost elements may require further critical review and 

investigation. We use primarily two methods to analyze the variability and uncertainty in the 

manufacturing cost assessment: 

• Sensitivityanalysis is used to qtiantify the impact of variability and uncertainty for any one specific 

parameter. 

• To understand the interaction and impact of multiple parameters we use Monte Carlo simulations. 

This analytical technique allows us to input a range of data with probability distributions and 

generate an estimated range for the baseline manufacturing cost with a quantifiable level of 

certainty. 

5. 7 .1 Key Parameters 

The physical teardown and activity based cost estimating conducted on two actual clothes 

washers removes much of the uncertainty about materials, components, and manufacturing processes. 

Many of the manufacturing processes, however, carry some variability based on individual 

manufacturers and product differentiation. This results i_n some variation in the total capital 

investment, the utilization of that capital, maintenance:;osts, and equipment uptime. Also average 

labor rates for different manufacturers can vary, so sensitivity analysis to determine the impact on 

costs is warranted. 

In addition to the variability in for example, input parameters, a certain degree of uncertainty 

accompanies the estimation of manufactμring process capabilities and equipment investment 

estimates. The impact of this uncertainty is quantified by using sensitivity analysis. 

5. 7 .2 Sensitivity Analysis 

We have conducted numerous sensitivity analyses and have selected the most appropriate and 

5-10 
significantparameters that will impact full production cost. The sensitivity results are presented for 

an aggregated baseline Y-axis machine. 

Labor Rate 

The 1992 Census data for household laoodry equipment manufacturers indicates an average 

fully burdened labor rate of$18 per hour while some uriion wages could be as high as $28 per hour. 

Sensitivity analysis was conducted on this parameter and the results are shown in Figure 5.6 . 

... 

~ 1215.00 

In 

3 .. 12'0.00 

Cll 

CL 

-:g aas.oo 

(,) 

c 

~ UDD.00 

u 

~ 

'C 

e StOS.00 

Cl. 

0 

"' ::I Stl0.00 

$115.00 

" .. .. .. Labor Rate (USO/hr) 

·Figure 5.6 Sensitivity analysis on labor rate 

. 21 

The manufacturing cost model indicates that a $17 to $28 labor rate variation would result 

In approximately a $20 differential in full production cost. 

Capacity Utilization (Plant Output) 

One important source of variability is the utilization of a plants capacity. Sensitivity analysis 

on capacity utilization is presented in Figure 5.7. 

5-11 
215.00 

~ 210.00 

i 

i 

i 

u 205 00 I . 

l 

~ 200.00 :> 

1115.00 

180.00 

I00.000 1,000,000 1.100.000 1.200,000 1,300,000 1.400,000 

Actual WHhera Bullt per Year In 1 .IMM p.a. Faclllty 

Figure 5. 7 Sensitivity analysis on actual plant output 

1.500.000 

If the greenfield baseline clothes washer manufacturing plant slipped to 60% of capacity this 

would result in an increase of approximately $10 per unit. 

Total Investment 

There is a considerable amount of uncertainty in estimating the equipment requirements of 

the total plant and the total cost. Sensitivity analysis was conducted on this parameter. In the 

sensitivityanalysis total investment was varied considerably from -50% to+ 150% of the aggregated 

baseline cost estimate of $226 million. The results are presented in Figure 5.8. 

$220.00 

S215.00 

-! $210.00 

i 

i. 

i 

~ 1205.00 

f 

! 

f: 1200.00 ::> 

1195.00 

50% 10% 10% 80% '°"' 100% 110% 120% 130% 140% 150% 

l•'ff•tnwnt F•ctor (PHcent of E•tlm•t•d lnvestm•nt) 

Figure 5.8 Sensitivity analysis on total investment cost 

5-12 
The x-axis represents a percentag~ of the baseline cost of approximately $226 million. It is 

apparent from the sensitivity analysis that this large variation in equipment cost could have a 

significant impact ($15 out of $202) on the full production cost. 

Equipment Average Depreciation Life 

The depreciation life of a piece of manufacturing equipment has been investigated, and there 

still remains a significant degree of variability between manufacturers. To understand the impact of 

this variability, sensitivity analysis was conducted and the results are presented in Figure 5.9 .. 

$220.00 

1215.00 

-! 1210.0D 

' l 

i 

': $205.00 

R 

I 

L 

i $200.00 ::> 

1195.00 

1100.00 

5014 - 10% - -· 100% 110% 120% 130% 1'0% 150% 

A""V- Oeprec .. tfon Ltfe Percent of .. ttmet.) 

Figure 5.9 Sensitivity analysis on equipment depreciation life 

We assumed that the average depreciation life on the equipment could vary from 7 to 15 

years, which translates into a cost differential of approximately $11 per unit. 

Maintenance 

Maintenance cost values do not change dramatically in the appliance industry but the 

uncertainty in.the estimates warranted an investigation of its significance (see Figure 5.10). 

5-13 
l205.00 

s:zoc.oo 

.i 

i i s=.oo 

J 

I J ROZ.00 .. 

2 

.. 

SZOl.00 

szao.ao 2.5 ... •.SY. 

..ln.teuttee u Perc.ntage of e:qulpaaent Depreciation 

Figure 5.10. Sensitivity analysis on maintenance 

5.0% 

Our cost assessment would indicate that within the range of typical maintenance costs (2-6% 

of equipment investment) there is not a significant impact on full production cost (approximately a 

range of$2). 

5.7.3 Monte Carlo Simulation 

To evaluate each of these plus several other parameters simultaneously and to understand the 

cumulative effects of variability and uncertainty we conducted a Monte Carlo simulation. This 

process generates random inputs within a prescribed distribution which then completes numerous 

iterations (10,000) to quantify the range of possible outcomes and the probability of each occurrence. 

For the simulation we made the fol.lowing assumptions: 

Labor Rate Uniform $17 to $28 per hour 

Plant Output Triangular 900,000 to 1.5 million 1.425 million 

Equipment Depreciation Life Uniform 7 to 15 

Total Investment Multiplier Triangular 50%to 150% 100% 

Maintenance Uniform 2.5% to 5.5% 

Equipment Uptime · Triangular 70%to95% 90% 

Utilities Triangular 1%to 3.5% 1.7% 

By randomly generating values in the key parameters of Table 5.6, a distribution of full 

production cost was generated as shown in Figure 5 .11. Based on this analysis, Figure 5 .11 shows 

5-14 
-- ------- ----------------------------- --

that the variation in key parameters mentioned in Table 5 .6 for. washer manufacturing results in an 

aggregated baseline clothes washer cost between $188 and $217 with 95% certainty. This range 

represents a variation of+ or - $16 per unit (8.5% ). This analysis can be repeated for different ranges 

in the key parameters, alternative probability distribution functions and for additional parameters if . 

warranted. · · 

Forecast: Aggregated Baseline Cost 

10,000 Trials Frequency Chart 

.023 

.017 

~ 

.c - .012 

.c = 

c 

... .006 c. 

.000 

~ 

$180.00 $191.25 $202.50 

($/washer) 

$213.75 $225.00 

Between $188 and $217 with 95% certainty 

Figure 5.11 Monte Carlo simulation of aggregated baseline cost 

5.8 CONCLUSION AND PHASE II PROGRAM PLAN 

20 Outliers 

232 

- 174 

..,, 

... 

n - 116 .c 

n = 

= - 58 ~ 

- 0 

Arthur D. Little has completed a manufacturing cost assessment of two Y-axis baseline 

clothes washers and estimates the aggregated full production cost at $202.57. Sensitivity analysis 

and Monte Carlo simulations would indicate the cost estimates would fall between $188 and 217 with 

95% certainty. This estimate is based on the key assumptions outlined in this Phase I report, 

interviews and feedback from stakeholders and Arthur D. Littles industry knowledge. 

Based on additional feedback from any stakeholders, Arthur D. Little can conduct alternative 

analyses on the baseline clothes washer, butat this time, we have initiated Phase II to estimate the 

differential costs of several higher efficiency designs relative to the aggregated baseline cost 

developed in Phase I. 

5-15 
REFERENCES 

I. U:S. Department of Commerce, Bureau of Census. 1995. 1992 Census of Manufacturers, 

MC92-I-36B. Washington, D.C. 

5-16 
CHAPTER 6: MARK-UPS FOR PRICE DETERMINATION 

6.1 OVERVIEW 

The following figure illustrates the representative prices and.mark-ups for a typical V-axis 

cloth~s washer being sold in the U.S. marketplace: 

Manufacturing Manufacturer 

Cost 1------'----~i Price t------''---...-' 

$211.50 $285.90 

Retail 

Price 

$400.25 

Figure 6.1 Average costs and mark-ups for the baseline clothes washer 

Consumer 

Price 

$421.10 

To perform Pre-ANOPR analysis, the Department proposes a 2-step mark-up approach 

where: (a) the manufacturing costs (i.e., full production costs) are marked up to the manufacturer 

price 'ilsing a manufacturer mark-up; and (b) the manufacturer price is marked up by a retail mark-up 

to arrive at the retail price. The price paid by the consumer includes the sales tax in addition to the 

retail price. This sales tax is accounted for by using a sales tax mark-up over the retaiprice of the 

clothes washer. 

The Department proposes a fixed retail mark-up of 1.40, and a fixed mark-up of 1.052 to 

cover the sales tax. The manufacturer mark-up over full production costs will be bound by a 

maximum value of 1.3 5 which maintains in~ustry (manufacturer) cost structure, and a minimum value 

of 1.00 which represents a pass-through of full production costs including depreciation of new capital 

but no recuperation of non-production costs. 

Based on the information above, the total mark-up from full production costs to consumer 

1 In this description a typical unit refers to a high production volume model with high market share (industry 

average); not necessarily a minimally complying unit under NAECA, or a unit with an MEF value of0.817 (AHAM 

baseline). Currently no data exists that would allow the Departmecyt to associate sales information on the basis of the 

MEF descriptor to be used in the present rulemaking. In the absence of specific price information for a baseline (0.817 

MEF) clothes washer, tvpical values will be used as default values for the baseline model Since the life-cycle-cost 

(LCC) analysis uses differential costs only, the absolute vah.~e of the baseline unit cosf has no impact on LCC and 

payback analysis. The absolute value of the baseline unit cost is important, however, if price survey information is used 

to compare prices derived from a mark-up over cost with prices observed in the market. 

6-1 
price varies from a minimum of 1.473 to a maximum of 1.990. The Department proposes to 

characterize the total mark-up using a triangular distribution with the above stated minimum and 

maximum values and a most likely value which is the average of the two extremes. 

This total mark-up distribution will be used by the Department as a means of estimating future 

consumer prices of clothes washers. 

Section 6.2 of this document presents a discussion on retail mark-ups, presenting current and 

historicalretail mark-ups for clothes washers. Section 6.3 presents a brief discussion on sales tax and· 

its effect on consumer pric~. · Section 6.4 outlines the Departments methodology for estimating 

manufacturer mark-ups. 

6.2 RETAIL MARK-UPS 

The.objective of this section is to estimate the manufacturer price-to-retail price mark-up for 

clothes washers from the available sources of price information. 

6.2.1 Background 

One measure of the effect of appliance efficiency standards on the consumers is the change 

(increase) in the purchase price of the appliance. A more stringent standard usually results . iri 

increased manufacturing costs to the manufacturers. These increased costs (and associated margins) 

are passed-through from the manufacturers to the retailers and eventually to the consumer in the form 

of higher purchase prices . 

.. 

It is important to identify what proportion of the standards-induced costs will be reflected in 

retail prices, as this mechanism is important for the rulemaking in two ways: a lower margin over 

incremental manufacturing costs passed-through justifies a less stringent standard from a 

manufacturer impact standpoint; and has the opposite effect oflowering consumer LCC that justifies 

a more stringent standard from a consumer perspective. 

Retailers .buy appliances (clothes washers in this case) from manufacturers and sell them to 

consumers at higher prices to cover their operation costs and profit margins. The amount by which 

the manufacturer price (price paid by the retailer to the manufacturer) is marked up by the retailer to 

arrive at the retail price is referred to as the retail mark-up. 

In this section we estimate the manufacturer price-to-retail price mark-up for clothes washers 

(i.e., the retail mark-up) based on available historical market information. 

Sources of information for the retail price of clothes washers include: 

6-2 
(1) Consumer Expenditure Surveys, conducted by the Bureau of Labor Statistics in conjunction 

with the Bureau of Census [1]. . 

(2) Dealerscope Merchandisings Annual Statistical Surveys [2] 

(3) ELCAP price database [3] 

Sources of informati.on for the manufacturer price of clothes washers include: 

{1) Current Industrial Reports, U.S. Department of Commerce [4] 

(2) Association of Home Appliance Manufacturerss Fact Book, 1997 [5] 

Sections 6.2.2 and 6.2.3 present the retail and manufacturer price data, respectively, along 

with short descriptions of the data collection procedures and the characteristics of each of these data 

sets. These data sets are then used to arrive at the retail mark-ups which are reported and compared 

in Section 6.2.4. Retail prices from each data source will be divided by manufacturer prices from 

each of two sources for manufacturer price, thereby resulting m six different sets of estimates for 

retail mark-ups. Section 6.2.5 presents an analysis of finanCial statements (SEC 10-K reports) of a 

few appliance retailers to get an understanding of gross margins in the appliance retail business. 

6.2.2 Retail Price Information 

6.2.2.1 Consumer Expenditure Surveys (CES) 

Consumer Expenditure Surveys (CES) provide information on the buying habits of American 

consumers, including data on their expenditures, income, and consumer unit (families and single 

consumers) characteristics. The survey consists of two components: a quarterly Interview Survey 

and a weekly Diary Survey, each with its own questionnaire and sample. Data collection is carried 

· out by the Bureau of Census under contract with the Bureau of Labor Statistics (BLS). 

The data are collected in independent Quarterly Interview and· Weekly Diary surveys of 

approximately 5,000 sample households. Each survey has its own independent sample, and each 

collects data on household income and socioeconomic chaiacteristics. The Interview survey includes 

monthly out-of-pocket expenditures, such as housing, apparel, transportation, major home appliances, 

health care, insurance, and entertainment. The Diary survey includes weekly expenditures of 

frequently purchased items, such as food and beverages, .tobacco, personal care products, and nonprescription drugs and supplies. 

Table 6.1 shows the unweighted average purchase price of clothes washers as reported in the 

·consumer expenditure surveys. These values represent the price to consumers net of a trade-in and 

represent purchases by individuals. These values are reported for the period of time 1986 through 

1994. Table 6.1 also shows the retail price of these washers. The retail prices were estimated by 

subtracting the sales tax from the purchase price paid by consumers (based on sales tax rates and 

shipments of clothes washers in all the 5_0 states, a shipment weighted average sales tax of 5.2% was 

6-3 
calculated). 

Table 6.1 

Retail Price 413.56 

6.2.2.2 Dealerscope Merchandising Statistical and Marketing Reports 

Dealerscope is the monthly trade publication for retailers of consumer electronics, major 

appliances, and computers. The magazine covers product news, industry trends, and new 

technologies, along with reports on the marketing, branding, merchandising, and retail strategies of 

vendors and dealers: 

Dealerscopereports contain one-year and five-year unit shipments industry-Wide and the retail 

value of these shipments. In these reports the data is based on information provided by leading 

manufacturers. Companies are asked to indicate how much the overall total shipments and average 

retail prices have changed for the industry, and for the product category. Manufacturers are not 

asked for the annual changes in their own companies s shipments or in retail prices of their own brand 

of merchandise~ 

Table 6.2 provides the retail price of clothes washers estimated from the statistical reviews. 

The estimated retail value of clothes washers shipped from factories is divided by the clothes washer 

units shipped industry wide to arrive at the per unit retail price. 

6.2.2.3 ELCAP Clothes Washer Sales Information 

The ELCAP2 service collects sales data {retail prices) on clothe~ashers from a number of 

retailers- consumer electronics -superstores, appliance stores, ·and mass merchants. Table 6.3 

presents shipment weighted retail price for. the years 1994 through 1997. (The 1997 retail price is 

based on sales data through August 1997.) · 

2 ELCAP service was discontinued at the end of 1997. Clothes washer retail sales are currently being tracked 

by INTELLECT ASW. - . 

6-4 
•. 

Table 6.3 Shipment Weighted Retail Prices of Clothes Washers from 1994 

throu h 1997 based on ELCAP Retail Sales Data 

$! !J.&1- ,. at~r ·· 

6.2.3 Manufacturer Price Information 

6.2.3.1 Current Industrial Reports (CIR) 

The CIR program has been providing monthly, quarterly, and annual measures of industrial 

activity for many years. The primary objective of the CIR program is to produce timely, accurate 

data on production and shipme~ts of selected products. The data are used to satisfy economic policy 

needs and for market analysis, forecasting, and decision making in the private sector. The CIRs 

include statistics for the United States on the quantity and value of shipments along with comparative 

data on exports, imports, and domestic output. 

The CIR program uses ~ unified data collection, processing, and publication system. The 

census bureau updates the survey panels for most reports annually and reconciles the estimates to the 

broader-based annual survey of manufactures and the census of manufactures. The census of 

manufactures provides a complete list of all producers of the products covered by the CIR program 

and serves as the primary source for CIR sampling. The CIR program includes a group of mandatory 

and voluntary surveys. Typically, the monthly and quarterly surveys are conducted on a voluntary 

basis. Those companies that choose not to respond to the voluntary surveys are required to submit 

a mandatory annual counterpart corresponding to the more frequent survey. 

The figures on quantity and value of shipments represent physical shipments of all products 

sold, transferred to other establishments of the same company or shipped on consignment, whether 

for domestic or export sale. The value represents the net sales price, f.o.b. plant, to the customer or 

branch to which the products are shipped, net of discounts, allowances, freight charges, and returns. 

Shipments to a companys own branches are assigned the same value as comparable appropriate 

allocation of company overhead and profit. Products bought and resold without further manufacture 

are excluded. 

Table 6.4 provides the estimated manufacturer price of clothes washers. The value of the 

clothes washers shipped from factories is divided by the quantity of shipments to arrive at the per unit 

manufacturer price. 

Table 6.4 Manufacturer Prices of Clothes Washers from 1986 through 1996 

estimated from Current Industrial Reports . 

6-5 
6.2.3.2 AHAM Fact Book 

The AHAM Fact Book is a comprehensive collection of facts defining the U.S. major home 

appliance industry, its market and its products. The Association lists both: 

• the total manufacturers' sales of clothes washers (excludes freight charges or extended warranty 

costs), and 

• the industry unit shipments (domestic and exports) of clothes washers. 

The above stated information can be used to arrive at an average per-unit manufacturer price 

for clothes washers, presented in Table 6.5. 

, 6.2.4 Retail Mark-ups from Manufacturer and Retail Price Information I . 

Table 6.6 shows average retail mark-ups over the 9-year period, 1986 through 1994, for 

clothes washers sold in the U.S. The retail mark-ups in this table are obtained using manufacturer 

and retail price information obtained from governmental sources- retail price information from the 

Consumer Expenditure Surveys conducted by the Bureau of Labor Statistics; and manufacturer price 

- information from the Current Industrial Reports prepared by the Bureau of Census. 

6-6 
Table 6.6 Retail Mark-ups for Clothes Washers from 1986 through 1994 using 

Retail Prices from the Consumer Expenditure Surveys, and 

M anu f: a:c t urer p· rices ti rom th e C urren tld n us t•IR r1a epo rts 

Year Retail Manuf. Retail 

Price Price Markup 

(CES) (CIR) 

1986 410.6 272.6 1.51 

1987 410.9 266.9 1.54 

1988 416.0 262.0 1.59 

1989 420.4 277.3 1.52 

1990 422.3 278.1 1.52 

1991 422.1 275.9 1.53 

1992 413.7 277.6 1.49 

1993 398.7 283.8 1.40 

1994 413.6 279.0 1.48 

Table 6.7 gives the retail mark-ups estimated using price information from different data 

sources explained earlier in the text, and the same information is presented graphically in Figure 6.2. 

For the period 1986 through 1996, the average retail mark-up varies from a minimum of 1.33 (in 

1995) to a maximtim of 1.62 (in 1988). 

Table 6. 7 Retail Mark-u s estimated from Different Data Sources 

Year Retail Retail Retail Retail Retail Retail Average 

Mark-up Mark-up Mark-up Mark-up Mark-up Mark-up Retail 

(Dealerscope) (Dealerscope) (CES) (CES) (ELCAP) (ELCAP) Mark-up 

CIR AHA CIR A CIR 

1986 1.65 1.61 1.55 1.51 1.58 

1987 1.67 1.65 1.56 1.54 1.61 

1988 1.64 1.68 1.55 1.59 1.62 

1989 1.46 1.46 1.51 1.52 1.49 

1990 1.43 1.47 1.47 1.52 1.47 

1991 1.44 1.50 1.47 1.53 1.49 

1992 1.31 1.38 1.42 1.49 1.40 

1993 1.31 1.35 1.36 1.40 1.36 

1994 1.32 1.41 1.38 1.48 1.31 1.41 1.39 

1995 1.30 1.44 1.23 1.36 1.33 

1996 1.36 1.41 1.39 

6-7 
1.8 

1.7 

~.6 

1.5 

1.4 

1.3 

1.2 

1.1 

1.0 

1986 1988 1990 1992 

Year 

-._. Dealerscope-AHAM 

--+- Dealerscope-CIR 

-CES-AHAM 

-*-CES-CIR 

-e-ELCAP-AHAM 

-+-ELCAP-CIR 

1994 1996 

Figure 6.2 Retail mark-ups estimated from different data sources 

6.2.5 Retail Mark-ups estimated from SECs Financial Statements 

Information relating to the gross margins (or gross profit) of appliance retailers can be 

obtained from their SEC 10-K financial statements. Table 6.8 presents gross margins for some 

appliance retail companies (Best Buy, Circuit City Stores, Lowes, and Sun T.V. and Appliances). 

Most of these retailers sell electronics and home office equipment along with household appliances. 

The margins in Table 6.8 represent the average of the margins that retailers obtained from sales in all 

the above mentioned categories. On average, appliance sales are more profitable (i.e., obtain higher 

margins) than sales of personal computers; T.V.s and other electronics due to the intensely 

competitive and highly promotional pricing environment in the electronics retail enviro~enf. As 

a consequence, some retailers are trying to further increase their margins by expanding the appliance 

part of their business and by putting greater emphasis on the sale of extended service contracts.4 

3 Source: Standard and Poors Stock Reports on Circuit City Stores and Best Buy. 

4 Source: Standard and Poors Stock Reports on Best Buy. 

6-8 
Table 6.8 Gross Margins for Retailers of Electronics, Home Office Equipment · 

and Ma· or A liances ~~ 

26.3% 15.0 

16.2% 9.0 1.19 

25.7% 17.5 1.35 

Typically in the retail industry, gross profits, as reported in SEC 10-K statements, are obtained 

by subtracting the 'bost of sales" from the ''net sales." · Dividing gross profit by the net sales gives 

the gross margin. Gross margin represents the mark-up that retailers receive over the total of the: 

• price that the retailers pay to the manufacturers for buying the goods, 

• costs involved in the process of buying the goods from manufacturers (these might iriclude some 

transportation costs, administration and labor costs), and 

• costs of maintaining and operating warehouses where the goods.are stocked after they are bought 

from manufacturers. 

From the above description of gross margins, it can be seen that the retail mark-ups (defined 

earlierin this write-up as the ratio of retail price to manufacturer price) will be, in general, higher than 

· what the gross margins indicate. This is because in addition to profits and SG&A, the retail mark-ups 

will also have to cover the costs incurred by retailers for buying and warehousing activities. The 

buying and warehousing costs as a percentage of the net sales or revenues are not available. 

6.3 SALES TAX 

Consumers pay sales tax in addition to the retail price at the time of purchasing an appliance. 

This additional margin for sales tax needs to be reflected in the mark-up from manufacturer price to 

consumer price. 

For the year 1995, the shipment weighted sales tax on clothes washers was calculated to be 

5.2%. This information is based on individual sales tax rates for 50 states and weighted by the clothes 

washer shipments (source: AHAM) in each of these states. Hence, a mark-up of 1.052 will be used 

to cover the sales tax. 

In the year 1994, for which retail price is available from all the three sources, namely, CES 

[l), Dealerscope [2], and ELCAP [3], the average clothes washer retail priceis $400.25. Looking 

. at Tables 6.2 and 6.3, there seems to be no discernible change in clothes washer retail prices since 

1994~ Hence (in the absence of price information specific to clothes washers with an MEF value of 

s Retail mark-up= 100/(100-Gross margin). Mark-up for clothes washers (appliances) should be higher given the 

product mix. 

6-9 
0.817), the Department proposes to use $400.25 as the baseline retail price for the clothes washer 

rulemakinganalysis. Adding a 5.2% sales tax would result in a baseline consumer price o:f$421.10. 

Looking at Table 6.7, the retail mark-up, on average, in the past three years has been around 

1. 40. Adding a sales tax of 5 .2% on top of the retail price would result in a manufacturer price-toconsumer price mark-up of 1.473. This mark-up of 1.473 is in agreement with the mark-up of 1.475 

used in the 1991 report to AHAM [6] e.ntitled 'Financial Impact of DOE Top Loading Horizontal 

Axis Standards on U.S. Washing Machine Manufacturers." Hence; the Department of Energy 

proposes using a retail mark-up ofl.40, and a sales tax mark-up of 1.052 to arrive at the consumer 

price for the clothes washer rulemaking analysis. 

6.4 MANUFACTURERMARK-UPS 

The objective of this section is to outline a methodology for estimating the mark-up from 

manufacturing cost to manufacturer price for the clothes washer rulemaking analysis. 

6.4.1 Background 

In order to meet the new appliance efficiency standards, manufacturers may have to introduce 

· design changes in their product lines. These design changes usually result in increased manufacturing 

costs. The increased manufacturing costs are 'passed-through" from manufacturers to retailers and 

eventually to consumers in the form of higher purchase prices. 

In this section the manufacturing cost to.manufacturer price mark-up is estimated. This is 

referred to as the manufacturer mark-up. The manufacturer mark-up has an important bearing on the 

manufacturerss profitability, as a higher mark-up suggests that manufacturers can pass-through the . 

variable costs and some of the product conversion.costs (the fixed costs) to retailers and eventually 

tci consumers; whereas a lower mark-up itμplies that manufacturers will not be able to fully recover 

the necessary investment in plant and equipment. 

6.4.2 Manufacturer Price and Mark-up for the Baseline Standard Clothes Washer 

The average retail price of a clothes washer in 1994 was $400.25 (as discussed in Section 

6.3). Using a retail mark-up of 1.40 (also discussed in Section 6.3), the baseline manufacturer price 

would be $285.90. 

Manufacturing cost estimates for the baseline clothes washer can be estimated from the 

baseline manufacturer price ($285 .90) and the industry cost structure developed in Section 3 .10. This 

is shown in Table 6.9. The break-upof.the price is based on the assumption that in order to earn a 

satisfactory return, revenues from the sale of goods (and hence the manufacturer price) must be large 

6-10 
enough both (I) to recover costs and (2) to earn a profit that provides a satisfactory return on 

investment. 

Table 6.9 Manufacturing Cost of a BaseUne Clothes Washer estimated 

from the Industry Cost Structure 

~iltrftmttii'1tit" .. ~ ~tf$ii; 

Material f48. 7 

Labor 29.3 

Overhead 33.5 

The full production cost of a baseline clothes washer is estimated to be$211.50. The full 

production cost (also called the inventory cost) and the non-production cost together constitute the 

full cost of a product. Full production cost is the sum of the direct material cost, the direct labor 

cost, and the overhead cost which includes depreciation (-3.5% ofrevenues). The non-production 

costs include selling costs, general and administrative costs, research and development costs, and 

interest costs. The full cost of a product and its components are shown in Chapter 5, Figure 5.1. 

Baseline manufacturer mark-up is defined as the increase in price over the full production cost 

of a baseline clothes washer, and is calculated as: 

Baseline Manufacturer Mark-up = Manufacturer Price 

Full Production Cost 

A baseline mark-up is applied over the full production cost to arrive at the manufacturer price 

at which the firm can recover all costs, both production and non-production, and earn a profit. In 

general, the manufacturer mark-up should ensure that the manufacturer price of the product is high 

enough (1) to recover the full cost of the product and (2) to yield a satisfactory profit. Such a price 

is called a normal price. 6 

A baseline manufacturer mark-up ofl.35 over full production cost is calculated from the cost . 

information in Table 6.9. 

6 Some people question whether full costs are widely used as a basis for pricing. In a survey oflarge industrial 

companies, 85% reported that they do use full cost pricing: (Source: V. Govindarajan and Robert N. Anthony, ''How 

Finns Use Cost Data in Pricing Decisions,"Management Accounting, July 1993, pp. 30-36.) 

6-11 
6.4.3 Manufacturer Price and Mark-ups for Different Efficiency Levels 

6.4.3.1 Pre-ANOPR Analysis 

For the ANOPR analysis the Department will use an average manufacturer mark-up of 1.35 

as an upper bound in conducting the LCC analysis. The Department will use a mark-up of I.DO as 

a lower bound. This lower bound represents pass-through of incremental full product costs, including 

depreciation of new capital expenditures. There is, however, no recuperation of incremental nonproduction costs (which include R&D and marketing expenses). 

In order to characterize the uncertainty in manufacturer mark-ups, the Department proposes 

usingatriangwardistributioncharacterizedbyamaximummanufacturermark-upofl.35,aminimum 

manufacturer mark-up of 1.00,"and a most likely mark-up of 1.18 (the average). Adding a fixed 

mark-up to retail price (of 1.40) and then to consumer price (mark-up of 1.052), as discussed in 

Section 6.3, the total mark-up from manufacturing cost to consumer price would be bound by the 

following values: . 

• minimum value of 1.00 x 1.40 x 1.052 = 1.4 73 

• maximum value of 1.35 x 1.40 x 1.052 = 1.990 

The Department proposes to characteri~e the total mark-up using a· triangular distribution 

with minimum, maximum, and most likely values of 1.473, 1.990, and 1.732, respectively. 

6.4.3.2 Post-ANOPR Analysis 

Post ANO PR analyses will. use a consistent set of assumptions for prices across all analyses· 

·. sections (manufacturer impact, national benefits, and consumer impacts). Manufacturer and retail 

price will be related by a fixed mark-up of 1.40, as discussed in Section 6.3 of this document, and a 

sales tax mark-up of 1.052 will be applied to arrive at the consumer price. 

Whereas the development of price scenarios for the manufacturer impact analysis will be 

determined at a later date, one possible approach that is being considered is the approach used in the 

1991 Arth:ur D. Little report to AHAM. This approach is discussed below: 

• For the detailed economic analyses conducted Post-ANOPR, manufacturer mark-ups will be 

determined by conducting financial analysis. The Government Regulatory Impact Model 

(GRIM) will be used to carry out the cash flow analysis. The GRIM is a standard annual cash 

flow analysis which ~es price, quantity, and cost information to assess the impact of 

regulatory conditions on manufacturer income and cash flow. The model calculates the actual 

cash flows, by year, and then determines the present value of those cash flows, both without 

regulations and with regulations. 

6-12 
The post-standard prices required in order to achieve several scenarios will be found by 

running the GRIM and treating manufacturer price as variable. Scenarios that will be 

considered include: · 

(a) What price (mark-up) results in maintenance of cuqent industry value? 

(b) What price (mark-up) would reduce industry value to zero? 

( c) What price would result from pass-through of incremental material, labor, and 

overhead costs only? 

6-13 
REFERENCES 

l. U.S. Department of Labor, Bureau of Labor Statistics. Consumer Expenditure Surveys, 

Washington, D.C. 

2. Dealerscope Consumer Electronics Marketplace. Annual Major Appliance ~tatistical Survey 

and Report. 

3. Elrick & Lavidge Computerized Audit Program (ELCAP) monitors the major domestic 

appliance industry in the U.S. INTELECT, a leading supplier of retail sales tracking 

information for the consumer electronics industry, acquired ELCAP on August 5, 1996. 

' 

4. U.S. Department of Commerce, Bureau of Census. Current Industrial Reports, MA36F, 

Major Household Appliances. Washington, D.C. 

5. Association of Home Appliance Manufacfurers. 1996 and 1997 Major Appliance Industry 

Fact Book, Chicago, IL. 

6. Arthur D. Little, Inc. 1991. Financial Impact of DOE Top Loading Horizontal Axis 

Standards on U.S. Washing Machine Manufacturers. Report to Association of Home 

Appliance Manufacturers Horizontal Axis Task Force. Cambridge, MA. 

6-14 
---------------~----- --- - ----- - - --------

CHAPTER 7: LIFE-CYCLE COSTS AND PAYBACK PERIOD 

7.1 INTRODUCTION 

This chapter describes the method for analyzing the economic impacts of possible standards 

on individual consumers. The effect of standards on individual consumers includes a change in 

operating experue (usually decreased) and a change in purchaSe price (usually increased). This 

chapter describes three metrics used in the consumer analysis, to determine the effect of standards 

OJ;l individual consumers: 

• 

• 

• 

Life-cyclecost (LCC) captures the tradeoff between purchase price and operating expenses 

for appliances. 

Payback period (PBP) measures the amount of time it takes consumers to recover the 

assumed higher purchase expense of more energy-efficient equipment through lower 

operating costs. 

Rebuttable Payback Period is a special case. of PBP. Where LCC and PBP are estimated over 

a range of inputs reflecting actual conditions, Rebuttable Payback Period is based on 

laboratory c~nditions, specifically, DOE test procedure inputs. 

These are discussed in sections 7.2, 7.3 and 7.4 of this chapter, respectively. Inputs and preliminary 

results are presented for each metric. Key quantities, current assumptions and calculations are 

· detailed for each metric. The calculations discussed here are performed on a series of Microsoft 

ExcelTM spreadsheets which are aceessible over the internet. Details and instructions for the 

spreadsheets are discussed in section 7 .6. A more complete set ofresults is available in Appendix C. 

7.1.1 General Approach for LCC and Distribution PBP 

In recognition that each household is unique, variability is analyzed in order to account for 

many of the differences among them by performing the calculations detailed here for a large sample 

of individualhouseholds. The results are expressed as the number of households having impacts of 

particular magnitudes. The LCC and PBP model is being developed using Microsoft ExcelM in 

Windows 95TM, combined with Crystal BallTM (a commercially available add-in). 

The analysis explicitly specifies both the uncertainty and variabilitY in the models inputs using 

probability distributions. A Monte Carlo simulation then takes thousands of random samples from 

each probability distribution within the model to calculate the outputs. Each input probability 

distribution is sampled in a way that reproduces the distributions' shape. The distribution of the 

·results therefore reflects the probability of the values that could occur given the range ofinput values. 

This techrlique helps provide an insight into the likelihood of various possible results. 

7-1 
The results are typically displayed as distributions of the impact examined as compared to the 

baseline. An importance analysis can be produced which shows relative contribution by each of the 

inputs to the total range of results. A variety of graphic displays will be created to illustrate the . 

implications of the analysis results. Examples would be: i) a cumulative probability distribution 

showing the percentage of American households which would have a net saving by owning a more 

energy efficient appliance or ii) a frequency chart that depicts variation of the life-cycle cost for each 

efficiency level considered. 

7.2 LIFE-CYCLE COST (LCq. 

· 7.2.1 Defmition 

· Life-cyclecost is the total consumer expense over the life of an appliance, including purchase 

expense and operating expenses (including energy expenditures). Future operating expenses are 

discounted to the time of purchase, and summed over the lifetime of theappliance. Life-cycle cost 

is defined by the following equation: 

0 

LCC=P+'L I 

(l+r)' (1) 

The following quantities are required to determine life-cycle cost: 

P = Purchase expense ($) 

L. = Sum over the lifetime, from year 1 to year N, where N = lifetime of appliance 

(years) 

0 = Annual operating expense ($) 

[Note: for clothes washers 0 = (cycles per year)* { (energy per cycle) * (energy 

rate) + (water per cycle) * (water rate)}] 

r = Discount rate (real) 

7-2 
7.2.2 LCC Inputs 

This section provides information about the quantities and assumptions used to calculate lifecycle cost for clothes washers. For each quantity, the discussion includes: 

• definition 

• ~pproach 

• current assumption 

Inputs for the LCC analysis are shown in Table 7.1 below. . . 

7.2.2.1 

Definition 

Table 7.1 Quantities in LCC Spreadsheet 

Purchase expense ($) 

Lifetime (years) 

Cycles per year 

En~rgy per cycle (kWh/cycle) 

Electricity rate (cents/kWh) 

Gas rate ($/MMBtu) 

Oil price ($/MMBtu) 

Water per cycle (gallons) 

Water rate ($/thousand gallons) 

Discount rate 

Water Heater/ Dryer Share 

Year to Start Date 

Base Case Design 

Standard Case Design 

Purchase expense 

Consumer expense for purchasing an appliance. · 

Approach 

Consumer purchase expense includes the retail price of the appliance plus sales tax. The 

baseline purchase .expense is the retail price plus sales tax for a clothes washer at the baseline 

efficiencylevel. The baseline price is a shipment weighted average (SWA) price of washers currently 

being sold. Efficiency and price are not necessarily related for clothes washers; price is more a 

function of features and brand name. 

7-3 
Example of Baseline Purchase Expense (all values in this description are rounded to the 

nearest dollar): 

Baseline retail price = $400 

Sales tax (U.S. average)= 5.2% 

Therefore, baseline purchase expense = $421 

The purchase expen.Se for each efficiency level is calculated as the sum of baseline purchase 

expense plus the incremental purchase expense for the efficiency level. .The incremental purchase 

expense is the product of incremental manufacturing cost, manufacturer variable markup, and retail 

variable markup, plusthe sales tax. The Association of Home Appliance Manufacturers (AHAM) 

provided incremental manufactllring costs for 9 efficiency levels. 

For example, the incremental manufacturing cost (shipment-weighted average) for a 35% 

energy reduction from baselineis $128, with a custom distribution provided by AHAM. The mean 

manufacturer variable markup is 1.175 (range of 1.0 to 1.35, triangular distribution), retail variable 

markup is 1.40, and sales tax adds 5.2% of the retail price. The incremental purchase expense is: 

$128 x 1.175 x 1.40 x 1.052 = $222. The purchase expense for the unit having a 35% energy 

reduction is the baseline purchase expense plus the incremental purchase expense= $421 + 222 = 

$643. A distribution of prices is used for the LCC analysis based on a distribution of incremental 

manufacturing costs and a distribution of markups. Single values are shown in this discussion for 

illustration purposes only. 

Current Assumptions 

AHAMdata is used for manufacturing costs (see Chapter 4). Arthur D. Little (ADL) report 

data is used for markups and sales tax (see Chapter 6). 

7.2.2.2 Lifetime 

Definition 

The period of time the clothes washer will provide service. 

Approach 

The lifetime is estimated by comparing historical shipments to percent of households owning 

the appliance (MTSC 1980)[1]. 

Current Assumption 

For clothes washers, lifetimes range from 12 to 16 years, with an average of 14.1 years. 

Figure 7 .1 shows the percent of washers surviving since year of original purchase. · 

7-4 
100% 

90% -

80% -

= '""' 70% - ·-... 

·-t 60% -

= ,.,, 50% - -

= ~ 

CJ 40% - .. 

~ 

~ 30% 

20% -

10% -

0% - ll - I 

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 

Years since original purchase 

Figure 7.1 Percent of clothes washers surviving since year of original purchase 

7.2.2.3 Cycles per year 

Definition 

Loads oflaundry washed per household per year. 

Approach 

The DOE test procedure assumes 392 cycles per year. In actuality, the number ofloads of 

laundry washed per household per year depends upon the number of persons in the household and 

on other factors. A survey ofU .S. households indicates a relationship between family size and cycles 

per year [2]. · 

Using the sample of households having both a clothes wa.Sher and a clothes dryer from the 

Energy Information Administration 1993 Residential Energy Consumption Survey (DOE/EIA 

1995)[3], we assigned these cycles per year to each household according to the persons per 

household. The weighted average result is 3 79 cycles per year for the U.S. mix of households. This 

value agrees closely with (is 4% lower than) the DOE test procedure. To the extent that households 

use different fuels for water heaters and clothes dryers, the cycles per year also differ among those 

households. 

Current Assumption 

To simulate a sample of households, each household is assigned a given number of cycles per 

year based upon the following table (the values are then scaled so the average agrees with the test 

7-5 
procedure assumption). Figure 7 .2 shows the percent of households (i.e., the weighting of 

households) for a given number of washer cycles per year. 

· Persons per household 

1 

rtJ 

... 

G.l 

40% 

i 30% 

0 

... 

Cl.I 

.:: 

rtJ 

0: 20% ~ . 

""' 

... = 

= t 10% ... 

G.l 

~ 

2 

3-4 

5+ 

207 

Cycles per year 

200 

310 

454 

624 

321 

Washer Cycles 

469 

Cycles per year (scaled) 

207 

321 

469 

.645 

645 

Figure 7.2 Percent ofWasher Owners by the number of washer cycles per year 

7.2.2.4 Energy per cycle 

Definition 

Energy consumed per cycle. One cycle corresponds to one load oflaundry. 

Approach 

In. accordance with the DOE test procedure (for modified energy factor, MEF), energy 

consumption per cycle jncludes electricity used by the clothes washer, and.energy used by the water 

heater to provide hot water, and energy used by the clothes dryer to dry the clothing. 

Current Assumptions 

Each of these components of energy consumption per cycle is obtained from data submitted 

by AHAM, for both the baseline and the various efficiency levels (see engineering analysis). Figure 

7.3 shows the energy use per cycle vs. MEF . 

. 7-6 
-

" 

3.5 3.2 3.2 

- 3.0 

CJ 

,,.... 

~ 2.5 

-= 

~ - 2.0 

~ 

fl'J 1.5 ::::> 

to 1.0 ... 

~ 

~ = 0.5 

0.0 

0.82 0.86 0.91 0.96 1.02 1.09 1.26 1.3 6 1.49 1.6 3 

MEF 

Figure 7.3 Energy use per cycle vs. modified energy factor (mef) 

7.2.2.5 Electricity rate 

Definition 

The value to a residential customer of saving electricity, expressed as cents per kilowatt-hour. 

Issues 

All households with clothes washers use electricity for the washer, and some households use 

electricity for water heating and clothes drying as well. Showing the change the customer will see 

in his/her electricity bill due to increased energy efficiency is complex because: 1) electricity rate 

varies from house to house, depending upon the rate schedule charged by the utility and the 

consumption pattern of the particular customer; and 2) future rates cannot be known with certainty, 

especiallysince the market is currently restructuring and a growing number of con,sumers are able to 

choose among competing utilities. 

A.pp roach _ 

The variations in electricity rate are handled by separating the problem into three parts: a) 

variabilityin energy rate among households; b) value of savings; and c) future trends. Each of these 

parts is discussed and current assumptions described. 

7-7 

1'· 
~ GI 

!! 

iii 

e 

0 

.s 

.., 

-

z: 

Cll 

'ii 

~ 

a) Variability. · The Energy Information Administrations 1993 Residential Energy 

Consumption Survey gathered information on electricity bills which is used to represent the 

variability from house to house (DOE/EIA 1993)[4). An advantage of using a survey 

containing information on specific households is the ability to directly capture correlations 

among quantities. Such correlations are important forelectric and gas prices, for example, 

or among household characteristics (e.g., fuel ofthe water heater and clothes dryer, persons 

per household) that affect energy consumption. 

Current assumption regardingvariabilitv: All those households from the Energy Information 

Administrations 1993 Residential Energy Consumption Survey (RECS) survey which have 

both a clothes washer and a clothes dryer are used. Energy prices are reported in the survey 

results for each household. These households are assigned weights to represent the U.S. 

Figure 7.4 shows residential electricity prices ranging from 2 to 17 cents/kWh. Prices are 

derived by scaling 1993 RECS householdenergy prices to 1997$ using the consumer price 

index arid then--to bring these prices in line with known national energy trends--scalinghe 

1997$ prices by the ratio of 1997 to 1993 national energy prices as reported in AEO 1998. 

(DOE/EIA,1997)[5] 

b) Value of Savings. The prices shown in Figure 7.4 are average prices including fixed 

charges. When a consumer saves electricity, the bill savings depend upon the marginal energy 

charge as reflected in the utility rate schedule (cents per kilowatt-hour), which may be higher 

or lower than the average rate in the figure. An exact accounting would require knowing for 

each household: the quantity and timing of energy used with and withoutthe saved energy, 

4.5% 

4.0% 

3.5% 

3.0% 

2.5% 

2.0% 

1.5% 

1.0% 

0.5% 

0.0% 

0 2 

Distribution of Residential Electricity Prices 

in 1997 

4 - 6 8 10 12 14 

cents/kWh 

16 18 

Figure 7.4 Distribution of 1997 residential electricity prices -

7-8 
::: 

and the rate schedule (rate per kilowatt-hour as a function of the quantity of kilowatt-hours 

used, including seasonal and/or time of day pricing). In general, such detailed information is 

not known. 

Three approaches are considered: 1) average rates, calculated as the ratio of household 

expenditures for electricity (including fixed charges) to kWh consumed; 2) partial adjustment 

of known inaccuracies by subtracting the average fraction corresponding to fixed charges 

from average rates; and 3) estimating the actual consumer marginal energy rate. We review 

the advantages and disadvantages of each of these approaches below: 

1) Average rates. 

AdVantage: Simple. Available in published historical data and forecasts. 

Disadvantage: May over- or under-estimate the consumer marginal energy rate. 

2) Subtract average fixed charge everywhere. 

Advantage: Approximately accounts for fixed charges. Edison Electric Institute 

(EEI) has assembled information on fixed charges for average general service 

residential rates for a large number of utilities that show fixed charges on average 

represent about 7.5% of average rates.[6] 

Disadvantage: By reducing the value of energy savings in all cases, including those 

where the marginal rate is higher than the average price including fixed charges, this 

method under-estimates the actual consumer marginal energy rate in some cases. 

3) Estimate distribution of consumer marginal energy rates. 

Advantage: Theoretically most accurate. 

Disadvantage: Practical difficulties: The rate schedules for households in the RECS 

~urvey are not known. The households, need to be assigned to utility service 

territories and to particular rate schedules within the utility. Month by month 

electricalconsumption would be needed to determine the monthly marginal rate, since 

energy is typically billed monthly, and rates may change seasonally. Rate schedule 

data does not exist for all utilities and, when available, is usually limited to general 

service residential customers. 

Current assumption regarding value ofsavings: Average rates are assumed in this analysis 

even though it is recognized that the use of marginal energy rates would improve the accuracy 

of the analysis. At this time, however, the data needed to determine marginal rates is not 

readily available. Moreover, in as much as it is unknown at this point if removing fixed costs 

is more or less reflective of m~ginal rates this intermediate step is not taken in this analysis. 

Developing consumer marginal energy rates: The Advisory Committee recommended that 

the Department use marginal energy rates rather than average energy rates, and the 

Department has agreed. In order to develop consumer marginal energy rates, the Department 

proposes to collect data on current rate schedules and energy consumption. Those rates will 

7-9 
be assigned to a national sample of households, weighted to represent the total U.S. 

population of buildings and households. The result will be a weighted distribution of 

consumption by marginal rates. 

DOE proposes to obtain a sample of residential buildings from existing surveys, such as the 

RECS or from a commercially available database. RECS data has the advantage of being in 

the public domain. In contrast, a commercially available database is likely to be more 

expensive, but may have significant added value in terms of assigning the buildings to states 

or to utilities, including a broader sample of the population, permitting stratification of this 

larger sample to distinguish among some sub-populations. The commercially available 

database also contains household load profiles. Each building will be assigned to a geographic 

region (e.g., state or utility service territory). Energy consumption by month will be included 

in the database for each building, in order to treat seasonal changes in consumption and rates. 

Recent Federal surveys (RECS) gather information by fuel on annual energy consumption and 

total expenditures for energy. Total expenditures for energy included customer and other 

fixed charges, energy rates, demand charges, taxes, etc., but these were not tabulated 

separately from each other. These surveys gathered customer bills but did not extract 

information on rate schedules, fixed charges or marginal rates. The Department proposes to 

explore the feasibility of extracting historical information on rate schedules, including the 

relationship between fixed charges and marginal rate.s to average prices. This effort, if 

successful, will provide information about the extent to which marginal rates differ from 

average prices, or from average prices less fixed charges. 

Given restructuring of parts of the energy supply sector, customers may have more than one 

bill( e.g., one from the distribution company, and one or more from generators or suppliers). 

To capture complete information, future surveys are expected to gather energy pricing 

information directly from customers, rather than from utilities or local distribution companies. 

The most efficient means to collect energy pricing information in the future involves changing 

the current processing of the billing information so as to gather more detail from the bills, to 

include consumption by month and pricing information. The pricing information would 

include for each customer, the rate schedule: marginal rates, fixed charges, demand charges, 

or time-of-use rates where applicable. The Department will express the need for these data 

in discussions with EIA about the design of future surveys. 

Residentialelectricity rate schedules will be collected from Federal databases where available, 

commercial vendors, state regulatory agencies or directly from utilities. The information 

obtained for each rate schedule will include any fixed charges (customer charges, etc.), block 

structure, and rate per kilowatt-hour (kWh) by block. Information from utilities or local 

distribution companies will be examined to determine: confirmation of the set of rate 

schedules, the number of cu~tomers by state using each rate schedule, the total electricity 

sales by state by rate schedule, and (if possible) monthly electricity sales by state by rate 

schedule.· 

7-10 
-----------~--- -------- -

In the database.ofbuildings, such characteristics as energy consumption arid expenditures and 

number of customers by state or utility will be used to map a rate schedule onto each of the 

buildings in the national sample. The marginal rate for each building will be the block from 

the rate schedule corresponding to that buildings monthly energy consumption. 

For life cycle savings calculations, the monthly energy savings will be estimated for each 

building. Those monthly energy savings for each building will then be evaluated for each 

building at the monthly marginal rate, using the rate schedule assigned to each building. 

c) Future Trend. Eswnating future electricity rates is now more difficult than ever. In some 

states, the electricity supply industry is undergoing restructuring. Previously, each household 

was assigned to a particular utility company, and the rates offered by that utility obtained from 

surveys. In the future, with restructuring, households will be able to purchase electricity from 

a large set of suppliers.· 

A projected trend in national average prices is applied to each households energy prices, after 

accounting for 'Value of savings" (described above). In the spreadsheets, the user can select 

from the following scenarios: 

1) Constant energy prices at 1997 values 

2) Energy Information Administration Annual Energy Outlook 1998, High 

Economic Growth 

3) Energy Information Administration Annual Energy Outlook 1998, Reference 

Case 

4) Energy Information Administration Annual Energy Outlook 1998, Low 

Economic Growth 

5) Gas Research Institute (GRl)l998 Baseline Projection 

Figure 7 .5 shows the trends for the last four of those projections. The values in later years 

(i.e. after 2015 for GRI and after 2020 for all others) are interpolated from their relative sources. 

Interpolation is needed because the sources used do not forecast beyond 2020 (or 2015 in the case 

of the GRI forecast). To arrive at values for these later years two techniques were used. In the three 

trends forecast by EIA (i.e., "High Growth,"'~O 1998,"and 'Low Growth') the price trend in the 

last five years of the forecast was used to ~stablish prices in the years 2020-2030. With the GRI 

forecast prices are simply left constant at 2015 levels. Additional work is being conducted to refine 

these later year forecast values in line with methods currently used by the EIA to forecast fuel prices 

for the Federal Energy Management Program (FEMP). Electricity prices will be held constant at 

2020 levels on the assumption that the transition to a restructured utility industry will have been 

completed. 

7-11 
9 E Iectricity Price · 

8 

7 

6 ---H igh·Growth 

-•-AEO 1998 

5 ---GRI 1998 

---Low Growth 

4 

1995 . 2000 2005 2010 2015 2020 2025 2030 

Figure 7.5 Alternative electricity price trends 

Current assumption regarding "future trend: This analysis assumes the trend from AE098 

Reference Case. 

7.2.2.6 Gas rate 

Definition 

The value to a residential customer of saving gas, expressed as dollars per million Btu. 

Issues 

Some households use gas for their water heater or clothes dryer~ Showing the change the 

cust<?mer will see in his/her gas bill due to increased energy efficiency is complex because: 1) gas rate 

varies from house to house, depending upon the rate schedule charged by the utility and the 

consumption pattern of the particular customer; and 2) .future rates cannot be known with certainty. 

Approach 

· The variations in gas rate are handled by separating the problem into three parts: 

a) variability in energy price among households; b) value of savings; and c) future trends. Each of 

these parts is discussed and current assumptions described. 

a) Variability . . The Energy Information Administrations 1993 Residential Energy 

Consumption Survey gathered information on gas bills which is used to represent the 

variabilityfrom house to house (DOE/EIA 1993). An advantage of using a survey containing . . 

7-12 
.!l 

= Cl 

"ii 

3': 

information on specific households is the ability to directly capture correlations among 

quantities. Such correlations are important for electric and gas rates, for example, or among 

household characteristics (e.g., fuel of the water heater and clothes dryer, persons per 

household) that affect energy consumption. 

Current assumption regarding variability: All those households from the Energy 

Information Administrations 1993 Residential Energy Consumption Survey (RECS) survey 

whichhave both a clothes washer and a clothes dryer are used. For each household, relevant 

energy prices are reported. These households are assigned weights to represent the U.S. 

Figure 7.6 shows residential gas prices ranging from 4.3 to 12.4 $/million BTU (MMBtu). 

Prices are derived by scaling 1993 RECS household energy prices to 1997$ using the 

consumer price index and then--to bring these prices in line with known national energy 

trends-~scaling the 1997$ prices by the ratio of 1997 to 1993 national energy prices as 

reported in AEO 1998. 

b) Value of Savings. The prices shown in Figure 7.6 include average prices including fixed 

charges. When a consumer saves energy, the bill savings depend upon the marginal energy 

charge as reflected in the utility rate schedule, which may be higher or lower than the average 

rate in Figure 7 .6. An exact accounting would require knowing· for each household: the 

quantity and timing of energy used with and without the saved energy, and the rate schedule 

(rate as a function of the quantity of energy used, Including seasonal pricing). In general, such 

detailed information is not known. 

0.60% 

0.50% 

0.40% 

0.30% 

0.20% 

0.10% 

0.00% 

0 2 

Distribution of Residential Gas Prices 

in 1997 

3 4 5 6 7 8 9 10 

dollar/MM Btu 

11 12 13 

Figure 7.6 Distribution of 1997 residential gas prices 

7-13 
1bree approaches are considered: (i) average .rates, calculated as the ratio of household 

expenditures for.gas (including fixed charges) to gas consumed; (ii) partial adjustment of 

known inaccuracies by subtracting the average fraction corresponding to fixed charges from 

average rates; and (iii) estimating the actual consumer marginal energy rate. We review the 

advantages and disadvantages of each of these approaches below. 

5) Average rates. 

Advantage: Simple. Available in published historical data and in forecasts. 

Disadvantage: May over- or under-estimate the actual consumer marginal energy 

rate. 

6) Subtract average-fixed charge everywhere. 

Advantage: Approximately accounts for fixed charges. The American Gas 

Association (AGA) has assembled information on fixed charges for residential gas 

customers that show fixed charges on average represent about 12% of average 

rates. 1

[7][8] 

Disadvantage: By reducing the value of energy savingsin all cases, including those 

where the marginal rate is higher than the average price including fixed charges, this 

method under-estimates the actual consumer marginal energy rate in some cases. 

7) Estimate distribution of consumer marginal energy rates. 

Advantage: Theoretically most accurate. 

Disadvantage: The rate schedules for households in the RECS survey are not known. 

The households need to be assigned to utility service territories and to particular rate 

schedules within the utility. Month by month gas consumption is needed to determine 

the monthly marginal rate, since energy is typically billed. monthly, and rates may 

change seasonally. Rate schedule data does not exist for all utilities and, when 

available, is usually limited to general service residential customers. 

Current assumption regarding value of savings: Average rates are assumed in this · 

analysis even though it is recognized that the use of marginal energy rates would 

improve the accuracy of the analysis. At this time, however, the data needed to 

determine marginal rates is not readily available. Moreover, inasmuch as it is 

unknown at this point if removing fixed costs is more or less reflective of marginal 

rates this intermediate step is not taken in this analysis. 

Developing consumer marginal energy rates: In order to develop consumer marginal energy 

rates, the Department proposes to collect data on current rate schedules and energy 

consumption. Those rates will be assigned to a national sample of buildings, weighted to 

represent the total U.S. population of buildings. The result will be a weighted distribution of 

consumption by marginal rates. 

1 More recent data has allowed for a revision in this number to 13.5%. 

7-14 
DOE proposes to obtain a sample of residential buildings from existing surveys, such as the 

RECS or from a commercially available database. RECS data has the advantage of being in 

the public domain. In contrast, a commercially available database is likely to be more 

expensive, but may have significant added value in terms of assigning the buildings to states 

or to utilities, including a broader sampleofthe population, and permitting stratification of 

this larger sample to distinguish among some sub-populations. The commercially available 

database also contains household load profiles. Each building will be assigned to a· 

geographic region (e.g., state or utility service territory). Energy consumption by month will 

be included in the database for each building, in order to treat seasonal changes in 

consumption and rates. 

Recent Federal surveys (RECS) gather information by fuel on annual energy consumption and 

total expenditures for energy. Total expenditures for energy included customer and other 

fixed charges, energy rates, demand· charges, taxes, etc. but these were not tabulated 

separately from each other. These surveys gathered customer bills but did not extract 

information on rate schedules, fixed charges or marginal rates. The Department proposes to 

explore the feasibility of extracting historical information on rate schedules, including the 

relationship between fixed charges and marginal rates to average prices. This effort, if 

successful, will provide information about the extent to which marginal rates differ from 

average prices, or from average prices less fixed charges. 

Given restructuring of parts of the energy supply sector, customers may have more than one 

bill(e.g., one from the distribution company, and one or more from generators or suppliers). 

To capture complete information, future surveys are expected to gather energy pricing 

information directly from customers, rather than from utilities or local distribution companies. 

The most efficient means to collect energy pricing information in the future involves changing 

the current processing of the billing information so as to gather more detail from the bills, to 

include consumption by month and pricing information. The pricing information would 

include for each customer, the rate schedule: marginal rates and fixed charges, where 

applicable. The Department will express the need for these data in discussions with EIA 

about the design of future surveys. 

Residential natural gas rate schedules will be collected from Federal databases where 

available,commercially available data, or state regulatory agencies. The information obtained 

for each rate schedule will include any fixed charges (customer charges, etc.), block structure, 

and rate per therm by block. Information from utilities or local distribution companies will 

be examined to determine: confirmation of the set of rate schedules, the number of customers 

by state using each rate schedule, the total gas sales by state by rate schedule, and (if possible) 

monthly gas sales by state by rate schedule. 

In the database ofbuildings, such characteristics as energy consumption and expenditures and 

number of customers by state or utility will be used to map a rate schedule onto each of the 

buildings in the national sample. The marginal rate for each building.rill be th~ block from 

7-15 
..... = 

= 

~ 

~ 61"7 

the rate schedule corresponding to that buildings monthly energy consumption. 

For life cycle savings calculations, the monthly energy savings will be estimated for each 

building. Those monthly energy savings for each building will then be evaluated for each 

building at the monthly marginal rate, using the rate schedule assigned to each building. 

c) Future Trend. A projected trend in national average prices is applied to each households 

energy prices, after accounting for "value of savings" (described above). In the spreadsheets, 

the user can select from the following scenarios: 

1) Constant energy prices at 1997 values 

2) Energy Information Administration Annual Energy Outlook 1998, High 

Economic Growth 

3) Energy Information Administration Annual Energy Outlook 1998, Reference 

Case 

4) Energy Information Administration Annual Energy Outlook 1998, Low 

Economic Growth 

5) Gas Research Institute 1998 Baseline Projection. 

Figure 7. 7 shows the projections. The values in later years (i.e. after 2015 for GRI and after 

2020 for all others) are interpolated from their relative sources Interpolation is necessary 

because the sources used do not forecast beyond 2020 (or 201 S in the case of the GRI 

forecast). To arrive at values for these later years forecast prices are simply left constant at 

2020 (2015 for GRI) levels. Additional work is being conducted to refine these later year 

forecast values in line with methods currently used by the EIA. Natural gas prices will be 

derived from the average growth rate for world prices over the years 2010 to 2020 and will 

be combined with regional price margins from the year 2020. 

6.5 

6.0 

5 .. 5 

5.0 

4.5 

4.0 

Gas Price 

---High Grow th 

. -•-AE 0 1998 

--GRI 1998 

---Low Growth 

1995 2000 2005 2010 2015 2020 2025 2030 

Figure 7.7 Alternative gas price trends 

7-16 
Current assumption regarding future trend: This analysis assumes the trend from the AEO 

1998 Reference Case .. 

7.2.2.7 Oil Prices 

Definition 

Residential price of distillate. 

Approach 

Variabilityin prices is taken from the RECS sample. No attempt is made to define a consumer 

marginal energy rate. Figure7.8 shows the oil price time trend. The values in later years (i.e. after 

2015 for GRI and after 2020 for all others) a:re extrapolated from their relative sources. 

Extrapolation is necessary because the sources used do not forecast beyond 2020 (or 2015 in the case . 

of the GRI forecast). To arrive at values for these later years forecast prices are simply left constant 

at 2020 (2015 for GRI) levels. Additional work is being conducted to refine these later year forecast 

values in line with methods currently used by the EIA For petroleum products, the average growth 

rate for the world oil price over the years 2010 to 2020 will be used in combination with the refinery 

and distribution markup from the year 2020 to determine the regional price forecasts. 

9.0 -

8.5 -

8.0 -

6.0 -

5.5 -

5.0 

1995 

I I 

2000 2005 

Figure 7.8 Alternative oil price trends 

0 ii Price 

---High Grow th 

-•-AEO 1998 · 

---Low Growth 

--GRI 1998 

I I I I I 

2010 2015 2020 2025 2030 

7-17 
Current Assumptions 

Variabilityin price from 1993 RECS sample ofhouseholds owning washers and dryers. The 

future trend is taken from the AEO 1998 Reference Case. 

7.2.2.8 Water per Cycle 

Definition 

Water consumed by clothes washer in one cycle. 

Approach 

Data as submitted by AHAM. [9] 

Current Assumption 

Use numerical values for baseline and eaclefficiency level, as provided by AHAM. Figure 

7.9 shows the water use per cycle vs. modified energy factor (MEF). 

45 

40 

39 39 39 39 38 

. 35 

-

~ 

~ 30 ~ --

= t>J) 25 

-~ 

I'll 20 ~ 

a. 

~ 15 

-

= 

~ 10 

5 

0 

0.82 0.86 0.91 0.96 1.02 1.09 1.26 . 1.36 1.49 1.63 

MEF 

Figure 7.9 Water use per cycle vs. MEF 

7-18 
7.2.2.9 Water and wastewater removal rates 

Definition 

Residential customer rate for water and wastewater (sewer) (expressed in $/ thousand 

gallons), excluding fixed charges. 

Issues 

Water is a significant cost of doing laundry. Different designs of washers have a large 

difference in water use. 

Approach 

An existing survey of water and wastewater rates represents the population-weighted . 

distribution of U.S. water rates in 1994 [10] [11]. Research continues to: 1) update this estimate; 2) 

estimate historical change in these rates; and 3) obtain projections of future change in·those rates. 

Figure 7 .10 shows the distribution of the sum of water and wastewater rates per 1000 gallons 

in 1994 expressed in·1997$. 

8.0% 

7.0% 

6.0% 

g 5.0% 

~ 

~ 

.Q 4.0% 

·.:: 

-

en i5 3.0% 

2.0% 

Net Water & Sewer Charges in 1994 

In 1997$; Average= $3.18 

o v m N ~ o v m N ~ o v m N ~ o v m N ~ o v m·N ~ o v m N ~ o v m 0 N v ~ m N v ~ m ~ v ~ m ~ M ~ ~ 0 M ~ m 0 N ~ ~ 0 N v ~ ~ N v w 0 0 0 0 0 ~ ~ ~ ~ N N N N M M M M ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ m ~ ~ ~ ~ ~ """"""""""""""""""""""""""""""""" 

1997$/1000 gallons 

.Figure 7.10 Distribution of net water and sewer charges in 1994 (expressed in 1997$) 

7-19 
Current Assumption 

Use thel 994 distribution of U.S. values for water and wastewater rates. The escalation rate 

of water is selectable by the user of the spreadsheet. Currently, the escalation rate is set at zero, 

pending further research. The average value for water price currently used is $3 .18 per 1000 gallons. 

Future Assumptions 

In some cases water prices come with a fixed cost to be connected, in some cases the rate is 

higher as usage increases and in other cases the rate deceases with increased water consumption .. 

Analysis is being conducted to determine the marginal cost of water and. the rate of increase of 

marginal rates. Because this is expected to vary with region of the country, work is being done to 

get costs and escalation rates for different geographic regions. This data can then be mapped onto 

the RECS database and onto each household. The analysis consists of updating previous data from 

Ernst and Young report as adjusted by Al Dietemann, as well as new data obtained from the 

American Water Works Association (A WW A). The Ernst and Young data is being updated with data 

from 125 utilities (used in the original study), including their water rate/wastewat~r disposal schedules 

and their forecasts for the future, as well as any historical information available. Historical data from 

previous Raftelis water rate studies will also be used. These three sources will be combined into one 

database. A distribution of water price~ (as iri the current analysis) will be used as well as a 

distribution of escalation rates. To determine escalation rates, information on historical trends in 

marginal rates as well as the cost to meet future Government water quality regulations will be taken 

into consideration .. 

7.2.2.10 Discount Rate · 

Definition 

The rate at which future expenditures are discounted to establish their present value. 

Approach 

A distribution of discount rates was derived to represent the variability in financing methods 

consumers use in purchasing appliances. The resulting distribution of discount rates is used to 

calculate a distribution oflife-cycle costs for clothes washers. Table 7.2 summarizes the interest rate 

assumptions discussed below. 

Table 7.2 Summary of Nominal Interest Rates 

FinanCing Method Percent of Clothes Washer Sales Range of Real Interest Rates 

Minimum Maximum Mean 

New home (after tax) 20% 1.60% 3.76% 2.68% 

Cash 32% 0.00% 3.00% 1.50% 

Credit Card 28% 6.00% 15.00% 10.5% 

Retailer Loan 20o/o 6.00% 15.00% 10.50% 

7-20 
Consumer Purchase and Financing Methods. Consumers purchase appliances in new homes 

and as retail purchases. The retail purchases are paid by cash, credit cards, or retailer loans. A 

stakeholder comment indicated that for white goods about 40% of retail purchases are paid in cash, 

35% use credit cards, and 25% use retailer loans [12]. The same comment indicates 25% of 

appliance purchases are for new homes. (For clothes washers, DOE estimates that purchases for new 

homes are now about 20%.) The method of purchase used by consum~rs is assumed to be indicative 

of the source of the funds and the type of financing used by these consumers. 

Interest Rates. DOE estimated a range of interest rates that may reasonably be expected to 

apply in the future to different types of consumer savings orfinancing. These are estimates, based 

upon historical data and judgment about how the future may differ from the historical period. 

For new housing, DOE estimated nominal mortgage rates ( 5-8% ), derived the after-tax rate 

assuminga tax of28%, then subtracted the inflation rate (assume 2%). The result is a range of real 

mortgage rates of 1.60-3.76%. (Example: 5% * (100%-28%)- 2% = 1.6%) 

For cash, the minimum rate is 0%. This rate applies to purchasers making cash purchases 

without withdrawing from savings accounts. The maximum is taken to be the opportunity cost 

represented by the interest that could have been earned in a savings account. The historical nominal 

maximumsavings rate ranged from 4.5-5.5% from 1970 to 1986 (real rates of-8.27 to +3.58%). A 

real rate of3% as indicative of the maximum. · 

The interest rates for.retailer loa.nS and credit cards are assumed to have the same range. The 

minimum credit card rate is taken as 6% real. Introductory rates on some credit cards today are 5 .9% 

nominal, but after the introductory period (often 6 months), the rate becomes much higher. 

Maximtnn rates are over 20% nominal. On the other hand, most retail purchasers will not make 

payments for a clothes washer over the entire lifetime ( 12-16 years) of the appliance. If the purchase 

is paid for initially by a credit card, but the consumer pays off the balance in less than the life of the 

washer, then the effective interest rate is lower than the nominal credit card rate. The current 

assumption is a range of 6-15% real. 

Current Assumption . 

The real interest rate associated with financing an appliance purchase or with the savings from 

which the necessary funds were drawn are good indicators of the additional costs incurred by 

consumers who pay a higher first cost, put enjoy future savings. For this reason, it is assumed that 

these rates are appropriate discount rates for use in this analysis. Table 7.3 summarizes the ranges 

of discount rates used in the LCC distribution analysis, derived from the analysis of financing methods 

described above. Figure 7 .11 shows the distribution of real discount rates, ranging from 0 to 15%, 

with a mean of 6%. The weights noted on the y-axis indicate percent of purchases. 

Tahl.e 7.3 uses the data in Table 7 .2 and accounts for overlap between the new home and cash 

interest rates. 

7-21 
--- 

--~~---------

Table 7.3 Distribution of Discount Rates 

Minimum Value Maximt1Pl Value Probability 

0.00% 1.60% 17% 

1.60% 3.00% 28% 

3.00% 3.76% 7% 

3.76% 6.00% 0% 

6.00% 15.00% 48% 

Consumer Real Discount Rate 

20% 

16% 

rll 12% ..... 

.c 

tlll 

.... ~ 

~ 8% 

4% 

0% 

0% 2% 4% 6% 8% 10% 12% 14% 16% 

Dis count Rate 

Figure 7.11 Distribution of Consumer Discount Rates 

DOE recognizes that other factors might be considered iri the esthnation of real consumer 

discount rates, such as the actual impacts of appliance purchases on consumer savings, indebtedness 

or consumption, and expressed or imputed consumer preferences. While such data, if it were to 

become available, might provide a stronger analytical basis for DO Es choice of discount rates, it is 

considered unlikely that such data would have a significant effect on the range of values considered 

in the current analysis. 

7-22 

... 
--------------- -- ------------------------------------ -

7.2.2.11 Fuel use of water heater and clothes dryer 

Definition 

The fuel used to heat water in the water heater and the fuel used to provide heat in the clothes 

dryer. 

Approach 

· When using the Crystal BallTM distribution option on the spreadsheet, the distribution of fuel 

used is determined by the sampling of the RECS households. The fuel type used for the water heater 

and the clothes dryer are provided in.the RECS database. Only those houses having both a clothes 

washer and dryer are included in the sample. Households using either electricity, natural gas or fuel 

oil are sampled. The DOE test procedure on which reported energy use is based accounts for the fact 

that not all clothes washed in a clothes washer are dried in a clothes dryer. 

The DOE test procedure assumes an electric water heater and an electric dryer. The DOE 

test procedure is used for the Rebuttable Payback calculation. 

Current Assumption 

The distribution of fuel use as provided by RECS is shown in Table 7.4. Currently only the 

households in the RECS data base using these fuels are sampled using the Monte Carlo method. 

Table 7.4 Shares in Households of Water Heaters and Dryers by Fuel Type 

Water Heater Dryer Percent of Households 

Electric Electric 41.2% 

Electric Gas 0.6% 

Gas Electric 35.5% 

Gas Gas 20.1%. 

Oil Electric· 2.5% 

Oil Gas 0.1% 

Source: EIA, RECS 1993 

Planned Future Changes 

Liquid petroleum gas, LPG (also commonly referred toas propane) will be added as a fuel 

choice to be consistent with ·the water heater rulemaking. 

7-23 
7.2.2.12 Year to start date 

Definition 

This is the year at which a new standard is expected to become effective. 

Approach 

The LCC is calculated for all households as if they each purchase a new washer in the year 

the standard takes affect. The cost of appliances are based on this year, however, all dollar values 

are expressed in 1997 dollars. AnnuaI·energy prices are included for the life of the washer. 

Current Assumption 

The new eriergy efficiency standard for clothes washers is assumed to take effect in the year 

2003. 

7.2.2.13 Base Case Design 

Definition 

This is the cost and efficiency of the starting point to which different improvement levels of 

washers are compared. 

Approach 

AHAM supplied cost and efficiency data for a baseline and nine higher efficiency levels. The 

user can select any level against ·which to compare higher efficiency levels (see Engineering section). 

Current Assumption 

The default assumption for the base case design is the baseline design option. 

7.2.2.14 Standard case design 

Definition 

The improved efficiency level for comparison with the base case design. 

Approach 

The spreadsheet user selects the level for the analysis. 

Current Assumption 

Analysis is done for all levels for which data was provided. 

7-24 
- --- - -- ----- ----------- 

7.2.3 LCC results and preliminary output 

Titls section presents preliminary results for life-cycle cost (LCC) for the efficiency 

improvement level~ specified in the Engineering Analysis (Chapter 4). Results presented here are 

based on the inputs described in section 7 .2.2. Since the value of most inputs are uncertain and must 

be represented by a distribution of values rather than a discrete value, the results are presented here 

as a distribution of values. Where distributions are shown, the mean value of the distribution is also 

presented. · 

LCC results are presented as differences in the LCC relative to the baseline clothes washer 

design. As mentioned previously, the LCC differences are depicted as a distribution of values. The 

primary results are presented in two types of charts: 1) afrequency chart showing the distribution 

of LCC differences With its corresponding probability of occurrence and 2) acumulative chart 

showing the cumulative distribution of LCC differences along with the corresponding probability of 

occurrence. In each chart, the mean LCC difference is provided along with the percent of the 

population for which the LCC will decrease. 

In the explanation below, the two charts depicting the case for a 25% efficiency improvement 

levelare used (Figure 7.12 and 7.13). The 25% efficiency improvement level represents the standard 

level at which the energy usage is reduced by 25%. In either chart (frequency or cumulative), the 

mean change (reduction of$205 in the examples here) is shown in a text box next to a vertical line 

at that value on the x-axis. The phrase 'Certainty is 89 .23 % from -Infinity to $0" means that 89 .23% 

of households will have reduced LCC with the increased efficiency level compared to the baseline 

efficiency level. 

10,000 Trials 

.047 

Forecast: LCC Difference 

Frequency Chart 0 Outliers 

- 471 

~ 

·- .035-i------------------tttHffllfft-----I- 353.2 

.024+---------------~ 

.,, 

~ 

ro 

fffffffffl!fffhr----:---l- 235.5 .c 

c 

ro 

::i . 

. 012-t-------------fflfffffffffftilffffffffffH+H----I- 117. 7 ~ 

.000-1-..,...~-----,,..---,-aaaa..~l&&Ul .......... l:l&LLl&.LllJ~111.1 

($1,500) ($1,063) ($625) ($188) $250 

Certainty is 89.23% from -Infinity to $0 $ 

Figure 7.12 Frequency chart of LCC differences for 25% efficiency improvement level 

7-25 
Figure 7.12 is an example of afrequency chart. They-axes show the number of households 

('Frequency" at right y-axis) and percent of all households ('Probability" at left y-axis). In this 

example, 10,000 households were examined ("l 0,000 trials'') and all results are displayed (''O 

outliers''). The x-axis is the difference in LCC between a baseline efficiency level and a higher 

efficiencylevel (in this example, 25% energy reduction). The x-axis begins with negative values on 

the left, which indicate that standards for those households provide savings (reduced LCC). Reduced 

LCC occurs when reduced operating expenses- energy and water- more than compensate for 

increased purchase expense. In Figure 7.12, going from the baseline efficiency level to the 25% 

efficiencyimprovement level provides households with an average LCC reduction of$205, and range 

from reductions of $1486 (at the left) .to increases of $137 (at the right) depending upon the 

household. (The minimum and maximum values cannot be read with precision for the graph, but 

rather, the program provides them in a statistical summary.) 

Forecast: LCC Difference 

.10,000 Trials Cumulative Chart 0 Outliers 

1.000..r-----------------.------==~--i- 10000 

.0001-..,....~ ~-..,..--,....---~i&lllllld,lll.WWlilUJIJJI 

($1,500) ($1,063) ($625) 

Mean= $205 WJi]!Dllllm~IJJ.DimrmDll,_l_ o 

($188) $250 

Certainty is 89.23% from -Infinity to $0 $ 

.,, 

.... 

n 

.Cl 

c 

n 

::i 

~ 

Fig~re 7.13 Cumulative chart of LCC differences for 25% efficiency improvement level 

The vertical axis in Figure 7.13 is the cumulative probability (left axis) or frequency (right 

axis) that the LCC difference will be less than the value on the horizontal axis.Starting at the left, 

there is a 0% probability that a household will have a reduction in LCC larger than $1486 in absolute 

value. Toward the middle, there is a 50% probability that a household will have a reduction in LCC 

larger than about $164. At the right, there is a 100% probability that a household will have either a 

decrease in LCC or an increase in LCC ofless than $13 77 

· 2 See Table 7.5 for intermediate values 

7-26 
Appendix C contains the.frequency and cumulative charts for all the efficiency levels. These 

charts provide more complete information than summarystatistics, but a summary of the change in 

LCC from the baseline by percentile groupings (i.e., of the distribution ofresults) is given below in 

Table 7.5. 

T a e bl 7 5 S • ummaryo fLCCR esu Its 

Percent Change in LCC from Baseline1 Percent 

Efficienc Shown by Percentiles of the Distribution of Resulti with LCC 

y Level (values in $) Less than 

Baseline 

0 10 25 so 75 90 100 Mean 

5 ($83) ($33) ($24) ($16) ($11) ($8) ($2) ($19) 100.0 

10 ($232) ($82) ($55) ($36) ($23) . ($15) $13 ($43) 99.5 

15 ($402) ($140) ($90) ($55) ($33) ($19) $63 ($68) 95.6 

20 ($504) ($161) ($98) ($55) ($26) $10 $129 ($67) 86.7 

25 ($1,486) ($465) ($303) ($164) ($67) $4 $137 . ($205) 89.2 

35 ($1,997) ($639) ($408) ($211) ($59) $79 $570 ($252) 83.4 

40 . ($2,039) ($649) ($412) ($207) ($64) $75 $645 ($253) 83.7 

. 45 ($2,068). ($606) ($365) ($155) $9 $159' $666 ($199) 73.6 

50 ($2,075) ($617) ($374) ($156) $6 $153 $571 ($204) 74.2 

1 The baseline LCC, based on SWA of the most likely costs, is $1,554 

2 For sample size of 10,000 trials. Energy price trends are for AEO 1998. Operatingcosts include water 

prices. No escalator is assumed for water price. 

Each row in Table 7.5 corresponds to the impacts on a population of households from a 

possible standard level. For example, for a 25% efficiency increase, the sample household with the 

largest reduction in LCC saves $1486 compared to the baseline. This result is in the 'O" percentile 

column. For the same 25% efficiency increase, the sample household with the largest increase in 

LCC, $137, is represented in the "100" percentile column. In between, 10% of sample households 

lower LCC by at least $465, 20% of sample households lower LCC by at lea$t $303, 75% of sample 

households lower LCC by at least $67, and 90% of households have either reduced LCC or increases 

in LCC no greater than $4. The mean impact is an LCC reduced by $205 (in column "Mean"). The 

right-most column shows the percent of sample households for which the particular standard level 

would produce some reduction in LCC, e.g. 89.2% of sample households for an efficiency 

improvement of 25%. I 

7-27 
7.3 DISTRIBUTION PAYBACK PERIOD 

7.3.l l\1etric 

The payback period (PBP) measures the amount of time it takes the consumer to recover the 

assumed higher purchase expense of more energy-efficient equipment through lower operating costs. 

Numerically,the PBP is the ratio of the increase in purchase expense (i.e., from a less efficient design 

to a more efficient design) to the decrease in annual operating expenditures. This type of calculation 

is known as a ''Simple" payback period, because is does not take into account changes in operating 

expense over time or the time value of money, that is, the calculation is done at an effective discount 

rate of0%. · 

PBP is found by solving the equation 

(2) 

for PAY, where t:..P = difference in purchase expense between the more efficient and the less efficient 

design options($), and t:..01 =difference in annual operating expenses ($/a). PBPs are expressed in 

years. PBPs greater than the life of the product mean that the increased purchase expense is not 

recovered in reduced operating expenses. 

7.3.2 Inputs 

The data inputs to PBP are the purchase expense for each design option and the annual (first 

year) operating expenditures for each design option. The inputs to the operating costs are the annual 

energy savings, the energy price, the change in annual water consumption, and the water price. The 

DistributionPBP uses the same inputs as the LCC analysis described in section 7.2 except for a few 

exceptions described below. 

Since this is a ''Simple" payback the fuel rates used are only for the year the standard takes 

effect, assumed here to be the year 2003. The price of electricity, gas and oil are those projected for 

that year. Similarly for water rates, a single value for the price of water and wastewater disposal is 

. used. Discount rates are not used for the payback calculation . 

. . 7-28 
7 .3.3 Preliminary Results . 

Figure 7 .14 is an example of a chart showing the distribution of payback periods for the 25% 

improvement efficiency level. Note that the mean value of the payback period distribution ( 4 .4 years) 

is provided. 

Appendix C contains Distribution PBP charts for all the efficiency levels. Again, these charts 

provide more complete information than summary statistics, but a summary of payback values by 

percentile groupings (i.e., of the distribution of results) is given below in Table 7 .6. Each chart is the 

result of 10,000 Monte Carlo runs or in other words, 10,000 samples from each of the distribution 

inputs. 

10,000 Trials 

.054 

.041 

~ -.Cl .027 ~ 

.Cl 

0 

a.. .014 a. 

.000 ~ 

0.0 

Forecast: Payback 

Frequency Chart 

Mean= 4.4 

8.8 17.5 

years 

26.3 

Figure 7.14 Distribution of PBPs for 25% efficiency improvement level 

7-29 

O Outliers 

544 

~ - 0 

35.0 

.,, 

"""' n 

.c 

c 

n 

= 

~ 
T a e bl 7 6 . S ummaryo f P b ay ac kP er10 . d R I esu ts 

Percent Payback Period in Years 

Efficiency Shown by Percentiles of the Distribution of Results1 

Level 

0% 10% 25% 50% 75% 90% 100% Mean 

5 0.0 0.0 0.0 0.0 0.0 0.2 3.7 0.1 

10 0.0 0.0 0.0 0.1 0.5 1.6 15:8 0.6 

15 0.0 0.0 0.1 0.2 0.6 4.1 40.7 1.4' 

20 0.0 0.1 0.2 0.5 5.2 10.8 57.9 3.6 

25 '0.0 0.8 1.8 3.6 6.0 8.8 34.5 4.4 

35 0,8 2.0 2.8 4.2 6.9 11.4 49.8 5.8 

40 0.7 2.0 2.8 4.3 6.9 11.4 57.8 5.8 

45 0.7 2.4 3.6 5.8 9.3 13.9 54.0 7.2 

50 0.9 2.7 3.8 5.9 9.1 13.5 54.5 7.2 

I For sample size of 10,000 tnals. Energy pncetrends are for AEO 1998. Operating costs include water 

prices. No escalator is assumed for water price. 

7.4 REBUTTABLEPBP 

Rebuttable PBPs are presented in order to provide the legally established rebuttable 

presumption that a energy efficiency standard is economically justified if the additional product costs . 

attributed to the standard are less than three times the. value of the first year energy cost savings (42 · 

U .S.C. §6295 ( o )(2)(B)(iii)). 

7.4.1 .l\tetric 

The basic equation for Rebuttable PBP is the same as that shown in section 7.3. Unlike the 

analyses in sections 7.2 andsection 7.3, the Rebuttable PBP is not based on· distributions and does 

not utilize the Crystal BallTM option in the spreadsheet model. Rather than using distributions, the 

Rebuttable PBP is based on discrete values and is based on the DOE clothes washer test procedure 

assumptions (Appendix JI). 

7-30 
7.4.2 Inputs 

Inputs differ from the Distribution PBP in that discrete values are used rather than 

distributions (where distributions are used in the LCC and Distribution PBP) for inputs. All dollar 

values are in 1997$. 

• For purchase expense the shipment weighted average (SWA) of most likely (in the AHAM 

supplied Engineering data) is used as the input. 

• For the lifetime an average value of 14.1 years is used. 

• The cycles/year are 392, from DOE test procedure 

• Energy per cycle is obtained from the AHAM supplied data; for this parameter the original 

data was not submitted as a distribution, but as single discrete values for machine energy, hot 

water energy, and dryer energy, for every efficiency level. 

• Electric, gas and oil rates used are average values for the year the standard takes effect, i.e., 

AEO projections for the year 2003. 

• Water use per cycle is the gallons per cycle submitted by AHAM (see Engineering data). 

• Water plus wastewater rates is the average of a national distribution; $3 .18/1000 gallons. The 

value used did not assume an escalation rate, but more research is being conducted to 

determine this value. 

• A discount rate is not required in this calculation · 

• The fuel use is based on the test procedure asslimption of an electric water heater and an 

electric dryer. 

• Year to start date assumed is 2003. 

Payback periods are first calculated between the new standard level and each washer 

efficiency being sold in the year 2003. The paybacks arahen weighted and averaged according to 

the percentage of each washer efficiencysold before a new standard is enacted. With the presently 

availabledata, the baseline efficiency level is weighted with a market share of 95% and the horizontal 

axis market share is weighted at 5%. Rather than distributions, single point values for the inputs are 

used. These values (including cycles per year, electric fuel source, etc.) correspond to those outlined 

in the DOE test procedure, discussed in Appendix JI. The result is a single payback value and not 

a distribution of PBPs. 

The payback is calculated for the expected effective year of the standard (e.g., 2003) and is 

presented below in Table 7. 7. Data on a forecasted distribution of clothes washer efficiencies in the 

year 2003 would be useful in refining these calculations. · 

7-31 
7.4.3 Preliminary Results 

Table 7. 7 Rebuttable Presumption Payback in Years 

Market Share 95% 5% 

Standard Level 0% to Standard 35% to Standard Weighted Payback 

5 0.1 NA 0.1 

10 0.2 NA 0.2 

15 0.6 NA 0.6 

20 1.8 NA 1.8 

25 2.7 NA 2.7 

35 3.7 NA 3.7 

40 3.7 3.7 3.7 

45 4.9 29.2 6.2 

50 5.0 19.6 5.8 

7.5 USER INSTRUCTIONS FOR SPREADSaEET 

It is possible to examine and reproduce the detailed results obtained in this part of the analysis 

using a Microsoft ExcelTM spreadsheet available on the U.S. Department of Energy Office of Codes 

and Standards website at: wwW.eren.doe.gov/buildings/codes_standards/. The LCC spreadsheet is 

called''LCC _ CW3. l .XLS". To execute the spreadsheets fully you will need both Mircosoft ExcelTM 

and Crystal BallTM software. Both applications are commercially available. 

The spreadsheets posted on the website represent the latest versions of the applicable models, 

and have been tested with both EXCEL97 and EXCEL95. 

Basic Operating Instructions: 

1. Once you have downloaded the LCC file from the Web, open the file using Excel. At the 

bottom, click on the tab for sheet 'LCC." 

2. The screen will display two figures (''Cumulative Cost" and "Difference in Cumulative Cash 

Flow'') and two columns of highlighted data ('Baseline" and a given efficiency level that the 

user can determine by modifying the input assumptions). (Use Excels commands at the top 

"View/Zoom" to change the size of the display to make it fit your monitor.) 

7-32 
3. To see the assumptions, click on 'List lnpll:ts." 

4. To change assumptions on 'List Inputs" click on the a5sumption you wish to change, and 

click on the new assumption from the menu. 

5. · After selecting each assumption, click on 'List Inputs.'' 'Cancel" wiH erase your last set of 

changes to assumptions without recalculating results .. 

6. Finally, to see results, click on 'Calculate." . 

Using these steps you can explore outputs using a range of alternative assumptions. 

To produce sensitivity results using Crystal BalTM , including the sort of distribution figures 

shown in Figures 7.12-7.14, you need simply select 'hm"from the Run menu on the menu bar. To 

make basic changes in the run sequence, including altering the number of trials, select 'tun 

preferences" from the Run menu. Once Crystal BallTM has completed its run sequence it will produce 

a series of distributions. Using the menu bars on the distribution results it is possible to obtain further 

statistical information. The time taken to complete a run sequence can be reduced by minimizing the 

Crystal BallTM window in Excel. For more information, please consult the Crystal BallTM users guide 

or Help menu in Crystal BallTM . 

Table 7 .8 shows the quantities and their locations in the life-cycle cost spreadsheet 

(LCC_CW3.l.XLS). The first column (QUANTITY) shows the name of the quantity and the units 

(e.g., $ or years). The remaining columns show the location, including the sheet ahd, as appropriate, 

the ceH address or 'button" where the numerical values are located. 

7-33 
Table 7.8 Quantities and Locations in Life-Cycle Cost Spreadsheet 

QUANTITY SHEET CELL(S) BUTTON 

Purchase expense ($) 

Lifetime (years) 

Cycles per year 

· Energy per cycle (kWh/cycle) 

Electricity rate (cents/kWh) 

Gas rate ($/MMBtu) 

Oil price ($/MMBtu) 

Water per cycle (gallons) 

Water rate ($/thousand gallons) 

Discount rate 

Water Heater/Dryer Type 

Year to Start Date . 

Base Case Design 

Standard Case Design 

Manufacturers Cost 

Engineering 

Lifetime 

Setup 

Engineering 

LCC 

LCC 

LCC 

Engineering 

Water Price 

LCC 

RECS households 

LCC· 

LCC 

LCC 

Engineering 

7-34 

ES-17 

C3 

E40 

G,H&IS-17 

'List Inputs" 

'List Inputs" 

'List Inputs" 

JS-17 

B4 

136 

'List Inputs" 

'List Inputs" 

'List Inputs" 

K8-Q17 
REFERENCES 

1. MTSC Inc./Management Technology Division 1980. Energy Capital in the U.S. 

Economy. Bethesda, MD: MTSC. No~ember. 

2. Erikson, J.T. 1995.; Letter dated May 4, 1995; The Procter & Gamble Company; NonClassified Information 

3. U.S. Department of Energy/Energy Information Administration (DOE/EIA) 1995. 

Residential Energy Consumption Survey: Household Energy Consumption and 

Expenditures 1993. DOE/EIA-0321(93) October. 

4. Ibid. 

5: U.S. Department of Energy/Energy Information Administration (DOE/EIA), 1997. 

Annual Energy Outlook 1998. DOE/EIA-0383(98) December. 

6. Electric Edison Institute, 1997. Binder and spreadsheet by Steve Rosenstock, presented 

to DOE Advisory Committee on Appliance Standards, Subcommittee on Economic 

Analysis,_ September 26, 1997, and subsequent updates. 

7. American Gas Association, 1997. Spreadsheet provided by Kirstie Kummer. 

8. · American Gas Association, 1998. Spreadshe~t provided by Kirstie Kummer 

9. Association of Home Appliance Manufacturers (AHAM) comments to the Department of 

Energy, December 2, 1997; Docket No. EE-RM-94-403, Comment #83. 

10. Ernst & Young LLP 1994. Ernst &Youngs 1994.National Water and Wastewater Rate 

Survey. Washington, D.C.: Ernst & Young. 

11. Dietemann, A. 1995. Spreadsheet with title:Ernst & Young 1994 National Water and 

Wastewater Rate Survey Population Weighted Average Commodity Charge 

12. Whirlpool Corporation co~ents to the Department of Energy, December 12, 1990, 

Docket No. CE-RM-90-201, p. 1&2, Attachment #1 

7-35 
CHAPTER 8: NATIONAL ENERGY IMPACTS 

8.1 INTRODUCTION 

This chapter describes the method for estimating the quantity and value of future national 

energy savings (NES) from possible standards. The two metrics discussed in this chapter are: 

• National Energy/Water Savings (section 8.2), and 

• Net Present Value (NPV) of Energy/Water Savings (section 8.3). 

All calculations are performed on a series of Microsoft Excel spreadsheets which are accessible over 

the Internet Basic outputs from the spreadsheets calculations are discussed in section 8.4. A more 

detailed set of results is available in Appendix D. Access to and basic instructions for the 

spreadsheets are discussed in section 8.5. In addition, the methodology the Department will use to 

determine indirect employment impacts is discussed in section 8.6. · 

8.1.1 Methodology and Definitions 

Among the important drivers of energy consumption of clothes washers are: (1) shipments 

of clothes washers, (2) voluntary programs promoting higher energy efficiency products, and (3) how 

consumers respond to any change in price or operating expense. The number of clothes washers 

purchased in future years (shipments) are an important component of any estimate of future impacts, 

and several alternate methods for projecting future shipments are described. Voluntary programs may 

increase the share of energy efficient products, prior to the implementation date of any new standards, 

and their potential impact is accounted for. If clothes washer prices are increased in response to new 

standards, then shipments may be influenced. The percent change in shipments due to a percent 

change in price is the price elasticity of demand for clothes washers. Similarly, as standards reduce 

pnergy consumption, operating expenses decline, which may also influence the number of washers 

purchased. Preliminary results are also described here including: energy consumption by fuel typ~, 

monetary value of energy savings, increased purchase prices, and the net present value (difference 

between value of energy savings and increased purchase prices). 

8.2 NATIONAL ENERGY/WATER SA VIN GS (NES) 

8.2.1 Definition 

· This section provides the definition of national energy savings. 

1 Water heaters or clothes dryers may use one of several fuel types (e.g. electricity, natural gas, LPG (propane), 

and--for water heaters--distillate oil). 

8-1 
. National annual energy savings are calculated as the difference between two projections: a 

base case (without new standards) and a standards case. Positive values of NES correspond to 

energy ~avings, that is, energy consumption with standards is less than energy consumption in the 

base case. 

(1) 

Cumulative energy savings are the sum over some period (e.g., 2000-2030) of the annual national 

energy savmgs. 

(2) 

The national annual energy consumption is calculated according to the following equati011: 

(3). 

The national water consumption is calculated according to the following equation: 

(4) 

For the above expressions, the quantities are required: 

AEC = 

AWC = 

Ilcycte = 

NES = 

STOCKv = 

UECv = 

Annual energy consumption each year (Quads), summed over vintages 

of clothes washer stocks, STOCK.y. 

Annual water consumption each year (I 03 gallons), summed over 

vintages of clothes washer stocks, STOCKv. 

Cycles per year (e.g., loads of laundry) 

Annual national energy savings (Quads) 

Stock of clothes washers (millions of units) of vintage V surviving in 

the year for which annual energy consumption is being calculated. 

Vintages range from 0 (new)- to 16-yeais old. 

Energy consumption per clothes washer per cycle (kWh electricity, 

MMBtu gas or oil). [NOTE: electricity consumption is converted 

from site energy (kWh) to source energy (Quads) by applying a time 

8-2 
·uwev = 

v = 

y = 

8.2.2 NES Inputs 

dependent conversion factor (I3tu/kWh). See quantity ·~rc_conv" 

below in discussion of AEC.] 

Water consumption per clothes washer per cycle (gallons per cycle) ' ' 

Year in which the washer was. purchased as a new unit. 

year in the forecast (e.g., 2000-2030) 

This section provides information about the quantities and assumptions used to calculate 

national energy/water savings for clothes washer standards. For each quantity, the discussion 

includes: 

• definition . 

• approach 

• current assumption 

The inputs into the NES are listed in the table below. 

8.2.2.1 

Definition 

Table 8.1 National Energy/Water Saving Inputs 

Input 

National Annual Energy Consumption (AEC) 

National Annual Energy Savings (NES) 

Source conversion factor (src_conv) 

Stock of clothes washers (STOCKv) 

Cycles per year (Ilcyc1

.) 

Energy per Cycle (UEC) 

Water per Cycle (UWC). 

Shipments 

Effect of Voluntary Programs 

Elasticities 

Annual Energy Consumption (AEC) 

· National energy consumption associated with residential clothes washers and dryers. 

8-3 
Approach 

National energy consumption is the product of energy consumption per washer times the 

number of clothes washers of each vintage. · Tiris approach accounts for differences in unit energy 

consumption from year to year. Annual energy consumption per clothes washer is calculated as the 

product of energy per cycle times number of cycles per year. 

AEC =. L STOCKv * (UEC * ncyc1e) (5) 

Current Assumptions 

Energy consumption is calculated at the site (e.g., electricity in kWh consumed in the 

household). Primary energy consumption is calculated from site energy consumption by applying a 

conversion factor to account for losses, such as losses in generation, transmission and distribution of 

electricity. SeeSource Conversion Factor, below. 

8.2.2.2 National Energy Savings (NES) 

Definition 

Energy savings attributable to the new standards. 

Approach 

Energy savings are calculated as the difference between projected energy eonsumption in the 

Base Case (having no new standards) and the projected energy consumption in the Standards Case. 

Current Assumptions 

Simple subtraction between two projections. 

8.2.2.3 Source Conversion Factor (src_conv) 

Definition 

For electricity, this is the factor by which site kWh is multiplied to obtain primary (source) 

Btu. The source conversion factor accounts for losses in generation, transmission and distribution. 

For natural gas, this is the loss in transmission and distribution .. 

Approach 

After calculating energy savings at the site, multiply those site energy savings by a conversion 

factor to obtain primary energy consumption, usually expressed in Quads (quadrillion Btu). This 

conversion permits comparison across fuels by taking account of the heat content of different fuels 

and the efficiency of different energy conversion processes. 

8-4 
Current Assumptions 

The source conversion factor is applied to site electricity, to convert from site kWh to source 

Btu. This analysis asswnes that the source conversion factor changes over time, and applies annual 

values. The annual values are the U.S. average conversion factors, calculated from AEO 98, Table 

A4 [l]. 

Future Assumptions 

DOE agrees with the Advisory Committees recommendation that the asswnption of a 

constant conversion factor should be dropped in favor of a time series projection of conversion 

factors, changing from year to year. For future conversion factors, DOE proposes to use the 

following method: 

(I) Start with an integrated projection of electricity supply and demand (e.g., the NEMS 

Annual Energy Outlook reference case), and extract the source energy conswnption. 

(2) Estimate projected energy savings due to possible standards for each year (e.g., using 

the National Energy Savings spreadsheet). 

(3) Feed these energy savings back to NEMS as a new scenario, specifically a deviation 

from the reference case, to obtain the corresponding source energy consumption. 

(4) Obtain the difference in source energy consumption between this standard level· 

scenario and the reference case. 

(5) Divide the source energy savings in Btu, adjusted for class specific transmission and 

distribution losses, by the site energy savings in kilowatt-hours to provide the time 

series of conversion factors in Btu per kilowatt-hour. 

Since NEMS can not adjust for class specific transmission losses, this information will come 

from sources outside of NEMS. For residential electricity customers, transmission losses might 

consist of about 3 percent (which basically gets you to a substation) and distribution losses may be 

about 7 percent (substation to house). For the above case, the conversion factor would be the 

marginal plant heat rate times I. I 0. For natural gas, the transmission and distribution losses amount 

to about IO% of primary energy, so primary energy is calculated from site energy by dividing site 

energy by 0.90. 

The base conversions may be based on the maximwn energy savings (e.g., 50% efficiency 

improvement) to avoid noise within NEMS. At small differences in energy savings, the accuracy 

within NEMS is less. NEMS drops the least efficient power producers (cost wise) first, which are 

not necessarily _the least polluting power plants. 

The resulting conversion factors will change over time, and will account for the displacement 

·of generating sources. Furthermore, the National Energy Savings spreadsheet models will include 

a clearly defined column of conversion factors, one for each year of the projection. DOE and 

stakeholders can examine the effects of alternative assumptions by replacing this column of numbers. 

8-5 
8.2.2.4 Stock of Clothes Washers by Vintage (STOCKv) 

Definition . 

Number of clothes washers purchased in a particular year that survive in a later year. The 

vintage is the age of the washer (I-year old up to 16-year old). 

Approach 

The NES spreadsheet keeps track of the number of clothes washers purchased each year. 

Clothes washers are assumed to have an increasing probability of retiring as they age. The probability 

of survival as a :function of years since ptJ.rchase is the survival :function. The lifetime was estimated 

by comparing historical shipments to percent of households currently owning the appliance [2]). 

Example: Base Case cell C51 shows 6.5 million washers purchased as new units in year 2000. Cell 

063 shows 5.6 million of these will still be in use in year 2012. The vintage of these units in year 

2012 is 12 years old (Cell 06). 

Current Assumption 

For clothes washers, lifetimes range from 12 to 16 years, with an average of 14.1 years (see 

Chapter 7, section 7.2.2.2, lifetimes). 

8.2.2.5 Cycles per Year (DcydJ 

Definition 

Loads.oflaundrywashed per household per year. 

Approach 

The DOE test procedure assumes 392 cycles per year. In actuality, the number ofloads of 

laundry washed per household per year depends upon the number of persons in the household, and 

probably on other factors. · 

Current Assumption . 

Assume an average of 392 cycles per year. 

8.2.2.6 Energy per Cycle (UEC) 

Definition 

Energy consumed per cycle. 

Approach 

In accordance with the DOE test procedure (for modified e_nergy factor, MEF), energy 

consumption per cyCie includes electricity used by the clothes washer, and energy used by the water 

heater to provide hot water, and energy used by the clothes dryer to dry the clothing. 

8-6 
. ~ 

The base case forecast of unit energy consumption is comprised of a weighted average of 

baseline efficiency level and 35% efficiency improvement level (35% energy reduction from baseline). 

The relative weight is a user input, namely, the percent of new washers each year that will be 

horizontal axis. 

The unit energy consumption is composed of three components: water heating, machine 

(clothes washer), and dryer energy. Machine energy is electricity. Water heating energy depends 

upon the fuel type of the water heater. Dryer energy depends upon the fuel type of the dryer. (The 

DOE test procedure, Appendix JI assumes a value based on an electric dryer; adjustments were made 

to this value for a gas dryer). 

The unit energy consumption is first calculated for an all-electric household, UE~. For each 

component (water.heat, dryer, and machine), the weighted average is calculated, given the share of 

new washers that are horizontal axis. 

Example: In 1995, 1.5% of new washers are assumed to have an efficiency equivalent to horizontal 

axis. 

Electricity (kWh/Cycle) 

Share 

98.5% (Baseline) 

1.5% (Energy Efficiency 

equivalent to H-Axis) 

Water 

Heater 

1.587 

0.462 

Weighted average 1.570 

Machine 

0.209 

0.133 

0.208 

(See sheet "Base Case", cells AE46, AF46, AG46.) 

Dryer 

1.430 

1.270 

1.428 

The average electricity consumption per new clothes washer depends upon the shares of fuels 

for water heating and for clothes drying. The electricity consumed for water heating (calculated in 

the example above) is multiplied by the percent of households having electric water heating, then 

summed together with the product of electricity per unit for dryer times percent of households having 

electric dryers. Finally the machine energy perunit is added. The result is multiplied by cycles per 

year. 

UECE=(kWh WH*%WHE)+(kWh DR*%DR E)+kWh machine 

Example: Continuing with the example for year 1995, 

= 

= 

= 

(1.570 * 41.81 %) + (1.428 * 79.22%) + 0.208 

0.656 + 1.131 + 0.208 

1.995 kWh/cycle 

(6) 

Annual energy consumption is the product of number of cycles per year and unit energy 

consumption per cycle. In the example for year 1995, AEC:: = I\:ycle * UEC = 392 * 1.995 == 782 

8-7 
kWh/year. (See sheet 'Base Case", cell AI46.) 

Energy consumption for natural gas and for oil are obtained by correcting for the difference 

in recovery efficiency of the water heater, and by converting units from kWh to MMBtu. For natural 

gas, energy consumption per cycle for water heating (expressed in MMBtu) is: 

{kWhWH I 0.15) * (3412Btu/kWh)/ (1,000,000 Btu/MMBtu), 

where the denominator of0.75 is the recovery efficiency of the gas water heater. Gas consumption 

for clothes dryer is 1.12 * kWlfR * (3412Btu/kWh)/( 1,000,000 Btu/MMBtu). 

For distillate oil, energy cbnsumption per cycle for water heating (expressed in MMBtu) is: 

(kWhWH I 0.81) * 3412Btu/kWh/ 1,000,000 Btu/MMBtu, 

\Vhere the denominator of0:81 is the recovery efficiency of the oil water heater. 

Current Assumptions 

Each of the components of energy consumed per cycle is obtained from the data submitted 

by AHAM, for baseline and for each of the efficiency levels. The percent of households having water 

heaters of each fuel type and having clothes dryers of each fuel type is used to calculate the annual 

energy consumption for each fuel type. A weighted average unit energy consumption for new clothes 

washers (including water heat, clothes drying, and machine energy) is calculated for each year of the 

projection, using the percent of new washers that are horizontal axis selected by the user. 

8.2.2.7 Annual Water Consumption (A WC) 

Definition 

National water consumption associated with residential clothes washers. 

Approach 

National water consumption is the product of water consumption per washer times the 

mnnber of clothes washers of each vintage. This approach accounts for differences in unit water 

consumption for year to year. Annual water consumption per clothes washer is calculated as the 

product of water per cycle time number of cycles per year. 

AWC= LSTOCKv *(UWC*ncyc1J (7) 

8.2.2.8 Water per Cycle (UWC) 

Definition 

Water consumed per cycle. 

8-8 
Approach 

Similar to the calculations forthe energy use per cycle, the base case forecast of unit water 

consumption is comprised of a weighted average of baseline water use and water use at the 35% 

efficiency improvement level. · · 

Current Assumptions 

The water consumed per cycle is obtained from the data submitted by AHAM, for baseline 

and for each of the efficiency levels. A weighted unit water consumption for new clothes washers 

is calculated for each year of the projection, using the percent of new washers that are horizontal axis 

selected by the user. · 

8.2.2.9 Shipments 

A key component in determining national energy savings lies in understanding the degree to 

which clothes washer shipments may change in the future. As of 1993, 93% of existing single family 

homes, 84% of existing mobile homes, and 31 % of existing multifamily homes had a clothes washer. 

[3]. Given these saturations the most basic method for forecasting future shipments would simply 

involve assuming saturations remain unchanged.and solving for a growth rate in shipments sufficient 

to keep saturations constant in light of population growth. Such a basic calculation yields a growth 

rate of0.9%, with domestic shipments totaling over 8.5 million by 2030 (as compared with shipments · 

of 6.2 million in 1996). Naturally, however, there are several factors that could make this estimate 

inaccurate. These factors include possiblechanges in: the number of households, saturation levels, 

washer lifetimes, washer prices (including operating costs), andconsumer decisions. about whether 

to repair rather than replace a faulty clothes washer. 

Because of these complexities, and to improve on the simple saturation based method 

described above, four different statistical models were studied in order to better forecast clothes 

washer shipments. Each of the four models and their respective pros and cons are discussed here. 

Auto-Regressive Moving Average (ARIMA) Model 

Under this model analysis of a univariate time series is usedto predict future values. In the 

case of clothes washers, then; only current and past sales data is used to predict future sales under 

this approach. In conducting the univariate time series analysis, a variety of forms of a linear process, 

termed an Auto Regressive-Moving Average (4.RIMA) process, are used to describe the time series 

of interest if the data show no apparent trends and/or seasonal· fluctuations. This approach is 

described by the following equation, where the decimal constants are used to fit the model to 

historical sales: 

cwsa/es(t+ 1) = 0.4196 Z(t) - 0.4372 Z(t-1) + 0.0879 + Cwsaresft) 

where: c~~es(tJ 

Z(t) 

Z(t-1) 

= CW sales in year t 

=CW sales(t) - CWsa1es(t-l) 

= CW sales(t-1) - CW sales(t-2). 

8-9 

(8) 
9500 

9000 

8500 

8000 

7500 

- 7000 

= "" 

= 6500 

= - 6000 -

"" GI 5500 -CIS 

00 5000 

4500 

4000 

3500 

3000 

2500 

1965 

• 

• • ••• 

• 

• • • • • • • 

• • 

•••• ••• • • •• 

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 

Year 

Figure 8.1 ARIMA model sales forecast 

The advantage of the ARIMA univariate approach is that only time series data are needed to 

run the model. The disadvantages of this approach are that (1) historical trends may not be a good 

guide to the future, and (2) the model cannot explicitly account for changes in the number of 

households, percent owning washers, price, or operating expense. ·Under this model, any future 

shipments would not affect shipments. 

Multi-Variate Time Series Fit 

In addition to the ARIMA univariate process for projecting sales, a multi-variate time series 

data analysis was also conducted based not only on clothes washer sales but number of houses as 

well. This approach is described by the following equation, where the decimal constants are used 

to fit the model to historical sales: 

CWsa1eltJ =.CWsa1elt-l) + 93.6708 + 0.6357 · {HousesrocftJ -Housestocft-1)} (9) 

where: C~a1elt) . = Clothes washer sales at time t 

C~a1~(t-l) =Clothes washer sales at timet-1 

HousestocltJ = Housing stock at time t 

Housesiocft-1) =Housing stock at time t-1 

Figure 8.2 shows the sales forecast produced by the multi-variate inethod. Note that the 

models ability to "backcast" and fit actual shipments is shown as the difference between the black 

diamonds (actual shipments) and the black trend line produc~d by the model. 

8-10 
9500 

9000 

8500 

8000 

7500 

~ 7000 

g 6500 

c 6000 

.!! 5500 

~ 5000 

4500 

4000 

3500 

3000 

• • 

2500+-~-.-~--.-~--.-~-.-~--.~~~~..-~-.-~-.-~-r-~-.-~--.~~~--. 

1965 1970' 1975 '1980 1985 1990 1995 2000 2005 2010 2015 2020 2025. 2030 2035 

Year 

Figure 8.2 Multi-variate time series fit sales forecast 

The advantage of the multi-variate time series method is that only two time series are needed 

to build the model (i.e., clothes washer shipments from the previous year and the change in the 

number of households from the previous year). Th~ disadvantages of this approach are that ( 1) again, 

historical trends may not be a good guide to the future, and (2) the model cannot explicitly account 

for replacement sales, changes in saturation, price, and operating cost. 

Saturation/Lifetime Model 

A saturation/lifetime (SIL) model was developed as yet another alternative for forecasting 

sales. The equation for the SIL model is as follows: 

CWsales = House(t)srock · Sat(t)cw - House(t- l)srock · Sat(t-l)cw + CWremovals (10) 

where CW..a1.,,, 

House(t) stock 

Sat(tJcw 

House(t-1) stock 

Sat(t-lJcw 

cw,emovals 

= Clothes washer sales 

= Housing sto.ck at time, t 

= Saturation of clothes washers at time, t 

= Housing stock at time, t-1 

= Saturation of clothes washers· at time, t-1 

= Clothes washers retired from service. 

The SIL model assumes that the saturation of clothes washers varies with time. Clothes 

washer removals are based upon assumptions regarding the distribution of clothes washer lifetimes, 

and the above functional form of the model allows for flexibility in that different assumptions 

8-11 
regarding clothes washer saturations and lifetimes can be used in an attempt to get the best fit to 

historical data. 

Figure 8.3 shows two sets of projections from the SIL model based on slightly differing 

asswnptions regarding saturations and lifetimes. In the top forecast, saturations are estimated 

through the use of a curve fit based on five years of saturation data from past Residential Energy 

Consuinption Surveys (RECS) [4]. So that saturations will not exceed 100%, the high end of the 

saturation curve fit asymptotically approaches 100%. Clothes washer lifetime in this forecast is taken 

9500 

9000 

8500 

8000 

7500 

~. 7000 

g 6500 

= e 6000 

re 5500 "; 

VJ 5000 

. 4500 

• CWsales 

-Sat= 5 point curve fit; Removal= 13.6 avg life 

- Sat= auto-regres:Sive fit; Removal = separate auto & wringe 

4000 • 

3500 

3000 

2500-t--~-.-~-,-~-..-~--.~---,.--~r-~-.-~-.-~~~-..-~--r~~.--~-r-----. 

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 

Year 

Figure 8.3 Saturation/Lifetime model sales forecast 

to be 13.6 years, derived from the best fit of this model to the shipment data.2 In contrast, the lower 

forecast forgoes· the curve fit and uses a saturation estimate based on an auto-regressive fit. Clothes 

washer lifetimes in this forecast are differentiated on the basis of various attributes, namely whether 

they are automatic or wringer washers. 

The advantages of the saturation/lifetime method are that (1) the method explicitly accounts 

for washer lifetimes, (2) housing and saturation stocks are. based only on time-series data, so that 

different housing and saturation fits can be used to get ''good" fits to historical sales. The 

disadvantages of this approach are that (1) removals must be based on asswnptions about lifetimes, 

and (2) the model cannot explicitly account for the impact of price and operating cost on housing and 

saturation stocks. 

2

The 13 .6 years was mathematically determined to give the best fit to historical data. In other parts of the TSD 

lifetimes from MTSC are used (see text). 

8-12 
Accounting Model 

The accounting model seeks to foreeast shipments by determining sales destined for new 

homes plus the additional sales meant to replace clothes washers being retired from service. For 

those sales meant for the replacement market, the model accounts for the impact o~homes which are 

being retired from the existing housing stock. The accounting model is described by the equation: 

CWsales = [Satnew home • Housestarts] + [CW,emovals - (Satstock home . House,etired)] (11) 

where C~a1es = Clothes washer sales 

Sat new home = Saturation of clothes washers in new housing starts 

Housestarts =New housing starts 

CW removals = Clothes washers retired from service 

Sat stock home = Saturation of clothes washers in existing housing stock 

House,etired = Homes retired from existing housing stock 

Clothes washer saturations in new housing starts are projected to have 1 % growth rates up 

to maximum saturations of 100% in single-family homes and 97% in both multi-family and mobile· 

homes. Saturations in the existing housing. stock as of 1980 are assumed to remain frozen at 1980 

. levels of 86.1 % in single ... family homes, .28.4% in multi-family homes, and 65.5% in mobile homes. 

Clothes washer removals are based upon a distribution of clothes wash~r lifetimes (MTSC 1980)(5]. 

The retirement function has a relatively narrow distribution with a mean clothes washer.lifetime of. 

14.1 years. 

Figure 8.4 provides projections of historical and future sales based on the accounting model. 

As the figure makes clear, the accounting model accuratelyestimates sales up to the year 1990 but 

significantly underestimates sales afterthis date. A couple of potential causes for this error are the 

models inability to vaiy: 1) clothes washer lifetime and 2) the saturation of washers in both the 

existing housing stock and new housing starts. With regard to future sales estimates, because the 

assumed range of lifetimes is narrow, sales projections exhibit an oscillatory behavior. 

8-13 
9500 

9000 

8500 

8000 

7500 

~ 7000 

g 6500 

::::, = 6000 . I'll 

~ 5500 CIS 

en 5000 

4500 

4000 

3500 

3000 

2500 

1980 1985 1990 1995 2000 

Figure 8.4 Accounting model sales forecast 

2005 

Year 

2010 2015 2020 2025 2030 

The advantages of the accounting model are that (1) it is a straightforward and simple model, 

(2) it explicitly accounts for new washers separately in new houses and replacements, and (3) price 

and operating costs can be incorporate_d into saturation terms (i.e, in the term Sat new home). The 

disadvantages of the acc01μ1ting model are that (1) saturations of washers in new and stock homes 

must be forecast, (2) housing starts must be forecast (e.g., based on AEO projections), and removals 

must be based on assumptions about life~es. 

8-14 
Figure 8.5 compares the results of the different forecasting approaches discussed here. 

9500 

9000 

8500 

8000 

7500 

~ 7000 

g 6500 

= c 6000 ., 

Col 5500 "; 

v.i 5000 

4500. 

4000 

3500 

3000 

• CWsales 

-Accounting 

-ARIMA 

- - - Time Series 

-Sat/Life 1 

--M-Sat/Life 2 

2500+-~--...~~-.-~~.,--~-,-~~-r-~~.---~--r~~....-~----,~~--.-~~-.--~--, 

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 

Year 

Figure 8.5 Visual comparison of forecasting models 

Table 8.2 shows the degree to which each approach accounts for different variables that 

impact actual shipments. 

I 

Table 8.2 Variables accounted for by different forecast approaches 

Model Variables Accounted For: 

washer sales number of saturation washer lifetime price & 

households operating cost 

ARIMA x 

Multi-variate x x 

Saturation/ x x x x 

Life 

Accounting x x x x x 

In as much as the accounting model is the only approach that takes into account price and 

operating costs, the accounting model is the recommended model for forecasting clothes washer 

shipments in this analysis. The other models do, however, provide important cross-checks to results 

obtained via the accounting model. 

8-15 
·8.2.2.10 Voluntary Programs 

A separate report describes voluntary programs already in place, which are promoting clothes 

washers with higher energy efficiency (see AppendixF)[6]. The impact of these programs has been 

expressed here as the percent of new clothes washers each year that will have efficiencies 

corresponding to those of horizonial-axis washers (35% energy efficiency improvement from the 

baseline washer design). 

The current spreadsheet provides the user with the opportunity to specify the percent of new 

shipments that will achieve 35% energy efficiency improvement (relative to the baseline washer 

design). For illustrative purposes, Table 8.3 shows the values used in the draft spreadsheet. 

-Table 8.3 Percent of Annual Clothes Washer Shipments having -Energy Consumption 

Equivalent to Horizollltal Axis Washers 

Year Percent of shipments 

1995 1.5% 

2000 4% 

2010 9% 

2020 14% 

2030 19% 

8.2.2.11 -Elasticities 

In previous analysis, the future shipments of clothes washers were assumed to depend, at least 

in part, on the purchase price of the clothes washers, and also on the operating expense. According 

to economic principles, increasing the price of clothes washers may decrease shipments. By the same 

principles, d~creasing the operating expense may increase shipments. Previous analysis also indicates 

-that these responses are not equally weighted, that is, the response to price changes is often greater 

than responses to operating expenses. 

One way to express these economic responses is by means of elasticities. An elasticity is the 

percent change in one quantity in response to a percent change in a driving variable. The price 

elasticity of demand for clothes washers is the change in annual shipments (expressed as a percent of 

baseline shipments) corresponding to a percent change in price. For example, if a 10% increase in 

price causes a 2% decrease in shipments, then the price elasticity is -0.2 (i.e.: -2%/10%). 

Previous analysis did not include consumer expenditures for water and wastewater in 

operating expenses. Research is on-going to develop new estimates of price and operating expense 

elasticities to account for: (1) changing the definition of operating expense to include water and 

. 8-16 
wastewater rates; (2) changing the definition of the value of energy savings from average prices to· 

marginal rates; and (3) a longer time series to include more recent data. 

· 8.2.3 Preliminary Results 

See section 8.4. 

8.3 NET PRESENT VALUE (NPV) 

8.3.1 NPV Metric 

NPV is the value in the present time of a time series of costs and savings. Net present value 

is described by the equation: 

NPV=PVS-PVC (12) 

where: PVS = present value of savings ('Total Fuel & Water Savings') 

= L Tota/OperatingCostSavingsY *DiscountFactorY (13) 

PVC = 

y = 

present value of costs ('Total Equipment Cost') 

= L Tota/EquipmentCostY *DiscountFactorY (14) 

Years (from start date of standards to the year when units purchased 

in 2030 retire) 

The net present value is calculated from the projections of national expenditures for 

appliances, including purchase price and operating costs. Costs and savings are calculated as the 

difference between a new standards case and a base case without those new standards. Future costs 

and savings are discounted to the present. 

A discount factor is calculated from the discount rate and the number of years between the 

· 'pre sent" (year to which the sum is being discounted) and the year in which the costs and savings 

occur. The net present value is the sum over time of the discounted net savings. 

8-17 
Current Assumptions 

The assumptions are contained in the terms PVC and PVS, which _are discussed below. Net 

present value (NPV) is the value today of a future stream of savings less expenditures. 

In the NES spreadsheet table labeled "Cost and Net Present Value,""fotal Fuel and Water 

Savings" is the present value of the savings.· 'Total Equipment Cost" is the present value ofincreased 

purchase prices. ''Net Present Benefit" is the difference between 'Total Fuel and Water Savings" and 

'Total Equipment Cost." 'Benefit/Cost Ratio" is the ratio of 'Total Fuel and Water Savings" to 

'Total Equipment Cost." 

8.3.2 NPV Inputs 

This section provides information about the quantities and assumptions used to cii.lculate net 

present value for clothes washer standards. For each quantity, the discussion includes: 

• definition 

• approach 

• current assumption 

Table 8.4 summarizes the inputs to the NPV calculation. 

Table 8.4. Net Present Value Inputs 

Input 

Discount factor 

Net Present Value (NPVY 

Present Value of Costs (PVC)4 

Present Value of Savings (PVSf 

Total Equipment Cosiy 

Total Operating Cost Saving~ 

3 Referred to in the spreadsheet as 'het present benefit." 

4 Referred to in the spreadsheet as "total equipment cost" (discounted). c 

5 Referred to in the spreadsheet as ''total energy and water savings" (discounted). 

8-18 
8.3.2.1 Discount Factor 

Definition 

The factor by which to multiply monetary values in one year, in order to determine the present 

value in a· different year. Discount Factor is also described by the equation: 

Discount Factor= 11(1 + DiscountRate)O'ear-presentyear) (15) 

Approach 

For example, to discount monetary values in the year 2000 to the value in year 1997, 

assuming a discount rate of7% is l/(l.07J or 0.816. 

Current Assumptiqns 

The discount rate is assumed to be 7% real.6 n.e present year is defined to be 1997, for 

consistency with the year in which the manufacturing cost data was collected. In the NOPR the 

present year is expected to be defined as 1998. · 

8.3.2.2 Present Value of Costs (PVC) 

Definition. 

Total equipment cost, discounted to the present, and summed over the time period (from start 

of standards to year 2030). 

Approach 

Costs are typically increases in purchase price associated with the higher energy efficiency 

of appliances purchased in the standards case compared to the base case. Costs are calculated as the 

difference in purchase price for new appliances purchased each year. ('tlel Unit Price'), multiplied 

by the shipments in the standards case 7 ('Total Equipment Costs'). 

Current Assumptions 

The chief assumption made in calculating PVC lies in determining the discount factor to be 

applied. Here the discount factor is taken to be 7% (seealso Discount Factor, above). 

·• 6Used by DOE in previous rulemaking for the National Impact Analysis. Higher and lower values can be used 

as sensitivities (i.e., to bound a range of discount rates). 

7 Counting the reduction in energy consumption from a reduction in shipments as a savings would be 

incorrect. If standards cause a decrease in shipments, then using the lower shipments in the standards case reduces 

the NPVappropriately. To illustrate with an extreme example, if standards cause shipments to be zero, then NPV is 

zero, no matter what. the shipments were in the base case. Using the shipments from the standards case avoids 

miscounting any reduction in shipments due to standards as a savings. · 

8-19 
·8.3.2.3 Present Value of Savings (PVS) 

Definition 

Annual operating cost savings (difference between base case and standards case) discounted 

to the present, and summed. 

Approach 

· Savings ('\:lei Unit Op. Cost') are typically decreases in operating costs associated with the 

higher energy efficiency of appliances purchased in the standards case compared to the base case. 

'Total Operating Cost Savings" is the product of savings per unit times number of units of each 

vintage surviving in a particular year. Appiiances consume energy over their entire lifetime, in some 

cases including energy consi.imed after year 2030. 

Net savings each year .are calculated as the difference between Total Operating Cost Savings 

and Total Equipment Costs. 8 The savings are calculated over the life of the appliance, accounting 

for the energy and water rates each year. 

Current Assumptions 

As with PVC, the chief assumption made in calculating PVS lies in determining the discount 

factor to be applied. Here the discount factor is taken to be 7% (see also Discount Factor, above). 

In addition, see Total Equipment Cost and Total Operating Cost Savings, below. 

8.3.2.4 Total Equipment Cost 

Definition 

Annual change in purchase price (difference between base case and standards case), multiplied 

by shipments in the standards case. 

Approach 

Purchase price per clothes washer in the standards case is subtracted from purchase price per 

clothes washer in the base case for one year. The result is multiplied by the projected shipments in 

that year. 

Current Assumptions 

See section 8.2.2.8 for a discussion of shipments. As noted, the accounting ~odel is currently 

assumed to produce the best shipments forecast. 

8 In the spreadsheet, Total Equipment Costs are expressed as a negative number (the difference between the 

base case and the standards case) then swnmed with Total Operating Cost Savings (the difference between base case 

and the standards case). 

8-20 
• 

---- ----- - ---------- -- 

8.3.2.5 Total Operating Cost Savings 

Definition . 

Annual national operating cost savings, calculated as the difference between total operating 

cost in the base case minus total operating cost in the standards case. 

Approach 

Operating expense per clothes washer in the standards case is subtracted from operating 

expense per clothes washer in the base case for one year. The result is multiplied by the projected 

shipments in that year. Positive valu~s are savings (e.g., operating costs in the standards case are 

lower than in the base case). 

Current Assumptions 

See Cycles per Year and Energy per Cycle as discussed in Sections 8.2.2.5 and 8.2.2.6, 

respectively, and the discussion of energy prices in Chapter 7. 

8.4 PRELIMINARY OUTPUT FORNES & NPV 

The NES spreadsheets offer a range of possible outputs, all of which depend on the 

assumptions used in deriving the results. Table 8.5 details the assumptions used in the NES 

calculations in this analysis. 

8.4.1 Assumptions 

Table 8.5 Assumptions Used for Generating Preliminary National Impacts 

Fuel Price 

Water Price 

Discount Rate and the Year of the NPV 

Start Year for New Standards 

Annual Real Change in Water & Sewer Cost (water 

price escalator) · · 

Manufacturing Cost 

Total Mark up on Manufacturer Costs 

Energy Consumption Data 

Clothes Washer Shipments 

Percent Horizontal-Axis Washers 

Primary Energy Conversion Factors 

EIA Annual Energy Outlook 1998 to the year 2020 

and extrapolated to the year 2030 

Average: $3.18 per 1000 gallons 

Discounted to the year 1997 (in 1997$) at 7 percent 

(real) 

2003 

0 percent 

Shipment-weighted average of the most likely (from 

AHAMdata) 

1.731 

AHAMdata 

Assumed same for standards and base case (inelastic 

to price and energy savings) 

1.5 percent in 1995, increasing by 0.5 percent each 

year 

AE098 

8-21 
Using these assumptions, we highlight preliminary output in key areas below. 

8.4.2 Base Case Shipments 

The results of the Accounting model are used to establish a base case shipment forecast. The 

base case forecast is shown in Figure 8.4. 

8.4.3 National Energy Savings and Net Present Value from Possible Standards 

Preliminary national energy savings and net present value results from the NES spreadsheet 

are shown in Table 8.6. More detailed results are also available in Appendix D. Results are 

cumulative to 2030 and are shown as absolute energy and water savings and as the discounted value 

of these savings in dollar terms. It should be noted that, currently, the NES spreadsheet uses a single 

value for the base case efficiencies rather than a distribution of values. 

Table 8.6 Preliminary National Energy Savings Results (2003 to 2030 cumulative) 

Efficiency Improvement Energy Savings Water Savings Net Present Value (NPV) 

over the Base Case Quads trillion '(billion 1997$) 

gallons 

5% 0.36 0.05 $1.02 

10% 1.18 0.46 $2.41 

15% 2.18 0.45 $3.80 

20% 2.66 0.59 $3.67 

25% 5.09 10.13 $11.07 

35% 7.85 14.62 $13.47 

40% 7.90 14.62 $13.53 

45% 9.49 12.47 $8.81 

50% 10.06 12.47 $9.07 

In this example (see Tables 8. 7 and 8.8), the value of savings and costs are discounted at 7% 

to 1997 from 2003 to 2030 (in billion 1997$). The value of total energy and water savings is $5~20 

billion, the total equipment cost is $1.53 billion, and theresulting net present value is $3.67 billion. 

The corresponding ratio of benefits to costs is 3.40 to 1. 

8-22 

.. 

.• 
Table 8.7 NES Results: 20°/o Improvement (MEF = 1.021) 

Cost and Net i>re5ent Values 

Discounted from 2003 at 7% real to 1997 (in billion 1997$) 

Total Energy & Water Savings 

Total Equipment Cost 

Net Present Benefit 

Benefit/Cost Ratio 

Table 8.8 Efficienc 

rom 2003 to 2010 

to 2020 

to 2030 

0.31 

1.40 

2.66 

0.87 

1.64 

0.11 

0.51 

0.98 

$5.50 

. $1.53 

$3.67 

3.40 

0.02 

0.04 

l 

0.31 

0.59 

Figure 8.6, below, illustrates the typical pattern of national savings and costs resulting from 

standards over time. Th~ heavy line running just below the energy and water savings bars indicates 

the undiscounted net national consumer impact of standards over time. Figure 8.6 shows the nature 

of net savings for a 20% efficiency improvement relative to thebaseline. Alternate standard levels 

would yield different values. 

j 

2000 

1.0 -

0.8 

0.6 -

0 c. 0.4 - r-

"' "' 

0.2 -

0.0 

-0.2 -

-0.4 -

2005 2010 2015 

- - -

2020 2025 

':i f( " ~ ~ ~ ~ 

' fl' 

·~ 

} 

' . , .. 0 ~" < . ~ ~ ~ 

~ ii 

c::::::JPrice Change 

1!!!!!!131 Energy & Water Saving 

--:-Net Saving 

: ! " tf 

..... -

2030 

1 

~ t • i ' t 

Figure 8.6 Net Present Annual Values of a Clothes Washer Standard with a 20% Efficiency 

Improvement 

8-23 
8.5 . USER INSTRUCTIONS FOR SPREADSHEET 

It is possible to examine and even reproduce many of the detailed results obtained in this part 

of the analysis using a Microsoft Excel spreadsheet available on the U.S. Department of Energy 

Office of Codes and Standards web site at: www.eren.doe.gov/buildings/codes_standards/. The NES 

spreadsheet is titled "NES_CWV2.LXLS ." The spreadsheets posted on the web site represenfthe 

latest versions of the applicable models, and have been tested with both EXCEL97 and EXCEL95. 

Basic Operating Instructions: 

1. Once you have downloaded the NES file from the Web, open the file using Excel. At the 

bottom, click on the tab for sheet "NES." 

2. . The screen will display two tables (''Energy Savings and Water Savings" and "Cost and Net 

Present Value') and a chart ("Cost and Savings of New Clothes Washers'). (Use Excels 

commands at the top "View/Zoom" to change the size of the display to make it fit your 

monitor.) 

3. To see the assumptions, click on 'List Inputs." 

4. To change assumptions on 'List Inputs" click on the assumption you wish to change, and 

click on the new asst.imption from the menu. 

5. After selecting each assumption, click on ''List Inputs." 'Cancel" will erase your last set of 

changes to assumptions without recalculating results. 

6. Finally, to see results, click on "Calculate." 

Using these steps you can explore outputs using a range of alternative assumptions. 

Table 8.9 shows the quantities and their locations in the NES spreadsheet 

(NES_CWV2.l.XLS). For example, NES occurs in sheet 'Standards Case", in column AT (annual 

energy savings). 

8-24 

•. 

•. 
-:.. 

Table 8.9 National Energy Saving Quantities and their Locations in the National Energy 

· Savings Spreadsheet 

QUANTITY SHEET COLUMN ROW 

National Annual Energy Consumption (AEC) Ba5e or Standards Case AO-AS 46-106 

National Annual Energy Savings (NES) Standards Case AU 46-81 

Source conversion factor (src_conv) Elec Conversion B 41-76 

Stock of clothes washers (STOCKy) Base or Standards Case D-AA9 46-81 

Cycles per year (Ilc,c1J Setup D 27 

Energy per Cycle (UEC) Engineering G-1 8-17 

Table 8.10 shows the quantities and their locations in the national energy savings spreadsheet 

(NES_ CWV2. l.XLS). . 

Table 8.10 Net Present Value Quantities and their Locations in the National Energy 

Savings Spreadsheet 

QUANTITY SHEET COLUMN ROW 

Discount factor Standards case BA 46-106 

Net Present Value. (NPVY0 (Copy)NES N 21 

(Original) Standards Case AM 87 

Present Value of Costs (PVC) 11 (Copy)NES N 20. 

(Original).Standards Case AM 86 

Present Value of Savings (PVSjl (Copy) NES N 19 

(Original) Standards Case AM 85 

Total Equipment Costy Standards case AY 46-106 

Total Operating Cost Saving; Standards Case AW 46-106 

8.6 INDIRECT EMPLOYMENT IMPACTS 

8.6.1 Introduction 

Indirect impacts are defined as net jobs created or eliminated as a consequence of reduced 

household spending on energy, and possibly increased spending on purchase price of appliances. 

National impacts will be estimated for major sectors of the U.S. economy. Public and commercially 

available data sources will be utilized to estimate employment impacts. Commercially available 

9 These columns may be hidden. To display them, use the Excel command sequence Fonnat/Column/Unhide. 

10 Referred to in the spreadsheet as 'het present benefit." 

11 Referred to in the spreadsheet as "total equipment cost" (discounted). 

12 Referred to in the spreadsheet as "total energy and water savings" (discounted). 

8-25 
software will be utilized. At least three scenarios will be analyzed to bound the range of uncertainty 

in future energy prices. All methods and documentation will be made available for review. 

New appliance standards are expected to reduce household expenditures for energy. The 

Indirect Employment Impacts analysis is intended to estimate national job creation or elimination from 

possible standards, resulting from reallocation of the associated consumer expenditures for purchasing 

and operating appliances. · · 

8.6.2 Assumptions 

Household expenditures for energy exceed $100 billion per year (not counting gasoline 

consumption for transportation). New appliance standards are expected to decrease energy 

consumption, and therefore reduce household expenditures for energy. The same standards may 

increase the p.urchase price of appliances (retail price plus sales tax, and installation). Over time, the 

increased purchase price is paid back through energy· savings. The savings in energy expenditures 

may be spent on other items. Using an input/output model of the U.S. economy, this analysis seeks 

to estimate the effects on different sectors, and the net impact on jobs. 

A simple model might involve reduced expenditures for energy, but reallocation of that money 

toward other expenditures. Even savings of a few dollars per household per year, taken in aggregate 

for the nation, may trigger some changes in employment While the creation of a new industry is not 

anticipated, small increases in jobs in many sectors may occur at the same time that decreases may 

occur for energy supply and appliance manufacturing employment This analysis is intended to 

quantify the indirect employment impacts from appliance standards (direct employment impacts will. 

be evaluated in the Manufacturer Impact Analysis). · 

8.6.3 Methodology 

The existing structure of the economy from 1977 through 1994 has been derived from publicly 

available data. The Department of Energys Office of Building Technologies, State and Community 

Programs has developed a spreadsheet model that focuses on those sectors most relevant to buildings. 

This software (IMBUILD) is a PC-based economic analysis system that characterizes the 

interconnections among 35 sectors as national input-output structural matrices. The model can be 

applied to future time periods. The IMBUILD output includes employment, industry output, and 

wage income. The impacts of new appliance standards as household energy savings (reduced energy 

expenditures) and increased appliance purchase price will be estimated elsewhere (see ''National 

Energy Impacts') and then those changes in expenditures will be introduced to IMBUILD as 

perturbations on the existing economic flows. The net national impact on jobs by sector will be 

estimated. · 

.For example, appliance standards could increase retail prices and reduce household energy 

expenditures. In the short term, this may reduce jobs in appliance manufacturing (for example, 

Household Appliance sector) and energy supply (gas and electric utilities'sectors), while increasing 

8-26 
expenditures on other consumer items, causing job creation in other sectors, especially over the 

longer term. 

. 8-27 
REFERENCES 

1. U.S. Department of Energy/Energy Information Administration (DOE/EIA) 1997. Annual 

Energy Outlook 1998. Washington, D.C.: DOE/EIA-0383(98) December, pp. 106-107. 

2. MTSC Inc./ Management Technology Division 1980.Energy Capital in the U.S. Economy. 

Bethesda, MD: MTSC. November. 

3. U.S. Department of Energy/Energy Information Administration (DOE/EIA) 1995. 

Residential Energy Consumption Survey: Household Energy Consumption and Expenditures 

1993. DOE/EIA-0321(93) October. 

4. Ibid. 

5. MTSC. 1980. Op. cit. 

6. Lawrence Berkeley National Laboratory (LBNL) 1998.Revised Draft Report on Consumer 

Research/or Clothes Washers. Comment No.85. Docket No. EE-RM-94-403 Berkeley. 

April . 3. (Currently available at: 

http://www. erendoe.govlbuildings/codes _standardslnotices/notcOO 12/index. htn} 

8-28 
CHAPTER 9: MANUFACTURER IMPACT ANALYSIS 

9.1 INTRODUCTION 

The manufacturer impact analysis (MIA) estimates the financial impact of standards on 

manufacturers and calculates impacts on competition, employment, and manufacturing capacity. The 

Departments methodology for conducting the MIA for the clothes washer rulemaking is presented 

in detail in Chapter 2, Section 2.8. The MIA will be conducted in three phases: 

• · Phase l consists of two activities, namely, preparation of an industry characterization and the 

identification of issues. 

• Phase 2 has as its focus the larger industry. In this phase, the Government Regulatory Impact 

Model (GRIM) will be used to perform an industry cash flow analysis. 

• Phase 3 involves repeating the process described in Phase 2 (the industry cash-flow analysis) 

but on different sub-groups of manufacturers. Phase 3 also entails calculating additional 

impacts on competition, employment, and manufacturing capacity. 

9.2 PHASE 1: INDUSTRY CHARACTERIZATION 

Prior to initiating the cash-flow analysis, financial data on variables such as tax rate, working 

capital, depreciation, cost of capital, etc., need to be input into GRIM. To obtain estimates for these 

inputs, DOE has analyzed information available from publicly available sources of firm-specific 

financialinfonnation for major clothes washer manufacturers- SEC-I OK Reports, Moody's Company 

Data Reports, Standard & Poor's (S&P) Stock Reports, and Value Line Reports. 

The financial reports of the five major U.S. clothes washer manufacturers were.studied. The 

· top three clothes washer manufacturers by market share, namely, Whirlpool, Maytag and General 

Electric, together accounted for 88 percent of all clothes washers manufactured in the U.S. in the year 

1996. For Whirlpool and Maytag, home appliance manufacturing represents the major portion of 

their business and revenue generation. Hence financial data obtained from a top-down analysis of 

their SEC 10-K reports would be representative of the clothes washer industry (assuming that clothes 

washers are a microcosm of the total major appliance industry). 

GE, however, is highly diversified, and its appliance manufacturing business-GE 

Appliance-accounted for only a small percentage (8%) of GE's total revenues in 1996. Similarly 

Amana (which was a part of Raytheon until recently when it was sold to Goodman Holdings) 

accounted for only about 12 percent ofRaytheon's annual sales (and 6 percent of the profits). Hence, 

like GE, the information obtained fromRaytheon's SEC 10-K reports may not be representative of 

·its appliance manufacturing business (i.e., of Amana's financi~ structure) . 

. Frigidaireis a subsidiary company of Electrolux AB, a European company. Electrolux is the 

world's largest manufacturer of household appliances, such as white goods, vacuum cleaners and 

9-1 
·outdoor products. Most ofElectrolux's revenues come from operations outside North America ( 59% 

from Europe, 6% from Asia, and 6.4% from Latin America) and hence financial data from 

Electrolux's income statements may not be indicative of the business environment in North America. 

Also~ differences in accounting principles for financial reporting purposes (between U.S. and other 

countries) might lead to a different financial structure. 

Value Line and Standard & Poor's (S&P) composite industry statistics for the home appliance 

industry were reviewed. Value Line's statistics for the home applianceindustry are a composite of 

the following companies: Whirlpool, Maytag, Fedders Corp., Black & Decker, Toro, and National . . Presto Industries. In S&P reports, appliance manufacturers are included in the "Household 

Furnishingsand Appliance" industry. The S&P statistics are a composite of the following companies: 

Armstrong World, Basset Furniture, Fedders Corp., Maytag, Mohasco, Roper, Rubbermaid, 

Whirlpool, White Consolidated Industries, and Zenith Electronics. Given the composition of the 

industry-composites, Value Line's industry statistics are possibly more representative of the clothes 

washer industry. 

. . 

The tables in the following sub-·sections present historical financial data obtained from the 

sources previously described. · 

9.2.1 Tax Rate 

Firms pay an annual tax, levied by the Federal and state governments, based on income. 

Taxable income and the income taxes paid are presented as separate line items in a company's income 

statement. Table 9 .1 presents the effect_ive income tax rates for two major appliance manufacturers 

over the period 1990-96. It also presents the industry average tax rate which is obtained by weighting 

effective income tax rates of four clothes washer manufacturers by their clothes washer shipments. 

This information was obtained from the manufacturers' income statements, by dividing the income 

taxes paid by the taxable income. The tax rate for the industry composite, as reported by Value Line, 

is in close agreement with that for the industry average. 

T a bl e 91 . A vera~e mcome tax rates 

Manufacturer Avg. Tax Rate (1990-96) 

A 46.5% 

B 45.4 % 

Industry Average 42.8% 

Value Line 40.0% 

The Department proposes using an income tax rate of 42.8% as an input to GRIM. IQ.is 

value is based on the industry average tax rate. . 

9-2 
9.2.2 Working Capital 

Working capital is defined as the differen~ between a firm's short-term (or current) assets and 

short-term (or current) liabilities. It is the part of a company's capital that must be-maintained in 

order to undertake day-to-day operations of the company without experiencing liquidity problems. 

Because cash inflows and outflows are not synchronized, a firm needs to maintain access to cash 

either from its own liquidity portfolio, or from back-up liquidity. This capital cannot be used in the 

investing activities of the company. 

Table9 .2 presents average working capital maintained by two major appliance manufacturers 

as a percentage of their annual revenues. Data has been averaged over the period 1990-96. ·Working 

capital data is also presented forthe home appliance industry composite as reported by Value Line. 

Working capital is calculated by subtracting current liabilities from current assets of the firm, where 

the current assets and liabilities are obtained from the firm's annual balance sheets. 

Table9 . 2 A vera2e W or ki nj? C . ap1ta I as a p ercenta2e o fR e venues 

Manufacturer Working Capital as % of 

Revenues 

(1990-96) 

A -0.70 

B 16.09 

Value Line 6.76 

Clearly there exists considerable variability in the working capital requirements of various 

firms based on information obtained from SEC 1.0-K reports. Discussions With appliance industry 

analysts indicate that working capital requirements are in the 7-16% range. Based on this 

information, the Department will assume working capital to be 11.5% (average of 7% and 16%) of 

revenues for input into GRIM. 

In general, companies will strive tolower their working capital needs to the lowest amount 

possible so as to free up as much cash flow as possible for revenue-generating investing activities. 

According to Standard & Poor's, in the last few years improved working-capital management 

techniques have greatly reduced the investment that might otherwise have been required. 

9.2.3 Selling, General and Administration (SG&A) 

Table 9.3 presents average SG&A expenses for two major appliance manufacturers as a 

percentage_oftheir annual revenues. The data has been averaged over seve~ years, 1990 through 

1996. SG&A information is presented as a line item in individual manufacturers' income statements, 

presented in separate SEC 10-K reports. 

9-3 
Table 9 3 A . veraee SG&AE xpense as a p ercen t aee o fR e venues 

Manufacturer SG&A as % of Revenues 

(1990-96) 

A 18.43 

B 17.01 

Selling costs include any expenseor class of expense incurred iii selling or marketing. In a· 

manufacturing firm, selling costs include both marketing (order getting) costs and logistics 

( order-filling)costs. Marketing costs include market research, advertising, point-of-sale promotions, 

and salespersons' compellSation and travel costs. Logistics costs include warehousing and delivery 

costs as well as record-keeping costs associated with processing an order. 

General and Administrative(G&A) costs are expenses incurred for the general direction of 

the enterprise. G&A costs include the cost of service and staff units (such as the personnel office) 

~d general corporate costs, including compensation of top management and donations to charitable 

organizations. 

The Department proposes using a value of 18.0% (revenue weighted average of the two 

manufacturers in Table 9.3) of revenues for SG&A expense.as an input to GRIM. 

9.2.4 Research and Development (R&D) 

Research and development (R&D) costs are costs incurred for the purpose of develpping new 

or improved goods, processes or services. The results of R&D efforts are increased revenues or 

lower costs. Since these results will not occur until future periods, often five or more years after the 

research project is started, a case could be made for capitalizing R&D costs and amortizing them over 

the benefits period. However, this practice would be counter to the Generally Accepted Accounting 

Principles(GAAP) promulgated by the Financial Accounting Standards Board (F ASB). Instead, it 

requires that R&D costs be treated as period costs-that is, charged off as an expense of the current 

period. The reason given by the F ASB for its requirement is that by their very nature, the future 

benefits to be derived from current R&D efforts are highly uncertain. 

Table 9.4 presents the R&D costs incurred by two major appliance manufacturers as a 

percentage of their annual revenues. The R&D cost data has been averaged over the past four years, 

1993 through 1996. 

9-4 
Tabl e 9 4 A . vera2e R&DC osts as a p ercen t a2e o fR eve noes 

Avg. R&D Cost as % of 

Manufacturer Revenues 

(1993-96) 

A 2.01 

B 1.50 

Data for the period 1990-1992 is not available as the separate disclosure of research and 

development expense is a relatively recent requirement. Formerly, most companies included this 

expense as a part of general and administrative expenses. Because the amount spent on research and 

development can provide an important clue as to how energetic the company is in keeping its 

products and processes up to date, FASB requires that this amount be reported separately if it is 

material. 

Based on the information in Table 9.4, the Department proposes using R&D expense as 

1.80% (revenue weighted average of the two manufacturers in Table 9.4) ofrevenues for input into 

GRIM. . 

9.2.5 Depreciation 

Plant and manufacturing equipment assets have limited useful lives; that is, they will provide 

service to the firm over a limited number of future accounting periods. A fraction of the cost of these 

assets· is therefore properly chargeable as expense .in each of the accounting periods in which the 

assets provide service to the firm. The accounting process for this gradual conversion of plant and 

equipment capitalized cost into expense is called depreciation. 

Table 9 .5 presents average depreciation expense for two major appliance manufacturers as 

a percentage of their annual revenues. The data has been averaged over seven years, 1990 through 

1996. Depreciation is presented as a separate line item in the manufacturers' income statementsis 

presented in SEC 10-K reports. 

Table 9.5 A vera2e D eprec1a . ti on E xpense as a p ercenta~e o fRevenue 

Manufacturer Depreciation as % of 

Revenues 

(1990-96) 

A 3.54 

I 

B 3.43 

Value Line 3.66 

9-5 
Based on information in Table 9.5, the Department proposes using depreciation expense as 

3.~1% (revenue weighted average of the two manufacturers) ofrevenues for input into GRIM. 

9.2.6 Capital Expenditures 

These are the expenditures made for additions to fixed assets, like plant, equipment, etc., 

intended to benefit future accounting periods (in contrast with other expenditures that benefit the 

current period). These expenditures are aimed at increasing the capacity, efficiency, life span, or 

economy of the operation of an existing fixed asset, or, in some cases, to build entirely new assets. 

Table 9.6 presents average capital expenditures for two major appliance manufacturers as a 

percentage of their annual revenues. The data has been averaged.over seven years, 1990 through 

1996. 

Table 9.6 A vera2e C api "t a IE xpen 1 res as a p ercen t age o fRevenues 

Manufacturer Capital Expenditures as 

% of Revenues 

(1990-96) . 

A 4.47 

B . 4.33 

Based on information in Table 9 .6, the Department proposes using capital expenditures as 

4.43% (revenue weighted average of the two manufacturers) of revenues for input into GRIM. 

9.2. 7 Cost of Capital/Discount Rate 

Cost of capital is a measure of a company's cost of using capital that it raises either through 

loans (i.e., debt) or by issuing equity. It is the minimum rate of return that must be earned on new 

investments that will not dilute the equity of the shareholders. This rate of return is often referred 

to as the discount rate, hurdle rate, or the opportunity cost of capital. 

The company cost of capital is a weighted average of the returns that investors expect from 

various debt and equity securities issued by the firm. It is calculated as the cost of debt capital (which 

is then adjusted for the income tax effect of debt financing) plus the cost of equity capital, weighted 

by the relative amount of each in a company's capital structure. For the purpose of characterizing 

financial impacts of regulations omnanufacturers using GRIM, the Department proposes using the 

company cost of capital to discount future cash flows for arriving at the clothes washer.industry 

value. · '' 

9-6 
In order to calculate the company cost of capital, it is essential to identify a company's cost 

of raising debt, cost of raising capital through issuing equity, and the capital structure of the company. 

Capital structure of a company gives information about the amount of debt versus the amount of 

equity in the company. 

Cost of debt: Cost of debt is a weighted average cost of debt based on all forms of debt financing 

outstanding. Information on the amount of debt outstanding and the interest rate at which it was 

issued was obtained from SEC 10-K financial statements and Moody's reports for two publicly traded 

appliance manufacturers. 

Table 9. 7 presents the nominal1 cost of debt and debt ratios for two appliance manufacturers. 

The cost of debt is calculated in two ways: 

(a) As the weighted average cost of all debt based on the coupon yield, which is the yield that a 

company promises to pay on a bond's face value at the time of issuance of the debt. This 

weighted average represents a company's nominal cost of debt capital from the time it issued 

the debt to the present time. 

(b) As the weighted average cost of all debt based on yield to maturity. Yield to maturity 

determines the rate of return an investor will receive today if a long-term bond is held to its 

maturity date. This yield represents the nominal cost of debt to a company if it needs to raise 

debt capital at the present time. 

Appendix E presents cost of debt calculations using both approaches (a) and (b ). While 

regulators often use an average stated actual cost of debt (as calculated in approach (a)) to determine 

cost of debt [I], it is preferable to use the current replacement cost of debt (market value of debt as 

·calculated by approach (b )) for calculating the cost of capital for the purpose of assessing the present 

value of the future cash flows or for evaluating alternative investment proposals of financial policies 

ifthe policies or proposals involve debt financing.[!] Cash flows associated witlnew investments 

should be matched with the current or prospective cost of all capital associated with such 

investments. 

Debt ratio is defined as the ratio oflong-term debt to total long-term capital (i.e., long-term 

debt plus equity), and illustrates a company's capital structure. 

Table 9.7 Cost of Debt and Debt Ratio 

Manufacturer Cost of debt Cost of debt Debt Ratio 

(Coupon Yield) (Yield to Maturicy) (1990-96) 

A 8.27 6.92 0.38. 

B 9.01 7.07 0.49 

1

Nominal cost implies that the cost has not been adjusted to take account of inflation. 

9-7 
The real cost of debt is obtained by adjusting the nominal value for inflation. Ave.rage annual 

inflation in the U.S. overthe period 1990-96 was 3.42% [2], and more recently, it was 2.2% [2] in 

the year 1997. For Manufacturer A, 8.27% represents the average nominal cost of debt that it raised 

· through corporate bond offerings at different points in time over the past 10 years, a time frame 

during which the interest rates and inflation rates have been varying; whereas, 6.92% represents the 

nonllnaI cost of debt if Manufacturer A raised its debt in 1997 when both interest rates and inflation 

were low. In the long run, for raising debt required to meet regulations, it could be assumed that 

Manufacturer A's nominal cost of debt would fall between 7% and 9% provided there are no sharp 

changes in interest rates. The same argument holds for Manufacturer B, which would also be able 

to raise debt at the 7-9% nominal rate. According to Statements of Management Accounting, cost 

of debt based on yield to maturity would provide a better estimate. However, it is difficult to estimate· 

the exact costs at which these companies could raise debt at some point of time in the future. 

Another way of looking at corporate debt rates is by treating them as spreads (basis points) 

over the Treasury bill rates. Both manufacturers have similar bond ratings (Baal as provided by 

Moody's). Looking at historical annualized return spreads for corporate bonds, both manufacturers, 

with theirBaal ratings, would be able to raise 30-year debt instruments at approximately 120 basis 

points above U.S. Treasuries [3]. Looking at current interest rates, this would mean a nominal cost 

of debt in the range of 7.3% for 30-year debt instruments. Adjusting for an inflation rate of2.2% in. 

1997, the real cost of debt would be around 5%. 

Ibbotson Associates, a company that publishes industry specific financial infonnation on a 

quarterly basis, reports a nominal cost of debt of 7 .14 %2 for the home appliance industry for the year 

1997. The July 1991 AHAM report, entitled "Financial Impact of DOE Top Loading Horizontal Axis 

Standards on Washing Machine Manufacturers," used a nominal cost of debt of 9 .2%. Adjusting for 

inflation(2.2% in 1997 and 4.2% in 1991), the real costs of debt as reported by Ibbotson Associates 

and the AHAM report would be 4.94% and 5.0%, respectively. 

Based on arguments above, the Department proposes to use 5% as the real cost of debt. 

Cost of equity: Cost of equity is the rate of return that equity investors expect to earn on their 

investments in a company's stock. These expectations are reflected in the market price of the 

company's stock. One way of estimating the cost of equity is by using the capital asset pricing model 

(CAPM). The CAPM is based on the premise that the expected or the required rate ofretum on any 

finn'scommon stock equals the riskless rate ofinterest plus a premium for risk. The CAPM is widely 

used by large corporations to estimate their costs of equitY. 

2

Used with permission. ©1998 lbbotSon Associates, Inc. All rights res~rved. [Certain portions of this work 

were derived from copyrighted works of Roger G. Ibbotson and Rex Sinquefield.] 

3

For more information on CAPM the readers are referred to Chapters 7 ,8 and 9 of Corporate Finance by 

Brealy, R.A., and Myers, S.C.; and to article entitled Does capital asset pricing model work by Mullins, D.W., 

published in the Harvard Business Review. · 

9-8 
According to the CAPM, the cost of equity, or, the expected return for equity, is determined 

as: 

cost of equity = riskless rate+ beta* [risk premium] 

where: 

• riskless rate is usually the short-term Treasury bill rate, or the long-term government bond 

rate adjusted for the risk premium over Treasury bills, 

• risk premium is the difference between the expected return on the market portfolio and the 

riskless rate, and 

• beta is a measure of the sensitivity of a company's stock to market movements. 

Table 9.8 presents yields on Treasury bills and government bonds, and the average risk 

premium associated with these securities. Each portfolio offers different degrees of risk. Treasury 

bills are about as safe an investment as· one can make.· There is little risk ofiefault, and their short 

maturity means that the prices of Treasury bills are relatively stable. However, investors cannot lock 

in a real .rate of return; there is still some uncertainty about inflation. By switching to long-term 

government bonds, investors acquire an asset whose prices fluctuate as interest rates vary. (Bond 

prices fall when interest rates rise and rise when interest rates fall.) An investor who switches from 

government bonds to common stocks has a direct share in the risks of the enterprise, and hence 

accepts the additional default risk. Investors who accepted the extra risk of common stocks received, 

on average, a premium of 8.5% a year over the return on Treasury bills. 

Table 9.8 Average Rates of Return (1990-96) and Risk Premiums (1926-94) 

on Some Securities (fi!!ures in oercent 1>er vear) 

Portfolio Average Annual Rate ofReturn4 Average Risk Premiunr 

(Nominal) (Extra return vs. Treasury ' 

bills) 

(1990-96) (1926-94) 

. Treasury bills . 5.00 0 

Government bonds (IO.yr.) 7.06 1.5 

Common stocks 8.5 

(S&P 500 index) 

Table9 .9 presents average values ofbeta for two appliance manufacturers. The data has been 

averaged over the period 1991-96. The values of beta are obtained from financial services provided 

by the Dow Jones Company. 1 

They measure the sensitivity of a stock's price to overall fluctuations 

4

Based on data from the Federal Reserve S~atistical Release. 

~used with permission. ©1998 Ibbotson Associates, Inc. All rights reserved. [Certain portions of this work. 

were derived from copyrighted works of Roger G. Ibbotson and Rex Sinquefield.] 

9-9 
in the S&P 500 stock index. Beta measures the sensitivity of the return on an individual stock to the 

return on the market portfolio. A stock. with a beta of 1 tends to move up and down in the same 

percentage as the market. Stocks with a beta coefficient less than 1 tend to move in percentage terms 

less than the market. Similarly, a stock with a beta that is higher than 1 will tend to move up and 

down more than the market. 

Table9 . 9 A verae:e B eta VI a ues fi or T A WO ,pp r 1ance M anu facturers 

Manufacturer Beta 

(1991-96) 

A 0.94 

B 0.90 

Using CAPM to calculate the cost of equity, the following results are obtained for two 

appliance manufacturers: 

Manufacturer A: Cost of equity= (7.06-1.5) + 0.94 * (8.5) = 13.55 °/o 

[riskless rate] beta * [risk premium] 

Manufacturer B: Cost of equity= (7.06-1.5) + 0.90 * (8.5) = 13.21 °/o 

[riskless rate] beta * [risk premium] 

Adjusting for inflation (averaging 3 .42% per year over the period 1990-96), the real cost of 

equity for Manufacturer A is 10.13o/o and for Manufacturer Bis 9.79%. 

Weighted Average Cost of Capital (W ACC) is defined as: 

WACC = (1-tax rate)* cost of debt *·[debt/(debt+equity)] + 

cost of.equity* [equity/(debt+equity)] 

Manufacturer A: 

Manufacturer B: 

WACC = [(1-0.465) * 5.00 * 0.38] + [10.13 * (1-0.38)] = 7.30°/c, 

WACC = [(1-0.454) * 5.00 * 0.49] + [ 9.79 * (1-0.49)] = 6.33°/o 

The weighted average cost of capital calculated accounts for inflation, as real values of cost 

of equity and debt are used in the calculation. In calculating WACC it is assumed that the two 

manufacturers maintain the samecapital structure in the future, i.e. future debt ratios are the same 

as those in the past (see Table 9.7). 

Independent estimates of the W ACC were obtained from Ibbotson Associates, a company that 

tracks the industry cost of capital for over 300 industries. Their analysis reports a W ACC of 11.6% 

for the household appliance industry (represented by the SIC code 363) in the year 1997. Adjusting 

for inflation (2.2% in 1997), the WACC net ofinflation would b~.4%. This analysis Is a composite 

9-10 
of eight companies: Whirlpool Corporation, Maytag Corporation, Royal Appliance Manufacturing 

Company, National Presto Industries Inc., Rival Company, Health 0 Meter Products Inc., 

Salton/Maxim Housewares Inc., and Windmere-Durable Holdings. 

The July 1991 AHAM report used a cost of debt of9.2% (bond rate) and a cost of equity of 

13% based on average return on equity earned by the Standard and Poor's 500 Industrial Average 

in 1990. Adjusting for tax effects and using a typical capital structure, the AHAM report calculated 

a total cost of capital of 10% before inflation. Adjusting for inflation (which was 4.2% in 1991) the 

AHAM report used 6% as the discount rate for conducting industry cash flow analysis. Table 9. I 0 

compares the cost of debt, cost of equity, and WACC calculated for manufacturers A and B to the 

household appliance industry composite values obtained from Ibbotson Associates and to the values 

used in the AHAM 1991 report. · 

T a bl e 910 . C ompar1son o fF" manc1a . ID aa t 

Item Manufacturer Manufacturer Ibbotson Associates AHAM 

A B Industry composite 1991 Report 

(1997) 

Cost of debt* 5.00 5.00 4.94 5.20 

Cost of equity 13.55 13.21 15.87 13.00 

Capital Structure 0.38 0.49 0.38 -

(Debtffotal Value) 

WACC* 7.30 6.33 9.40 6.00 

Beta 0.94 0.90 0.856 - *Values have been adjusted for inflation. · 

The Department proposes using 7 .25% (revenue weighted average of the WACC of the two 

manufacturers) as the real cost of capital for discounting future cash flows in GRIM. 

9.3 PHASE 2: INDUSTRY CASH FLOW 

The Department proposes to use the GRIM to calculate the financial impact of new standards· 

on manufacturers of clothes washers. The GRIM model will be used to conduct astandard annual 

cash flow analysisthat uses price, quantity, and cost information to assess the impact ofregulatory 

conditions on manufacturer income and cash flow. In Phase 2, GRIM will be used with industry6

Used with permission. ©1998 Ibbotson Asso~iates, Inc.~ All rights reserved. [Certain portions of this work 

were derived from copyrighted works ofRoger G. Ibbotson and Rex Sit:lquefield.). For beta values, Ibbotson Associates 

use their market index as the composite monthly return ofNew York Stock Exchange (NYSE) companies as developed 

by Center for Research in Security Prices at the University of Chicago. 

9-11 
average values of pre- and post-standards manufacturing costs, prices, shipments, and financial 

information to perform an industry cash flow analysis. This process is represented in Figure 9 .1. 

Price 

Quantity 

Cash Flow 

Manufacturing Cost ... 

Financial Information ... 

Figure 9.1 Using GRIM to perform an industry cash flow analysis 

Based on information presented in Section 9 .2, the folloWing table illustrated a representative 

financial structure that will be used for conducting the industry cash flow analysis. 

Scenario Description 

Base Year 

AMouncement Year 

Standard Year 

Tax Rate 

Discount Rate for NPV 

Inflation Rate 

Working Capital 

Standard SG&A 

Research and Development 

Ordinary Depreciation 

Ordinary Capital Expenditures 

Assumptions Page for 

Clothes Washers .. 

Clothes'Washer8 .. 

.. ::;: ·~> ,J9.98 Beginning year for analytical purposes 

· :.,/L'.:·~ .·. ·' >199!) Year in which the regulaJion announcement is made 

+- :. "''L:: , ·: 2002 Year in which the regulation takes effect 

.... :- .'J2.8% •'"'.v .. : ·.'7:25% .. ... 

.. :0.0% per annum 

11.50% of Revenue 

•.18.00% of Revenue 

·. , L80% of Revenue 

3.51% of Revenue 

.. ..4A3% of Revenue 

·Figure 9.2 Summary of Financial Inputs to GRIM 

9-12 
9.4 PHASE 3: SUBGROUP ANALYSIS AND MANUFACTURER INTERVIEWS 

The Department proposes to conduct interviews with manufacturer representatives to gather 

key information on the effects that potential new energy efficiency standards for clothes washers 

might have on their company's revenues and finances, on their direct employment, capital assets and 

industry competitiveness. DOE has developed an interview guide to assist in this process. The 

manufacturer interview guide is presented in Appendix G. This interview guide provides a starting 

point to identify relevant issues and will help identify, both qualitatively and quantitatively, the 

impacts of possible candidate standard levels on individual ·manufacturers or sub-groups of 

manufacturers. 

The interview guide is structured into six different sections: 

(I) Current Organizational Characteristics 

(2) Industry Infrastructure 

(3) Manufacturer Cash Flow Analysis 

( 4) Competitive Impacts Assessment 

(5) Employment Impacts Assessment, and 

( 6) Manufacturing Capacity Impacts Assessment 

Once the interviews are conducted DOE will use the information obtained to identify groups 

of manufacturers representing similar characteristics, e.g. small vs. large manufacturers, full product 

vs. niche players or other relevant characteristics. DOE will collate the completed interview 

questionnaires and prepare a summary of the major issues and outcomes. 

9-13 
REFERENCES 

1. National Association of Accountants. 1994. Statements on Management Accounting, 

Statement Number 4A, Practices and Techniques: Cost of Capital. 

2. U.S. Department of Labor, Bureau of Labor Statistics. Consumer Price Index-A// Urban 

Consumers. {Available at ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt). Washington, 

D.C. 

3. BridgeCorporation Spreads for Industrials, http://bondchannel.bridge.eom/public spreads.cgi; 

and Federal Reserve Statistical Release, http://www~bog.frb.fed.us/ 

releases/h 15/ 

9-14 

'! 
CHAPTER 10: CONSUMER ANALYSIS 

10.1 · CONSUMER ISSUES 

As part of the process of setting a new energy efficiency standard for clothes washers, 

legislationrequires that "the Secretary consider, among other factors, .. .if any lessening of the utility 

or the performance of the products is likely to result from the imposition of the standard (42 U.S.C. 

Sec. 6295 ( o) (2) (B) (I) (IV)). In order to better inform the clothes washer standards rulemaking 

relating to consumer issues, the Department decided to follow the recommendations made by the 

Consumer Subcommittee of the Advisory Committee in December of 1997: 

Adopt a three-step process of obtaining background research, holding focus groups, and 

conducting interviews/surveys. 

The Department first conducted an extensive review of secondary sources of information on 

consumers and clothes washers (see Appendix F). The information was compiled by the Lawrence 

Berkeley National Laboratory and made available at a March 11, 1998, public workshop attended by 

. manufacturers, trade associations, energy efficiency groups, and other stakeholders. 

This study was the subject of a reviewto determine if secondary studies provided adequate 

information to enable the Department to determine the value consumers placed on different clothes 

washer attributes. It was the consensus of the workshop participants that the secondary findings were 

inadequate because the samples contained in the surveys were not representative of the nation, not 

all the relevant clothes washer attributes were covered, and the methodologies of the different surveys 

were not comparable. A Clothes Washer Consumer Analysis Working Group (Working Group) was 

formed representing manufacturers, energy efficiency groups, national laboratories, and other 

stakeholders. In addition to developing a list of relevant clothes washer attributes, the Working 

Group reviewed different approaches such as focus groups, surveys, and a conjoint analysis. (A 

conjoint analysis is research technique that lets the consumer determine the combination of features 

that the product (or service) must have to appeal to them.) 

The Working Group recommended the use of focus groups and conjoint analysis. Results 

from these will be reviewed by the Department to better understand the value consumers place on 

clothes washer attributes, such as cycle options, door placement, temperature options, etc. 

The Department will also be studying purchase price increases to avoid negative impacts to 

" identifiablepopulation groups and to assess the likely impacts of purchase price increases on product 

sales and fuel switching. This information will then be considered by the Department when it 

proposes energy efficiency for clothes washers · 

In order for the Department to conduct a consumer survey and/or focus group, the Paper 

Reduction Act of 1995 requires that the Department seek comments on the proposed information 

10-1 
collection in a Federal Register notice, and seek Office of Management and Budget (OMB) approval 

through submissionofthe form OMB 83-1 upon completion of the comment period of the Federal 

Register notice. 

10.1.1 Focus Groups and Conjoint Analysis 

The DOE will hire a marketing research firm that will collect clothes washer consumer data 

in a two-phase process. In the first phase, the research firm will convene 10 focus groups comprised 

of8-10 individuals (sample of 100 respondents) at five different geographic sites in the U.S. (sites 

·to be determined) to refine the initial list of32 clothes washer attributes that were developed by the 

Working Group. The goal is to refine the initial list to 8-12 attributes for use in a conjoint analysis 

that will be given to a representative sample of 500 respondents at five sites in the United States (sites 

to be determined). Conjoint analysis is a method that permits OCS to identify the value a respondent 

places on a particular attribute of a clothes wash~r. This analysis is accomplished through a trade-off 

procedure in which the respondents are asked to give up various attribute levels to achieve other 

attribute levels. The attributes data are collected through personal interviews with the respondents 

interacting with a pre-pro~ed microcomputer that is provided by the research company. 

10.2 CONSUMER SUB-GROUP ANALYSIS 

The consumer analysis evaluates impacts to any identifiable groups or consumers, such as 

households of different income levels, who may be disproportionately affected by any national energy 

efficiencystandard level. This will be accomplished in part by analyzing the life-cycle cost, payback 

and internal rate of return for those households that fall into any identifiable groups or consumers. 

(Inputs to the spreadsheet used in determining life-cycle-cost and payback periods are explained in 

detail in Chapter 7.) 

10.2.1 Life-Cycle Cost Analysis (LCC) · 

The spreadsheet model used for the LCC analysis can be· used with different data inputs. 

Among the inputs are the data from the 1993 RECS published by· the EIA. The standard LCC 

analysis( described in Chapter 7) includes all the households having both a washer and a dryer. The 

LCC for any subgroup, such as low-income households, could be analyzed by using the LCC 

spreadsheet model by only sampling that subgroup. 

10-2 

,-, 
CHAPTER 11:. UTILITY IMPACT ANALYSIS 

11.1 INTRODUCTION 

Analysisofthe effects of proposed clothes washer standard levels on both the gas and electric 

utility industries will be conducted using a variant of Energy Information Administrations ( EIA) 

National Energy Modelling System (NEMS), togetherwith some minor external calculations. '.fhe 

versionofNEMS to be used for appliance standards analysis is called NEMS-NAECA. NEMS was. 

used by the EIA to produce the I 998 Annual Energy Outlook (AE098). The electricity and gas 

utility analyses will consist of a comparison between model results for the base case and policy cases 

in which proposed standards are in place. Outputs of the utility analysis are presented in a format 

similar to the results in AE098 [1]. 

Assumptions 

The use of NEMS for the .utility analysis offers several advantages. As the official DOE 

energy forecasting model, it relies upon a set of assumptions which are fairly transparent due to the 

exposure and scrutiny the AEO receives. NEMS also offers a picture of the effect·of appliance 

standards since its scale allows it to measure the interactions between the various energy supply and 

demand sectors and the economy as a whole. In addition, the scale of NEMS allows an analysis of 

the effects of standards on both the electric and gas utility industries. 

· The entire utility analysis will be conducted as a policy deviation from the AE098, with the 

same basic set of assumptions applied. For example, the operating characteristics (energy conversion 

efficiency, emissions rates, etc.) of future electricity generating plants are exactly those used in 

AE098, as are the prospects for natural gas supply. 

Deviations from some of these assumptions have also been explored through a few scenarios 

that represent alternative futures. Two of these alternative scenarios are based on the economic 

growth cases of AE098 and use variations in economic growth assumptions, These growth cases 

include a set of both high and low growth assumptions, with the moderate case serving as the 

reference scenario. The growth assumptions for each case are based on macroeconomic forecasts 

prepared by Design Research, Inc. {DRl)/McGraw-Hill and demonstrate the effects of alternative 

growth assumptions on energy markets. The high growth case incorporates higher growth rates for 

population, labor force and labor productivity, which cause lower inflation and interest rates and an 

increased projected economic output of2.4 percent a year. Conversely, the low growth case is based 

on lower growth rates for population, labor force and productivity, resulting in higher prices and 

interest rates and lower growth in industrial output. Economic output in the low growth case 

increases by 1.3 percent a year from 1996 through 2020. While supply-side growth determinants are 

varied in these cases, AE098 assumes the same reference case energy prices for all three economic 

growth cases. 

11-1 
11.2 METHODOLOGY 

NEMS is a large multi-sectoral partial equilibrium model ofthdJ.S. energy sector that has 

been developed over several years by the EIA primarily for the purpose of preparing theAnnual 

Energy Outlook. NEMS produces a widely recognized baseline forecast for the U.S. through 2020 

and is available in the public domain. NEMS-NAECA is the model that is used for appliance 

standards analysis and is based on the AE098 version of NEMS with minor modification$. The 

electric utility industry analysis will consist ofNEMS-NAECA forecasts for generation, installed 

capacity, and prices .. 

Since the AE098 version of NEMS forecasts only to the year 2020, a method. for 

extrapolating price data to 2030 is required. The adopted method uses the EIA approach to forecast 

fuel prices for the Federal Energy Management Program (FEMP). These are the prices used.by 

FEMP to estimate life-cycle costs of federal equipment procurements. For petroleum products, the 

average growth rate for the world oil price over the years 2010 to 2020 is used in combination with 

the refinery and distribution markups from theyear 2020 to determine the regional price forecasts. 

Similarly,natural gas prices are derived from an average growth rate figure in combination 

with regional price margins from the year 2020. 

11.3 RESULTS 

Results will include sales and price forecasts for the electric and gas utilities. Below is an 

example output table of basic NEMS-NAECA results for the AE098 case. Final results will include 

equivalent tables for the AE098 case with residential energy demand reduced by the levels forecast 

by the National Energy Savings spreadsheet models, and all results will be extrapolated to 2030, as 

described above. Further, similar tables will be presented for the alternative scenarios. 

1 EIA approves use of the nameNEMS only to describe an AEO version of the model without any modification tci code 

or data. Since, in this work, there are some minor code modifications and the model is ruri under various policy scenarios that 

deviate from AEO assumptions, Berkeley Lab proposed the use of the name NEMS-NAECA for the model as used here. 

11-2 
Table 11.1 Examele Outeut Table of NEMS-NAECA Results for Utili!l'. Anall'.sis 

NEMS-NAECA: AE098 Baseline 2000 2005 2010 2015 2020 2025 2030 

Residential Consumption 

Electricity Sales (PwhJ 1.16 1.26 1.35 1.45 1.55 1.65 1.77 

Natural Gas (EJ) 5.641 

5.60 5.76 5.97 6.11 6.29 6.48 

LPG {EJ) 0.47 0.50 0.52 0.53 0.54 0.55 0.56 

Oil (EJ) 0.90 0.83 0.81 0.78 0.76 0.73 0.71 

Natural Gas (Quads) 5.35 5.31 5.46 5.66 5.79 5.96 6.14 

LPG (Quads) 0.45 0.47 0.49 0.50 0.51 0.52 0.53 

"' Oil (Quads) 0.85 0.79 0.77 0.74 0.72 . 0.70 0.67 

Residential Prices 

Electricity (¢/kWh) 7.82 7.51 7.30 6.96 6.83 6.83 6.83 

Natural Gas ($/GJ) 6.09 5.86 5.74 5.60 5.77 6.06 6.36 

LPG($/GJ) 12.20 12.88 . 13.12 13.12 13.26 13.73 14.22 

Oil ($/GJ) 7.54 7.88 7.98 8.07 8.13 8.42 8.72 

Natural Gas ($/MBtu) 5.77 5.55 5.44 5.31 5.47 5.74 6.03 

LPG ($/MBtu) 11.56 12.21 12.44 12.44 12.57 13.01 13.48 

Oil {$/MBtul 7.15 7.47 7.56 7.65 7.71 7.98 8.27 1 Trillions of kWh. 

11-3 
REFERENCES 

1. U.S. Department of Energy, Energy Information Administration. Annual Energy Outlook 

1998: With Projections Through 2020. DOE/EIA-0383(98), December 1997. 

11-4 
CHAPTER 12: ENVIRONMENTAL ASSESSMENT 

12.1 INTRODUCTION 

The clothes washer environmental analysis uses a variant ofEIA s National Energy Modelling 

System (NEMS), plus some minor additional analysis. Outputs of the environmental analysis mirror 

the results of AE098[1]," with some additions. 

Purpose of.the Analysis 

The environmental analysis is intended to provide emissions results to policymakers and 

interveners, and to fulfill requirements that the environmental effects of all new Federal rules be 

properly quantified and considered. The environmental analysis considers only two pollutants, SQ 

and NOx, and one emission, carbon. The only form of carbon tracked by NEMS-NAECA is cq, 

so the carbon discussed in this report is only in the form of cq. For each of the standard levels, total 

emissions will be calculatedusing NEMS-NAECA in part, with some external analysis required. 

Assumptions 

The environmental analysis will be conducted as a policy deviation from the AE098, with 

the same basic set of assumptions applied. For example, the emissions characteristics of an electricity 

generating plant will be exactly those used in AE098. 

12.2 METHODOLOGY 

Carbon emissions are tracked in NEMS-NAECA by a detailed carbon module; this gives good 

results because of its broad coverage of all sectors and inclusion of interactive effects. Past 

experience with carbon results from NEMS suggests that emissions estimates are somewhat lower 

than emissions based on simple average factors. One of the reasons for this divergence is that NEMS 

tends to predict that conservation displaces renewable generating capacity in the out years. Or.the 

whole, NEMS-NAECA provides carbon emissions results of reasonable accuracy, a level consistent 

with other Federal published results. 

The two airborne pollutant emissions that have been reported in past analyses, sq and NOx, 

are reported by NEMS-NAECA. The Clean Air Act Amendments of 1990 set an SQ emissions cap 

on all power generation. The attainment of this target, however, is flexible among generators through 

the use of emissions allowances and tradable permits. NEMS includes a module for SQ allowance 

trading and delivers a forecast of SD.z allowance prices. Please note that accurate simulation of sq 

trading tends to imply that physical emissions effects wilbe zero because emissions will always be 

at the cejling. This fact has caused considerable confusion in the past. However, there is an SQ 

benefit from conservation in the form of a lower allowance price and, if big enough to be calculable 

12-1 
by NEMS-NAECA, this value will be reported; One small effect that NEMS-NAECA must consider 

in addition to AE098 calculations is the effect of standards on sq emissions from in-house 

combustion of oil, since the emissions cap does not apply to households. This effect is calculated 

using simple emissions factors. 

NEMS also has an algorithm for estimating NOx emissions from power generation, but it is 

somewhat inadequate for this analysis because it does not estimate in-house emissions. In-house 

emissions account for the combustion of fossil fuels, primarily natural gas, within individual homes. 

Since households that use natural ·gas, fuel oil or coal contribute to NOx emissions, LBNL has added 

a separate household N Ox emissions estimation capability, based on simple emissions factors derived 

from general literature. 

12.3 RESULTS 

As described, the results for the environmental analysis are similar to a complete NEMSNAECA run. Some key results are summarized in the table below. The outcome of the analysis for 

each case are reported as deviations from the AE098 result. All resuits are extrapolated to 2030 and 

include household emissions. 

Table 12.1 Exam~le Ou!]!ut Table ofNEMS-NAECA Results for Environmental Analysjs 

NEMS: AE098 Baseline 1000 2005 2010 2015 2020 2025 2030 

Jn-house Combustion 

. NOx(Mt/a) 0 13.3 16.8 30.8 44.9 73.4 120.0 

Carbon. (Mt/a) 11.1 54.7 88.5 134.7 165.7 226.7 310.2 

$02 (Mt/a) 

Power Sector Emissions 

NOx(Mt/a) 5.05 5.16 5.32 5.52 5.71 5.92 6.13 

Carbon (Mt/a) 1577 1685 1798 1882 1950 2031 2115 

S02 (Mt/a) 10.19 9.69 9.22 9 9 9 9 

S02 Trading Price {$/t} ·120.9 173.l 218.9 181.1 165.2 143.5 124.7 

12-2 
REFERENCES 

l. U.S, Department of Energy, Energy Information Administration. Annual Energy Outlook 

1998: With Projections Tlrrough 2020. DOE/EJA:.0383(98), December 1997. 

12-3 
--------- ------

CHAPTER 13: REGULATORY IMPACT ANALYSIS 

13.1 INTRODUCTION 

DOE will be preparing a draft regulatory analysis pursuant to E.0. 12866, 'Regulatory 

Planning and Review," which will be subject to review under the Executive Order by the Office of 

Information and Regulatory Affairs (OIRA) 58 FR51735 (October4, 1993). Six major alternatives . . 

were identified by DOE as representing feasible policy options to achieve consumer product energy 

efficiency. Each alternative will be evaluated in terms of ability to achieve significant energy savings 

at a reasonable cost and will be compared to the effectiveness of the rule. 

As part of the docket for the Refrigerator Products Energy Conservation Standards (Docket 

No. EE-RM93-801) AHAM stated that the Department needs to improve the evaluation ofnonregulatory means of achieving energy savings. (AHAM, No. 207 at 7). 

Under the Process Rule policies, the Department is committed to continually explore nonregulatory alternatives to standards. Listed ill Table 13.1 below are the non-regulatory means of 

achieving energy savings, proposed for analysis by DOE. The final TSD will include a complete 

quantitative analysis of each alternative, the methodology for which is discussed in brief below. 

Table 13.1 Non-regulatory Alternatives 

-No new regulatory action 

-Consumer tax credits 

-Manufacturer tax credits 

-Performance standards 

-Rebates· 

-Voluntary energy efficiency targets 

-Early replacement 

-Mass government purchases 

13.2 METHODOLOGY 

To compare each alternative quantitatively to .the proposed conservation standards in terms 

of energy savings and net present value (or cost savings), it is necessary to quantify the effect of each 

alternative on the purchase and use of energy-efficient consumer products. In the final TSD (NOPR 

TSD) each alternative to the proposed regulation will be quantified so that inputs to the accounting 

model can be determined, The accounting model will be used to calculate the energy use and the net 

present value corresponding to each alternative. The key inputs to the accounting model will be: 
• energy prices and escalation factors; 

• implicitmarket discount rates for trading·off purchase price against operating expense 

when choosing appliance efficiency; 

• consumer demand and Income elasticities; 

• price versus efficiency relationships for each consumer product; 

• appliance stock data (purchase of new appliances or turnover rates for inventories). 

The key measures of the impact of each alternative will be: 

• ResidentialEnergy Use (EJ = 1018 joule): Cumulative, from the effective date of the new 

standard to the year 2030 for the appliance product Electricity consumption is reported 

as primary energy. DOE agrees with the Advisory Committees recommendation that the 

assumption of a constant conversion factor should be dropped in favor of a conversion 

factor that changes from year-to-year. 

• Energy Savings. Cumulative energy use from base-case projection less alternativepolicy-case projection. 

• Net Present Value. Value of future savings from appliances bought in the period from 

the effective date of the new standard to the year 2030. Calculated as difference: present 

value of equipment and energy expenditures in base case less present value of 

expenditw:es in alternative policy case. Future fuel and equipment expenditures are 

discounted to 1998 using 7% real discount rate. Fuel expenses are calculated for the life 

· · of the appliance. · 

13-2 
APPENDIX A: APPROACH FOR UNCERTAINTY AND VARIABILITY 

A.I INTRODUCTION 

Analysisof an energy efficiency standard involves calculations of impacts~ for example the 

impact of a standard on consumer life cycle cost. In order to perform the calculation, the analyst 

must first: 1) specify the equation or model that will be used; 2) define the ·quantities in the equation; 

'- and 3) prov:ide numerical values for each quantity. In the simplest case, the equation is unambiguous , 

(contains all relevant quantities and no others), each quantity has a single numerical value, and the 

calculationresults in a single value. However, unambiguity and precision are rarely the case. In 

almost all cases the model and/or the numerical values for each quantity in the model are not 

completely known (i.e, there is uncertainty) or the model and/or the numerical values for each 

quantity in the model depend upon other condi~ons (i.e., there is variability). 

Thorough analysis involves accounting 'for uncertainty and variability. While the simplest 

analysisinvolves a single numerical value for each quantity in a calculation, arguments can arise about 

what the appropriate value is for each quantity. Explicit analysis of uncertainty and variability is 

intended to provide more complete informatio~ to the decision process. 

A.2 UNCERTAINTY 

When. making observations of past events or speculating about the future, imperfect 

knowledge is the rule rather than the exception. For example, the energy actually consumed by a 

particular appliance type (such as the average U.S. clothes washer) is not directly recorded, but rather 

estimated based upon available information. Even direct laboratory measurements have some mar~ 

of error .. When estimating numerical values expected for quantities at -some future date, the exact 

. outcome is rarely known in advance. 

A.3 VARIABILITY 

Variabilitymeans that different applications or situations produce different numerical values 

when calculating a quantity. Specifying an exact value for a quantity may be difficult because the 

value depends on something else. For example, the number ofloads oflaundry per week washed by 

a household depends upon the specific circumstances and behaviors of the occupants (e.g., number 

of persons, personal habits about how often clothes are washed, etc.). Variability makes specifying 

an appropriate population value more difficult in as much as any one value may not be representative 

of the entire population. Surveys can be helpful here, and analysis of surveys can relate the variable 

of interest (e.g., laundry loads per week) to other variables that are better known or easier to forecast 

(e.g., persons per household). 

A-1 
A.4 APPROACHES TO UNCERTAINTY AND VARIABILITY 

Two approaches to consider uncertainty and variability are described here: 

• scenario analysis and 

• probability analysis. 

Scenario analysis uses a single numerical value for each quantity in a calculation, then 

changes one (or more) of the numerical values and repeats the calculation. A number of calculations 

are done, which provide some indication of the extent to which the result depends upon the 

assumptions. For example, the life cycle cost of an appliance could be calculated for energy rates of 

2, 8, and 14 cents per kWh. 

The advantages of scenario analysis are that each calculation is simple; a range of estimates 

is used; and crossover points can be identified. (An example of a crossover point is the energy rate 

above which the life cycle cost is reduced, holding all other inputs constant. That is, the crossover 

point is the energy rate at which the consumer achieves savings in operating expense that more than 

compensate for the increased purchase expense.) The disadvantage of scenario analysis is that there 

is no information about the likelihood of each scenario. 

Probability analysis considers the probabilities within a range of values. For quantities with 

variability( e.g., electricity rates in differe~t households), surveys can be used to generate a frequency 

distribution of numerical values (e.g., the number of households with electricity rates at particular 

levels) to estimate the probability of each value. For quantities with uncertainty, statistical or 

subjective measures, such as a triangular probability distribution~ can be used to provide probabilities 

(e.g., manufacturing cost to improve energy efficiency to some level may be estimated to be $10 +/-

$3). . 

The major disadvantage of the probability approach is that it requires more information, 

namely information about the shapes and magnitudes of the variability and uncertainty of each 

quantity. The advantage of the probability approach is that it provides the greatest information about 

the outcome of the calculations, that is, the probability that the outcome will be in any particular 

range is provided. · 

Scenario and probability analysis provide.some indication of the robustness of the policy given 

the uncertainties and variability. A policy is robust when the impacts are acceptable over a wide 

range of possible conditions. 

A-2 
APPENDIX B: CLOTHES WASHER MANUFACTURERS 

Corporation Addre5ses Brands 

Whirlpool Estate 

North American Appliance GE 

Group Kenmore 

2000M63N Kitchen Aid 

Benton Harbor, MI Roper 

49022-2692 Whirlpool 

Phone: (616) 926-5000 

Fax: (616) 926-3622 

General Electric Appliances GE 

GE Appliance Park Hotpoint 

Louisville, KY 40225 RCA 

Phone: (502) 452-4311 

Phone: (800) 626-2000 · 

Fax: (502) 452-0471 

Maytag Admiral -

403 W. 4th St., N. Maytag 

Newton, IA 50208 Admiral Design· 

Phone:(515)792-8376 Crosley 

·Jenn-Air 

Hardwick 

Magic Chef 

Signature 

B-1 
AB Electrolux Flex-Line 

Luxbacken 1, Frigidaire 

Lilla Essingen GE 

S-105-45-Stockholm Gibson 

Sweden Kelvinator Internation 

Kenmore 

Frigidaire Roper 

Frigidaire HQ Tappan 

600 Perimeter Dr. White-Westinghouse 

Dublin, OH 4317 

Phone:(614)792~100 

Fax:(614)792-4079 

Raytheon Amana 

141 Spring St. Caloric 

Lexington, MA 02173 Glenwood 

Phone: (617) 862:.6600 Modem maid . Phone: (800) 843-0304 Roper 

Fax: (617) 860-2172 Speed Queen 

Sunray 

Whirlpool 

Andi-Co AEG {imports) 

Applianees, Inc. 

65 Campus Plaz.a 

Raritan Center 

Ediso~ NJ 08837 

Phone: (908)225-8837 

Fax: (908) 225-8839 

Miele Appliance Inc. Miele 

22D Worlds Fair Dr. 

Sommerset, NJ 0899 ·. 

Phone:(908)560-0899 

Fax: (908) 560-9649 

Miele & Cie/Gmbh 

Postfact 2400 

4830 Guetersloh 

Germany 

B-2 
BSH Home Appliances Bosch 

120 Bosch Boulevard 

New Bern, NC 28562 

Phone: (919) 6364303 

General Domestic Appliances Limited Creda 

Morley Way, Peterborough 

PE2 9JB 

England 

Phone: 44(0) 1733 568989 

Fax: 44(0) 1733 341783 

Staber Industries, Inc .. Staber 

4411 Marketing Place 

Groveport, Ohio 43125 

Phone: (800) 848-6200 

.. 

B-3 
.. 

APPENDIX C: PRELIMINARY LIFE-CYCLE COST AND PAYBACK PERIOD 

·RESULTS 

This appendix contains the complete set of life-cycle cost (LCC) and payback period results 

for all the efficiency improvement levels analyzed for clothes washers. For details on the 

methodology used to calculate the LCC and payback period refer to Chapter 7. 

C.l LIFE-CYCLE COST RESULTS 

The following section contains the preliminary LCC results for all the efficiency improvement . 

levels. For each efficiency level, :frequency and cumulative charts are provided showing the 

distribution of expected LCC differences from the baseline design . 

C-1 
10,000 Trials 

.048 

.036 

~ 

·-

·-

-.Cl .024 

00 

.Cl 

c 

.... . 012 c. 

.000 ~ 

($90} 

Forecast: LCC Difference 

Frequency Chart· 

($68} ($45) 

$ 

($23) 

. 0 Outliers 

477 

llllU-Nl----l- 357. 7 

.,, 

... 238.5-§ " 

" = 

.__--I- 119.2 ~ 

.~~___,_ 0 

$0 

Figure C.15% Efficiency Level: Frequ~ncy Chart ofLCC Differences 

10,000 Trials 

~ 

·--·-

1.00 u-

.75 0 

.Cl .50 0 

00 

.Cl 

c 

~ .25 0 

.000 ~ 

($90} 

Forecast: LCC Difference . 

($68) 

Cumulative Chart 

-

($45) 

$ 

• I 

.• 1111111 

_I Mean= ($19) 

($23) 

............ 

Figure C.2 5% Efficiency.Level: Cumulative Chart of LCC Differences 

C-2 

0 Outliers 

10000 

.,, . 

... 

" .c 

c 

" = 

~ 

..__ 0 ~ 

$0 
10,000 Tria.ls 

Forecast: LCC Difference 

Frequency Chart 0 Outliers 

.057 -r--~~~~~~~~~~~~~~~..---=-~~~---,_555 

.042 

~ 

·-·--.c .028 

o:i 

.c 

= a.. .014 a. 

.000 ~ 

($250) ($181) ($113) ($44) 

-----t- 424.5 . "'Tl 

... 

n 

fffffH------1- 283 .c 

c 

n 

~ 

MIHIH-----+- 141.5 ~ 

$25 

Certainty is 99.49% from -Infinity to $0 $ 

Figure C.3 10% Efficiency Level: Frequency Chart of LCC Differences 

10,000 Trials 

1.00 

.750 

~ 

·-·-

-.c .500 ~ 

.c 

= a.. .250 a. 

.000 ~ 

($250) 

Forecast: LCC Difference 

Cum·u1ative Chart 

($181) ($113) ($44) 

Certainty is 99.49% from -Infinity to $0 $ 

Figure C.4 10% Efficiency Level: Cumulative Chart of LCC Differences 

C-3 

$25 

0 Outliers 

·10000 
Forecast: LCC Difference 

10,000 Trials Frequency Chart O Outliers 

~ 

·-

-.c 

.c = 

Q 

t.. 

c.. 

.069 ..r-----------------,----:--------... 690 

.052-i-----------------

.035+------------------

.000 ..L_ _________ ......,..a111111W 

.. ($450) ($313) ($175) 

"------·- 517.5 

.,, 

...... 

n -------l- 345 .ca c 

n 

= -------+- 172.5 ~ 

$100 

Certainty is 95.92% from -Infinity to $0 $ 

Figure C.5 15% Efficiency Level: Frequency Chart of LCC Differences 

10,000 Trials 

1.000 

.750 

~ 

·--.c - .500 

= .c 

Q 

t.. .250 c.. 

.000 

($450) 

Forecast: LCC Difference 

Cumulative Chart 

($313) ($175) 

Certainty is 95.92% from -Infinity to $0 $ 

$100 

Figure C.6 15% Efficiency Level: Cumulative Chart of LCC Differences 

C-4 

0 Outliers 

10000 

0 

-n 

..... 

n 

.ca 

c 

n 

= 

~ 

.• 
·-

10,000 Trials 

Forecast: LCC Difference 

frequency Chart 0 Outliers 

.072 ~~~~~~~~~~~~~~-.....,,~~~~~~~111 

.054 

~ ·--·-.Cl .036 

co 

.Cl 

0 

L. .018 c. 

.000 ~ 

($600) ($400) ($200) 

~------ 537.7 

-r'! 

~ 

n 

-------r 358.5 ~ 

n 

== fffftlttr------+ 179.2 ~ 

$200 

Certainty is 86.65% from -Infinity to $0 $ 

Figure C. 7 20% Efficiency Level: Frequency Chart of LCC Differences 

10,000 Trials 

~ 

·--·-

1.000 

.750 

.Cl .500 

.Cl = 

0 a: .250 

.000 ~ 

($600) 

Forecast: LCC Difference 

Cumulative Chart 

I -···~ I 

($400) ($200) 

~JdU 

I ,11 

Mean= ($6~).UW 

~ 

$0 

Certainty is 86.65% from -Infinity to $0 $ 

Fig-.re C.8 20% Efficiency Level: Cumulative Chart of LCC Differences 

C-5 

0 Outlie.rs 

10000 

-

0 

$200 

-r'! 

~ 

n 

.c 

c 

C"D 

== 

~ 
10,000 Trials 

.047 

Forecast: LCC Difference 

Frequency Chart 0 Outliers 

471 

~ 

·-·-

-.c 

«:J 

.c 

.Q 

... 

c. 

.035-i----------------+ fllllll-----f- 353.2 

"'Tl 

~ 

n 

.024;---------------:- ffffffffffllfffftt----+ 235.5 ~ 

n 

= 

.012-1-------------tttttttttttttttttltltttHttttttt------r 117. 7 ~ 

.000.L_...-~----..-u-....... llllllWJUWlllllllQWWWWUllllmm~ili::::::::...-1 O 

($1,500)· ($1,063) ($625) ($188) $250 

Certainty is 89.23% from -Infinity to $0 $ 

Figure C.9 25% Efficiency Level: Frequency Chart of LCC Differences 

Forecast: LCC Difference 

10,000 Trials Cumulative Chart 0 Outliers 

1.000·-r---------------~.----:;;;;rnun-1- 10000 

~ 

·--·-.c .500-1---------------....l..lilUUllUWIWUllUUU--l-

«:J 

.c 

Q 

~ .250;--------------:ttttfflttilfffflttttfflfffllfflffff--+ 

.000 ~ 

($1,500) ($1,063) ($625) ($188) 

Certainty is 89.23% from -Infinity to $0 $ 

Figure C.10 25% Efficiency Level: Cumulative Chart ofLCC Differences 

C-6 

' -0 

$250 

"'Tl 

~ 

n 

.c 

c 

n 

= 

~ 
10,000 Trials 

.049 -

Forecast: LCC Difference 

Frequency Chart 0 Outliers 

489 

f-------+- 366.7 

~ . ~ 

·-= ... n ~ .024 tttt--------1- 244.5 ~ 

~ n 

0 = ct .012-t-----------fttfffffffilfffflfltttilffffl..-----+ 122.2 ~ 

.000 ~ 

($2,000) ($1,313) ($625) 

Certainty is 83.42% from -Infinity to $0 $ 

. Figure C.11 35% Efficiency Level: Frequency Chart of LCC Differences 

10,000 Trials 

~ 

·--

1.00 0-

.75 0 

~ .50 0 ~ 

..Q 

C) 

... a. .250 

.000 ~ 

{$2,000) 

Forecast: LCC Difference 

Cumulative Chart · 

·····~1111111 ($1,313) ($625) 

Mean = ($25211111111 

~· 

$63 

Certainty is 83.42% from -Infinity to $0 $ 

Figure C.12 35% Efficiency Level: Cumulative Chart of LCC Differences 

C-7 

$750 

0 Outliers 

10000 

-

0 

$750 

.... 

~ 

n 

.Cl. 

c 

n 

= 

~ 
10,000 Trials . 

.055 

~ 

--

·-.c 

Forecast: LCC Difference 

Frequency Chart 

Cl 

.c 

Q 

~ 

a. .014-1-----------~ 

.000 ~ 

($2,250) ($1,500) . ($750) 

Certainty is 83. 70% from -Infinity to $0 $ 

Figure C.13 40% Efficiency Level: Frequency Chart of LCC Differences 

Forecast: LCC Difference 

0 Outliers 

552 

- 414 

"'Tl 

~ 

n - 276 .c 

c 

n 

= - 138 ~ 

0 

$750 

10,000 Trials c·umulative Chart 0 Outliers 

1.0Q·nJ----------------.-----~=--... 10000 

·~ 

·-

·-

-.c 

Cl 

.Cl 

Q 

~ 

a. 

.500-r----------------hfiltttt11tttt11tttt11ttt 

.250+-------------"TIHIHIHHIHHfftltlfft:Hfff 

Mean= $253 .ooo.L...-~ ---,---...-~~ullllJ,U.UJWJW!IUllJUiiil~~~mmijiif 

($2,250) ($1,500) ($750) $0 

Certainty is 83.70% from -Infinity to $0 $ 

Figure C.14 40% Efficiency Level: Cumulative Chart of LCC Differences 

C-8 

$750 

"'Tl 

~ 

n . .c 

c 

n 

= 

~ 
10,000 Trials 

.052 

Forecast: LCC Difference 

Frequency Chart 

~ 

·---.ca 

~ 

.ca 

Q 

'-

a. 

.039+---------------t 

.026+---------------Hf 

.013+--------------~ 

O Outliers 

520 

.,, 

... 

n - 260 -§ 

n 

= 

----130 ~ 

.000..L .... ~.-----.....,.a..1..&&JIJldWIUl.l,UWUl~WllllblD:IJJmllllDi:qm!C:__,..._J_ 0 

($2,250) ($1,500) ($750) $750 

Certainty is 73.64% from -Infinity to $0 $ 

Figure C.15 45o/o Efficiency Level: Frequency Chart of LCC Differences 

Forecast: LCC Difference 

10,000 Trials Cumulative C~art 0 Outliers 

1.oonr---------------.------:--==::---l.. 10000 

~ 

·-

·-

-

.750+---------------+-----:: 

.ca .500+-------____;_ ______ __ 

.ell = 

Q £t .250-t-------------~fffffffffftttfft 

Mean= $199 .000..L_r-~ ---=------:--~~ulllll:llJlllllWll.μWllllDllllJ~~m'.lil~WWJ.,_J.. 0 . 

($2,250) ($1,500) ($750) $0 $750 

. Certainty is 73.64% from -Infinity to $0 $ 

·Figure C.16 45% Efficiency Level: Cumulative Chart ofLCC Differences 

.C-9 

.,, 

... 

n 

.c 

c 

n 

= 

~ 
Forecast: LCC Difference 

10,000 Trials Frequency Chart O Outliers 

~ 

-·--

.049..r------------------.-~----------,_493 

.037-+-------------- ------- 369.7 

"'Tl 

.... 

n 

.c 

o::i 

.c 

0 

a.. 

.025-t---------------t fffftHfftll---------1- 246.5 "§ 

n 

:::I 

D. ----123.2~ 

.000 ~ 

($2,250) ($1,500) ($750) 

Certainty is 74.16% from -Infinity to $0 $ 

Figure C.17 50%, Efficiency Level: Frequency Chart of LCC Differences 

Forecast: LCC Difference 

$750 

10,000 Trials Cumulative Chart 0 Outliers 

1.oon.r---------------..--------==~-,_ 10000 

~ 

·-

·-

--= .500.;-----------------ttlfftfflfftffllfffffffffllfffffffff--l- o::i 

.c 

0 ct .250.;-:--------------=ff 

.ooo..l_~~--..--...---~...u11WJ.U. 

($2,250) ($1,500) ($750) $750 

Certainty is 74.16% from -Infinity to $0 $ 

Figure C.18 50°./c, Efficiency Level: Cumulative Chart of LCC Differences 

C-10 

"'Tl 

.... 

n 

.c 

c: 

n 

:::I 

~ 
-----------~--- -------- - -- - ----

C.2 PAYBACK PERIOD RESULTS 

The following section contains the preliminary payback period results for all the efficiency 

improvement levels. 

10,000 Trials 

.765 

. -

~ 

·-.ca 

= .ca 

Q 

c.. ""

.574 

.383 

.191 

.000 ~ 

0.0 

Mean= 0.11 

1.0 

Forecast: Payback 

Frequency Chart 

2.0 

years 

3.0 

Figure C.19 5% Efficiency Level: Distribution of Payback Periods 

10,000 Trials 

.615 ·-

.461 

.307 

.154 

11 JMean =0.61 

.000 I 

~ 

0.0 

. 

4.4 

Forecast: Payback 

Frequency Chart 

. 

8.8 

years 

13.1 

Figure C.20 10% Efficiency Level: Distribution of Payback Periods 

C-11 

· 0 Outliers 

7651 

~ 

'4.0 

~ 

17.5 

0 

.,, 

... 

n 

~ 

c 

n 

= 

~ 

O Outliers 

6148 

0 
10,000 Trials 

~ 

·--·-

.717 

.537 

.cl .358 

ti:! 

.cl 

c d: .179 

.000 

I 

~ 

0.0 

Mean= 1.41 

11.3 

Forecast: Payback 

Frequency Chart 

22.5 

years 

33.8 ~ 

O.Outliers 

7165 

-

0 

-rt 

... 

C'D 

.Cl 

c 

C'D 

= 

~ 

45.0 

Figure C.21 15% Efficiency Level: Distribution of Payback Periods 

10,000 Trials 

~ 

·-

-.Q 

o:::i 

.Q 

Q 

a.. 

c. 

.512 

.384 

.256 

.128 

.000 Im 

~ 

0.0 

.I Mean= 3.61 

15.0 

Forecast:. Payback 

Frequency Chart 

30.0 

years 

45.0 

Figure C.22 20% Efficiency Level: Distribution of Payback Periods 

C-12 

60.0 • 

0 Outliers 

5115 

0 

.,, 

... 

C'D 

.c 

c 

C'D 

= 

~ 

•. 
Forecast: Payback 

10,000 Trials Frequency Chart O Outliers 

.054 544 

.041 408 

~ .,, ·- ... - n ·-.ca .027 272 .c ~ c 

.ca n 

... 

C) = 

c. .014 136 ~ 

.000 ~ 0 

8.8 17.5 26.3 35.0 

years 

Figure C.23 25% Efficiency Level: Distdbution of Payback Periods 

Forecast: Payback 

10,000 Trials Frequency Chart OOutliers 

.096 961 

.072 - 720.7 

~ .,, ·- ... - n ·-.ca .048 - 480.5 ~ ~ 

.ca n 

... 

C) =· 

c. .024 240.2 ~ 

.000 ~ 0 

0.0 12.5 25.0 37.5 50.0 

.~ years 

Figure C.24 35°/o Efficiency Level: Distribution of Payback Periods 

C-13 
Forecast: Payback 

10,000 Trials Frequency Chart 0 Outliers 

.115 1151 

;086 863.2 

-~ ""Tl 

·- ... - ni ·-.Cl .058 575.5 -g 

.Cl = ni 

.. 

0 = 

c. 287.7 ~ 

.. 000 ~ 0 

0.0 15.0 30.0 45.0 60.0 

years 

Figure C.25 40o/o Efficiency Level: Distribution of Payback Periods 

Forecast: Payback 

10,000 Trials Frequency Chart 0 Outliers 

.071 712 

.053 . 534 

~ ""Tl 

·- ... - n ·-.Cl .036 356 .c 

= c 

.Cl n 

.. 

0 = 

c. .018. 178 ~ 

.000 ~ . 0 

0.0 13.8 27.5 41.3 55.0 

years ··-

Figure C.26 45% Efficiency Level: Distribution of Payback Periods 

C-14 
Forecast: Payback 

10,000 Trials Frequency Chart 0 Outliers 

.073 732 

.055 549 

~ .,, - ... - n 

.c - .037 366 .c ~ c 

.c n 

... 

0 = 

a. .018 183 ~ 

.000 ~ 0 

0.0 13.8 27.5 41.3 55.0 

years 

Figure C.27 50°/o Efficiency Level: Distribution of Payback Periods 

C-15 
APPENDIX D: NATIONAL ENERGY SAVINGS (NES) SPREADSHEET OUTPUT· · 

The NES spreadsheet (NES _ CWV2. l .XLS) calculates the energy and water. savings by 

comparing the energy and water consumption associated with clothes washers in a base case with that 

in a specified standards case. The following pages contain tables and figures showing the results for 

each of the standards cases assessed in this analysis. For each standards case two tables are 

presented. The first shows cumulative energy savings by fuel type (electricity, gas, and oil) and water 

savings for years 2010, 2020 and 2030. The second shows net present values (including energy and 

water savings and equipment cost) from2003 to 2030 discounted at 7% real to 1997. Benefit and 

cost ratios are. also presented. The corresponding figure for each standard case shows the annual 

benefit/cost pattern of national savings and costs resulting from standards over time. The heavy line 

running just below. the energy and water savings bars indicates the undiscounted net national 

con5umer impact of standards over time. It· is important to note that the net saving shown in each 

plot only captures a snapshot of each year. For example, the benefit of the energy and water savings 

from the equipment expenditure in year 2005actually continues through the lifetime of the clothes 

washers bought in 2005. 

D-1 
Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) 

Total Energy & Water Saving 

Total ~uipment Cost 

Net Present Benefit 

Benefit/Cost Ratio 

from 2003 

to 2010 

to 2020 

to2030 

1000 

-

0.04 

0.19 

0.36 

1005 

0.03 

0.13 

0.25 

2010 

0.2 c::=:I Price Ch an g e 

0.01 

0.05 

0.10 

l!ll!!!'!!!!IEnergy & Water Saving 

0.2 - -Net Saving "" .,,, 

r! .,,, .! 

;; 0.1 ~ - .,,, .,,, ... 

°' .,,, 

-

°' 

i: "" 0.1 ~ 

il5 

0.0 

- "" A11"' 

-0 .1 -

Figure DJ 5% Efficiency Level: Net Saving 

D-2 

1015 

,,,. ,,,. 

0.00 

0.00 

0.00 

.... 

$1.03 

$0.01 

$1.02 

113.23 

2020 

0.05 

0.24 

0.46 

1015 1030 
-.. 

- -- ------- -----------..-------~· 

Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) 

Total Energy & Water Saving 

Total Equipment Cost 

Net Present Benefit 

Benefit/Cost Ratio 

from 2003 

to 2010 

to2020 

to 2030 

0.14 

0.62 

1.18 

0.09 

0.42 

0.78 

0.04 

0.20 

0.38 

0.00 

0.01 

0.02 

$2.50 

$0.09 

$2.41 

27.39 

0.05 

0.24 

0.46 

co·st and)Sa,·ing of Ncw·C.Jothcs W ashcrs 

2000 2005 2010 2015 2020 2025 

o.s - c:=:::JPrice Change 

0.4 l!!l!!!EEnergy & Water Saving - ,....-

--Net Saving .... 

"" 

a: "" 0.3 ~ 

c 

c::i 

- " "" ,... " 

°' 0.2 ~ " - c " " :5 0.1 iii - "" " 

0.0 .Kl -

... - .. .. .. 

-0 .1 -

Figure D.2 10% Efficiency Level: Net Savings 

D-3 

2030 
D.1.515% Efficienc Level: Cost and Net Present Values 

Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) 

Total Energy & Water Saving 

Total Equipment Cost 

Net Present Benefit 

Benefit/Cost Ratio 

from 2003 

to 2010 

to 2020 

to2030 

0.26 

1.15 

2.18 

0.17 

0.73 

1.38 

1000 1005 2010 

0.8 - c:::::=I P rice Change 

0.09 

0.40 

0.77 

0.7 - Eiiii!5!!'i3Energy & Water Saving 

0.6 - -Net Saving 

f ~· 

GI 0.5 - ,. ~ "" Q 0.4 - ,. ... 

°' ,. °' .... 

II: 0.3 - ~ 

5 ,. ' 

ii 0.1 - ,. / 

0 .1 - "" 

0.0 IV 

u ... ~ . ' .. ~ ~ 

-0 .1 -. 

Figure D.315% Efficiency Level: Net Savings 

D-4 

1015 

!-" ~-

~ 

-. .; 

... 

0.00 

0.02 

0.03. 

.,., 

.:i 

$4.i4 

$0.44 

$3.80 

9.67 

1020 

. 1 

0.05 

0.23 

0.45 

1025 

: . 

2030 

~ 

~ 
----------------------- 

Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) 

Total Energy & Water Saving 

Total Equipment Cost 

Net Present Benefit 

Benefit/Cost Ratio 

from 2003 

to 2010 

to2020 

to2030 

0.31 

1.40 

2.66 

0.20 

0.87 

1.64 

2000 2005 2010 

1.0 - c==::JPrice Change 

0.11 

O.Sl 

0.98 

0.8 - !Ei!!!51Energy & Water Saving 

-Net Saving 

0.6 - 1--' ,, 0.4 - ,, ,, ,, 

0.2 ""' ""' 

o.o II 

-0 .2 -

-0.4 -

Figure D.4 20% Efficiency Level: Net Savings 

D-5 

2015 

i; 

.... 

0.00 

0.02 

0.04 

$5.20 

$1.53 

$3.67 

3.40 

2020 

0.07 

. 0.31 

0.59 

2025 2030 

. .. . 
Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) 

Total Energy & Water Saving 

Total Equipment Cost 

Net Present Benefit 

Benefit/Cost Ratio 

from 2003 

to 2010 

to2020 

to2030 

2000 

-

0.60 

2.68 

5.09 

2085 

0.38 

1.67 

3:13 

2010 

0.21 

0.97 

1.88 

3.5 

3.0 

2.5 

-

-

c:::=::J Price Ch an g e 

l'iiEil!lil!!IEnergy & Water Saving 

-Net Saving 

r! 2.0 - ~ 

c ""' 

Q 

1.5 - ii"'" 

°' 1.0 - -

°' 

i: 0.5 - 5 

iiS 0.0 

-0 .5 -

-1.0 -

-1.5 -

Figure D.5 25% Efficiency Level: Net Savings 

D-6 

2015 

""' 

0.01 

0.04 . 

0.08 

$17.57 

$6.50 

$11.07 

2.70 

2020 

. 

1.15 

5.26 

10.13 

2025 2030 
-> 

•, 

------------------.-----

Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) 

Total Energy & Water Saving 

Total Equipment Cost 

Net Present Benefit 

Benefit/Cost Ratio 

from 2003 

to 2010 

to 2020 

to2030 

2000 

-

0.93 

4.14 

7.85 

2005 

0.65 

2.85 

5.35 

2010 

0.27 

1.24 

2.39 

6.0 

5.0 

4.0 

-

c:=:::JPrice Change 

lii&!!!IEnergy & Water Saving 

--Net Saving -

z: 

• 3.0 :; - Cl 

.... 2.0 "' "' .,, -r::: 

5 1.0 - iii 

0.0 

-1.0 -

-2.0 -

· Figure D.6 35% Efficiency Level: Net Savings 

D-7 

2015 

0.01 

0.05 

0.10 

$26.46 

$12.99 

$13.47 

2.04 

2020 

1.66 

7.59 

14.62 

2025 2030 
Discounted from 2003 to 2030 at 7% real to 1997 (in billion 1997$) 

Total Energy & Water Saving 

Total Equipment Cost 

Net Present Benefit 

Benefit/Cost Ratio 

from 2003 

to 2010 

to 2020 

to 2030 

2000 

O.!J4 

4.17 

7.90 

2005 

0.65 

2.87 

5.40 

2010 

0.27 

1.24 

2.40 

6.0 

. 5.0 -

4.0 -

· c::=J Price Ch an g e 

~Energy & Water Saving_ 

~Net Saving 

z: 

m 

~ 3.0 - Q 

r--

°' 2.0 - °' "" ..... ... 

s: 

:! 1.0 - s 

.. "" "" "" I-"" 

0.0 

-1.0 - ~ 

~ .. • 

-2 .o -

Figure D. 7 40% Efficiency Level: Net Savings 

D-8 

2015 

.. ... 

,,; 

0.01 

0.05 

0.10 

$26.56 

$13.04 

$13.53 

2.04 

2020 

1.66 

7.59 

14.62 . 

2025 2030 

·. 
Discounted from ioo3 to 2030 at 7% real to 1997 (in billion 1997$) 

Total Energy & Water Saving 

Total Equipment Cost 

Net Present Benefit 

Benefit/Cost Ratio 

from 2003 

to 2010 

to2020 

to 2030 

1.12 

4.99 

9.49 

0.81 

3.60 

6.80 

0.29 

1.34 

2.59 

0.01 

0.05 

0.10 

$27.70 

$18.89 

$8.81 

1.47 

1.43 

6.50 

12.47 

Cost an-d, Sa.ving-·-of N.ew: .. C lothes"W as hers . --

6.0 

5.0 

4.0 

.f 

.!! 3.0 c 

Q 

..... 2.0 ... 

... 

-1::1 

:5 1.0 

ii 

0.0 

-1.0 

-2 .o 

2000 2005 2010 

-

-

-

-

-

-

c=:::J Price Ch an g e 

IEEi5i\IBEnergy & Water Saving 

--Net Saving 

Figure D.8 45% Efficiency.Level: Net Savings 

D-9 

2015 2020 2025 2030 

~ 
T a e bl D 1 . . 17 SOo/c 0 Effi 1c1encv . L I C t eve: os an dN e tP resen t V a I ues 

Cost and Net Present Values 

Discount~d from 2003 to 2030 at 7% reaJ to 1997 (in billion 1997$) 

Total Energy & Water Saving $28.75 

Total Equipment Cost $19.68 

Net Present Benefit $9.07 

Benefit/Cost Ratio 1.46 

T a bl e D 1 . . 18 SOo/c 0 Effi c1encv . L eve: IE nerl!V an dW a t er S aVIDS?S 

Energy Saving in Quads Water Saving 

Total Elec Gas 

from 2003 

to 2010 1.18 0.87 0.30 

to 2020 5.28 3.85 1.38 

to2030 10.06 7.28 2.67 

2000 2005 2010 

6 .o -

5.0 -

4.0 -

3.0 -

2.0 -

1.0 -

0.0 

-1.0 -

-2 .0 -

-3 .0 -

c:::::::J Price Ch an g e 

• -+>Energy & Water Saving 

-Net Saving 

.. -

.... '."" .; 

- -

Figure D.9 50°/o Efficiency Level: Net Savings 

2015 

... 

D-10 · 

Oil (Trillion 2als) 

0.01 1.43 

0.05 6.50 

0.11 12.47 

2020 2025 

I~ 

- -

2030 

.. 

I~ , , 

- .. 
APPENDIX E: GOVERNMENT REGULATORY IMPACT MODEL (GRIM) 

E.1 . INTRODUCTION AND PURPOSE 

The purpose of the Government Regulatory Impact Model (GRIM) is to.help identify the 

effects of various efficiency regulations and other regulations on manufacturers. The basic mode of 

analysisis to determine the change in value of the manufacturers(s) following a regulation or a series 

::- of regulations. The model structure also allows an analysis of multiple products with regulations 

taking effect over a period of time, and of multiple regulations on the same product. 

Industry value is defined, for the purposes of this analysis as the present value of cash flows 

for the manufacturer(s) in question. Cash flow iscalculated by the user entering shipment volumes 

and manufacturer prices (i.e., prices from manufacturers to the first customer, such as a wholesaler) 

and then selecting user-defined regulatory levels. The model calculates the actual cash flows by year 

and then detenlμnes the present value of those cash flows both without regulations (the pre:-regulation 

. base case) and with regulations (the post-regulation standards case). 

Output comes from the model in terms of summary statistics, graphs of major variables, and, 

when appropriate, access to the complete cash flow calculation. 

E.2 MODEL DESCRIPTION 

The basic structure of the GRIM is a standard annualcash flow analysis that uses price and 

volume information as an input, builds on fundamental base cost information, and accepts a set of 

regulatory conditions as changes in costs andinvestments.The cash flow analysis is separated into 

two major blocks: income and cash flow. The income calculation detennines the profit after taxes 

but before financial charges. The cash flow calculation converts profit after taxe.s into an annual cash 

flow by including investment and non-cash items. Below are definitions oflisted items on the printout 

of the output sheet. (see Section E.3) 

(1) Income Statement: Overall calculation ofNet Income Before Financing (17) 

using revenue and cost items subject to income tax effects 

as described in (2) through (17). 

E-1 
(2) Price/Unit: Actual manufacturer unit price charged to the first level of 

distribution (distributor, retailer, etc.) 

Price of Baseline Unit= $285.90 

Price of higher efficiency unit= Price of Baseline Unit+ 

(Incremental Mfg. Cost)* (Mfg. Markup) 

It can.also be entered as an input by the user 

(3) Unit Sales: Annual shipments for the industry will be obtained from the 

National Energy Savings Spreadsheet. 

Baseline Industry Shipments (1997) = 7.065 million 

It can also be entered as an input by the user 

(4) Revenues: Annual revenues; computed by multiplyingPrice/llnit (2) 

by Unit Sales (3). 

(5) Base Costs: Costs per unit prior to regulations. 

(6) New Costs: Costs per unit following regulations. 

(7) Labor: Factory direct labor and fringe benefit costs; 

Baseline Labor Cost= $29.31 

New Labor Cost = Baseline Labor Cost + 

Incremental Labor Cost 

(8) Material: Purchased materials and components; 

Baseline Material Cost= $148.1 

New Material Cost= Baseline Material Cost + 

Incremental Material Cost 

(9) Overhead: Factory overhead excluding depreciation. Overhead is 

treated as both fixed and variable, with the percentage of 

overhead (excluding depreciation) that js variable set under 

the Major Assumptions. 

Baseline Overhead Cost= $33.5 1 · 

New Overhead Cost= Baseline Overhead Cost + 

Incremental Overhead Cost 

1 Source: "Mark-ups for Clothes Washer Rulemaking Analysis." This document was presented in the Clothes 

Washer Workshop held in March, 1998. 

E-2 

-

. .,, 
(10) Deprecia#on: 

(11) Cost of Sales: 

(12) SG&A: 

(13) R&D: 

(14) Product Conversion: 

(15) Profit Before Tax: 

(16) Taxes: 

(17) Net Income Before 

Financing: 

Annual depreciation on pre-regulation assets; computed as a 

percentage of Revenues ( 4). Annual depreciation on postregulation assets is computed by dividingConversion 

Capital Expenditures (25) by a useful life fromMajor 

Assumptions. For post-regulation years, Depreciation is the 

sum of depreciation on pre- and post-regulation assets. 

Pre-regulation: 

Depreciation= % of annual revenues 

Post-regulation: · 

Depreciation = Depreciation_ Pre-regulation + 

(Conversion Capital Exp./Useful_ life) 

Total cost of sales, CQmputed by adding Labor (7), 

Material (8), Overhead (9) and Depreciation (I 0). 

Selling, general and administrative costs are computed as a 

percentage of Revenues ( 4 ). 

Research and development costs unrelated to regulations; 

computed as a percentage of Revenues ( 4 ). 

Expensable costs related to meeting a regulation, often 

including product redesign costs and expensable factory 

conversion expenses. Also includes costs incurred for new 

product literature and catalogs, product obsolescence, and 

various related marketing expenses. GRIM allocates these 

costs over a number of years. 

Profit before taxes and any financing costs; computed by 

subtracting Cost of Sales (I I), SG&A (12), R&D (13), and 

Product Conversion (14) from Revenues (4). 

Taxes on Profits Before Tax; computed by multiplying the 

tax rate contaitted in Major Assumptions by Profit Before 

Tax (15). 

Profits after taxes; computed by subtracting Taxes 

from Profit Before Tax (15). 

E-3 
(18) Cash Flow Statement: 

(19) Net Income: 

(20) Depreciation: 

Overall assessment including·net income, other cash related 

items and adjustments and investments. 

Net income; identical toNet Income Before Financing 

(17). 

Depreciation is a non-cash cost and is added back intoNet 

Income (19) as part of the cash flow calculation; identical 

to Depreciation (10). 

(21) Change in Working Capital: Additional accounts receivable, inventory, and other cash 

investments necessary to support increased revenues; 

computed by multiplying a percentage fromMajor 

Assumptions by the change inRevenues (4). 

(22) Cash Flow from Operations: The cash flow from operating activities; computed by 

addingNet Income (19), Depreciation (20) and subtracting 

Change in Working Capital (21) . 

. (23) Capital Expenditures: Investment to maintain and replace existing production 

assets; computed as a percentage of Revenues (4) using a 

percentage contained inMajor Assumptions. Postregulation capital expenditures equal pre-regulation 

expenditures plus depreciation onConversion Capital 

Expenditures (24 ). 

Pre-regulation: 

Capital Exp. = % of annual revenues 

Post-regulation: 

Capital Exp. =Capital Exp._Pre-regulation + 

(Conversion Capital Exp./Useful~ life) 

(24) Conversion Capital Exp.: Capital costs for meeting regulations, typically including 

plant, equipment, tooling and the like. These have been 

provided by AHAM for each efficiency level. . 

(25) Cash Used in Investments: Cash required for assets; computed by adding Capital 

Expenditures (23) and Conversion Capital Expenditures 

(24). 

E-4 
(26) Net Cash Flow: 

(27) Post-Regulation Value: 

•, 

Annual cash flow from operations and investments; 

computed by subtracting C.ash Used in Investments (25) 

from Cash Flow from Operations (22). 

Value·ofthe relevant industry following adoption and 

implementation of the regulatory scenario; the present value 

of the annual Net Cash Flow (26) from the base year to the 

year 2012, plus a ~rminal value based on the final years 

cash flow valued as an annuity, are computed by model and 

listed. The discounted rate for the present value is the 

"Discount Rate for NPV'' contained in Major Assumptions. 

Pre-regulation value is computed in a similar way using base 

case Net Cash Flows(26) and terminal value. · 

E-5 
E.3 INCOME STATEMENT 

, '"itif"~4lSlt~ .~ . I Announce l j Standard I Bau Year Year i l Year 

(I) ~ 1998 1999 ' 2000 2001 ' 2002 I· 2003 ' 2004 

(4) Revenues 2,020.1 2,063.S i 2,107.9 2,153.2 j 2,1995 1 2,246.8 ! 2.295.1 ! . : 

(11) Cost of Sales 

(7) Labor 207.0 21151 216.0 220.7 i 225.4 230.3 ! 235.2 ! 

(8) Material 1,050.7 l,073.3 i 1,096.3 1,119.9 ! 1,144.0 1,168.6 ! 1,193.7 j 

(9) Overhead - Fixed 

(9) Overhead - Variable 165.7 1693 ! 172.9 '176.6 ! 180.4 184.3 l 188.21 .• 

(10) Depreciation 70.9 72.4 ! 74.0 75.6 ! 77.2 78.9 ! 80.6 i 

Se/Unit, General and .Administrative 0.035 ! 0.035 0.035 ' 

(12) Standard SG&A 

(13) - R&D 

363.6 371.4 i 379.4 387.6 ! 395.9 '404.4 

36.4 37.1 i 37.9 38.8 i 39.6 40.4 

413.1 _j 

41.3 _, i 

(14) Product Conversion Expense 

(IS) Profit Before Tall 125.8 128.5 i 131.3 134.1 i 137.0 139.9 ! 142.9 

06) Taxes 53.9 S5.0 i S6.2 S7.4 ! S8.6 S9.9 i 61.2 I 

.. I 

(17) Net Income Before Financing 72.0 73.5 i 75.1 I 76.7 i 78.4 i 80.0 l 81.8 i 

l 

72.0 73.5 i 75.1 76.7 i 78.4 81.8 j 

(20) Depreciation 70.9 72.4 ! 74.0 75.6 ! 77.2 78.9 ! 80.6 ! 

(21) !Change in Workinl! Canital (2.9)! (3.0)• (3.l)i (3.1) (3.2)[ (3.3)[ 

142.9 143.0 ! 146.1 ' 149.2 ! 152.4 ! 159.I i 

(89.S) (~_!_:_~}! (93.4ii ~95.~1 (97.4)! 

(89.S) (91.4)! (9;.4)1 (95.4)! (97.4)1 

i 

(26) Net Cash Flow 53.4 51.6 l 52.7 53.8 i 55.0 56.2 ! 57.4 i 

Terminal Value 

Present Value Factor 1.0000 0.9324 l 0.8694 0.8106 l 0.7558 0.7047 i 0.6571 

Discounted Cash Flow S3.4 48.1 ! 45.8 43.6 i 41.6 39.6: 37.7 

(27) Industry Value {Net Present Value) S 905.8 

E-6 
E.4 MAJOR ASSUMPTIONS 

In making the cash flow calculations, the model uses several major assumptions, each of 

which can be modified. The major assumptions are outlined below. 

Base Year 

Announcement Year 

Standard Year 

Tax Rate 

DiscoW1t Rate for NPV 

Inflation Rate 

Working Capital 

Base Year Unit Price 

Base Year Unit Sales 

Unit Sales Growth Rate 

Standard SG&.A 

Research and Development 

Ordinary Depreciation 

Ordinary Capital Expenditures 

Variable Overhead as % of Total 

Standards Case Efficiency Level 

=> Efficiency Improvement of 

Manufacturin Costs 

Materials I Unit 

Labor/Unit 

Overhead I Unit (excl. Depreciation) 

r'.!.'\ftf~;'i/;'..:'::' :'19!18 Beginning yur for analyticlll pwposa 

~>:i\l~ Year in which the regulation fllUIOllllUlllelll is wide @~i!i:\'0 /:;.:2()()2 Year in which the regulation tllka effect 

:;~;,,~;J':'·'"''".42'.So.4 

·'k':~~~~::.:~~;~~~;7.25% 

~>~P·'r: ,:;:0:00A.1na annum 

'{'0'°'0-~:'iU>.:'.6'.76% of Revenue 

!::~:::i>,t'~.,'.285;90; ($) 

"·}:~;<~ ~:·~ .. ; :·' .... ·1.01.; (000,000) 

.r<i'..'.<:.':: 2;15%1.DUannum 

r .'':: : :18.00%10/Revenue 

. · '. ·> · .. . · ;1;800.4. of Revenue 

'·· ' ... ;' ;,.;"3.51% of Revenue 

·: :·.·:':'',''•·.: ~; ·.4.43% of Revenue 

I ·.· ;' : .• ·' <JOOOA.I 

' y· i .,. ·-~~~;; iaseline MEF o/0.817 

Base Incremental Due to 

Year Standard "":'"""\""'~?,.,..'::,...,:·:"""·~··"""n""'"4g~_~10,....,,""' 

:?>'-Li:.~·.y29.30. 

?>'>YI/ '23A5; 

. __ ($)per unit 

($)per unit 

aii!m1JJI ($)per unit 

Industry Mark-up (over full production costs) 

Standard Year Unit Price 

Conversion Costs 

Coversion Capital Expenditure 

Useful Life 

Design &. Marketing Expenses 

E-7 ·. 
APPENDIX F: CONSUMER RESEARCH REPORT 

F-1 
DRAFT REPORT .ON CONSUMER RESEARCH FOR CLOTHES WASHERS 

March 27, 1998 

LAWRENCE BERKELEY NATIONAL LABORATORY 

Environmental Energy Technologies Division 

Technology and Market Assessment Group 

Berkeley, CA 94 720 

Prepared· for 

U.S. DEPARTMENT OF ENERGY 

Office of Codes and Standards 
CONSUMER RESEARCH SUMMARY ......................................... . 

1.0 INTRODUCTION . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

. 2.0 SOURCES OF INFORMATION ...................................... 1 

3.0 SOURCES and PROJECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 

3.1 Major Studies by Consortiums ................................... 2 

3.1.1 THELMA .......................................... 2 

3 .1.2 Bern, Kansas Washer Study . . . . . . . . . . . . . . . . . ·. . . . . . . . . . . . 9 

3.1.3 Wash Wise·. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . 10 

3.1.4 Northeast Energy Efficiency Partnership (NEEEP) ........... 13 

3.2 Individual Utility Demand Side Management I Market Transformation 

Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

3.2.1 Pacific Gas & Electric (PG&E)-. Utility Survey ............. 14 

3.2.2 Commonwealth Electric . . . . . . • . . . . . . . . . . . . . . . . . . . . . . . . 15 

3.3 Consumer Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

3.3.l Consumer Reports I Consumers Union .................... 15 

3.3.2 Consumer- New Zealand publication ................... 16 

3.3.3 CHOICE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 

3 .4 Trade Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 7 

3.4.1 The Soap and Detergent Association (SDA) ................ 17 

3.4.2 Arthur D. Little (AOL) Report on the behalf of AHAM, 1991 ... 18 

3.4.3 EPRI (Electric Power Research Institute) Journal ............. 19 

3.4.4 American Society of Mechanical Engineers, Journal. .......... 19 

3.5 Government Projects .·. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 

3.5.l Energy Star Program .................................. 20 

3.5.2 Commercial Laundry Field Study ........................ 21 

3.5.3 MacDill Air Force Base ............................... 21 

3.5.4 Fort Hood Demonstration Study ......................... 22 

3.5.5 Fort Hood Government Procurement, DOD ................ 23 

• 
CONSUMER RESEARCH SUMMARY 

1.0 Introduction - Presentation of Consumer Research 

This report is a summary of consumer research on clothes washers. 

2.0 Sources of Information 

Listed below are sources of information regarding consumer issues. Some programs are applicable 

to more than one general heading. 

Major Studies by Consortiums 

The High Efficiency Laundry Metering & Marketing Analysis (THELMA) 

Bein, Kansas I Maytag demonstration project 

Wash Wise - Northwest Energy Efficiency Alliance (NWEEA) 

Northeast Energy Efficiency Program (NEEP) · 

Individual Utility Demand Side Management (DSM) I Market Transformation Programs 

Pacific Gas & Electric (PG& E, California) 

Commonwealth Electric 

Consumer Publications 

ConsUD)er Reports (USA) 

Consumer (New Zealand) 

CHOICE (Australia) 

Trade & Professional Organizations 

Soap & Detergent Association 

American Home Appliance Association (AHAM) 

Electric Power Research InstitUte (EPRI) 

American Society of Mechanical Engineers (ASME) 

Government Programs 

Energy Star Appliance Programs (DOE, EPA)- D&R International and other 

ORNL - Commercial Laundry Field Study 

MacDill Air Force Base, DOD 

Fort Hood Demonstration Study, DOD 

Fort Hood Government Procurement, DOD 

1 
3.0 SOURCES and PROJECTS 

3.1 Major Studies by Consortiums 

3.1.1 THELMA (fhe High-Efficiency Laundry Metering & Marketing Analysis Project) 

Sponsors: 

The High-Efficiency Laundry Metering & Marketing Analysis (THELMA) study is an 

Electric Power Research Institute Tailored Collaborative project sponsored by a 

consortium of28 electric, gas, water and sewer utilities, as well as the Electric Power 

Research Institute (EPRI) and the U~S. Department of Energy. The project is being 

coordinated through EPRI. 

Contractors: 

Hagler Bailly Consulting Inc., Phone: (608) 232-2800 

· Dethman & Associates, Phone: (206) 545-0717 

SBW Consulting, Inc., Phone: (206) 827-0330 

National Center for Appropriate Technology, Phone: (206) 324-8064 

Arthur D. Little, Phone: (617) 498-5000 

Objectives: 

The broad goals of THELMA are to acquire the information necessary for leveraging a 

lasting market transformation to efficient clothes washers and to design effective incentive 

programs and other promotional tools for simulating the purchase of efficient washers. 

Specific objectives include: 

.. Characterize status ofH-axis technology. 

.. · Confirm savings estimates. 

.. Assess customer laundering habits. 

.. Identify market barriers. 

.. Suggest strategies for utility actions. 

Methodologies: . . . 

The THELMA report includes the following volumes (not all are currently published): 

Volume 1: Final Research Plan 

Volume 2: Laboratory Testing of Clothes Washers 

Volume 3: General Market Assessment 

Volume 4: Distribution Channels 

Volume 5: Product Demonstrations 

Volume 6: Product Users: In-Home Interviews 

Volume 7: Product Users: Field Monitoring 

Volume 8: Product Benefits and Target Markets 

Volume 9: Product Users: Further Evaluations 

2 
" 

Individual THELMA report volumes, organized by tasks, are discussed below. 

Future Work: 

Findings of the THELMA project will be published in a nine volume set. Four volumes 

were made available to LBNL; all but the Laboratory Testing Report were draft 

documents. It is not certain when the other eight report volumes will become available to 

the general public. Additional field testing with the Neptune, Maytag was done but this 

report is not available.yet. There are currently no plans for EPRI to do additional work. 

3.1.1.1 THELMA Task 2.1 Final Report 

Title: Results of Focus Groups with Consumers 

Date: June 1995 

Report leader: Dethman & Associates 

Objectives: 

... Qualitatively assess the current market for H-axis washers. 

... To explore consumer attitudes, rather than to measure them. 

... Identify and explore the educational needs of consumers. 

... Inform other THELMA research components. 

Methodologies: . 

Washers: prior to introduction of Neptune (Maytag); both top-loading and front-loading Haxis washers were discussed. 

Description: Focus Groups with Consumers. 

Date: February 6-9, 1995. 

Location: Bellevue, Washington & Concord, California 

Sample size: Four focus groups of 10 people each (2 in each location). 

Sample demographics: selected by wash loads and income 

Rebates:. None 

Topics covered included: 

... satisfaction with current washer 

... reactions to H-axis washer 

... interest in buying a H-axis washer 

... consumer descriptions of H-axis washers 

Findings: [from Executive Summary] 

Barriers to Acceptance ofH-axis Washers 

Awareness ofH-axis washers was extremely low among focus group participants. 

Participants reactions were initially quite negative to front-loading H-axis washers, 

based on old associations. They also were resistant to certain features, citing 

concerns about the neeq to bend; leaking; child safety; and ease of use. (Participants 

were shown photographs of front-loading and top-loading models; information and 

3 
diagrams that compared the mechanical action and cleaning technique of Y-axis and 

H-axis washers; specific details of resource and cost savings) . 

.,. Participants viewed top-loading H-axis washers as a confusing hybrid. They also 

had concerns about specific features (e.g., double door entry; the hatch always 

rotating to the top). 

Even when given basic information about H-axis washers, participants had a 

multitude of questions and concerns. 

The Appeal of H-axis Washers 

.,. Most participants in the.focus groups said they might consider buying an H-axis 

washer if it was equal to their V-axis washer in terms of reliability, capadty, price, 

and convenience . 

.,. Participants who spontaneously suggested H-axis benefits were few in number and 

quite tentative . 

.,. Participants perceived that resource conservation is less important than other 

washer attributes such as ease of use and cleaning ability. 

.. Participants rarely mentioned energy and water efficiency as key buying criteria; 

rather, they mentioned cost, reliability, large capacity, versatility, and easy use. 

Key Buying Criteria[froni Results of Focus Groups with Consumers, Draft] 

The most frequently mentioned buying criteria were: 

.. Cost 

.,. Reliability and durability 

·.,. Large capacity 

.,. Versatile features (e.g., water levels; wash speeds; temperature settings; rinse 

. cycles; delicate settings; dispensers; etc.) 

.,. Controls that were easy to use 

Factors that were occasionally mentioned included: ease of home maintenance; safety 

features; gas vs. electricity; type of agitator; noise level; aesthetics; length of wash cycle. 

Energy and water efficiency were rarely mentioned, except in the context of selecting the 

water level to match the load size. 

3.1.1.2 THELMA Task 2.3 Draft 

Title: Laundry Demonstration Center 

Date: April 1996 

Report leader: Dethman & Associates 

·Objectives: 

The Demonstration Centers· purpose was to introduce potential H-axis consumers to two 

types of H-axis washers, and to collect their reactions to and insights about these washers. 

The primary objectives of the Demonstration Center were to assess respondents: 

... Awareness and initial impressions of H-axis washers. 

4 

--
. · 

.. Preferences for top-loading vs. front-loading H-axis washers. 

.. Reactions to trying out an H-axis washers when they conducted a 'test wash". 

.. Likelihood of buying an H-axis washer. 

.. Questions about H-axis washers and reactions to H-axis utility program ideas. 

Methodologi'es: 

Washers: 

.. Participants were shown a top-loading and a front-loading machine, and they 

performed a test wash on the front loader. 

.. Neptune washer (Maytag) was not involved. 

Description: Surveyed participants after demonstrating washers. 

Time period of surveys: November 7, 1995 to February 21, 1996. 

Location: Lynnwo9d, Washington (a suburban area just north of Seattle) 

Sample size: one hundred in depth interviews were conducted. 

Sample demographics: 

.. Random from phone books covering neighborhoods near the demonstration center. 

.. Participants who had some potential for buying an H-axis washer were selected. 

.. Homeowners with at least $30,000 income 

.. Currently owned V-axis machines 

.. Household did four or more wash loads per week 

Findings: [from Executive Summary] 

Whats Important in a New Washer? 

.. Capacity (51%) 

.. Specific features such as dispensers, cycles ( 48%) 

.. Efficiency (35%) (e.g., selecting cold wash rinse or adjusting water level) 

.. Reputation/reliability (31 % ) 

.. · Purchase price (28%) 

What Initial Impressions Did Participants Have ofH-axis Washers? 

·.. One-third had heard of residential H-axis washers; 15% had used one. 

.. Just over a quarter of respondents.said they would be extremely or very likely to buy one. 

.. Those less predisposed to H-axis washers most often cited 'tack of information" as the 

reason for their negative ratings; other concerns included front-loading issues (e.g., 

bending, leaking) and inferior performance. 

What Difference Did Hands-on Experience Make? 

Findings are based on two specific brands of washers, and therefore, may not apply equally well to 

other H-axis machines, especially findings on specific product features . 

Response to Front-load Washer (Washer A) 

.. Perceptions on capacity changed. Over a third of participants remarked that they could 

have added more clothes. 

5 
Many asked for reliable data on the true capacity ofH-axis washer$, and on the relationship 

between capacity and cleaning performance. 

• About half thought the cfothes would get as clean and rinse as well as with their V-axis 

washer. 

• 

• 

About half were unsure or expected inferior cleaning and rinsing. Respondents were 

confused about how clothes could get clean without immersion in water, or without the 

scrubbing action of an agitator. 

Respondents wanted more hands-on proof about the machines s performance under a 

variety of real life settings. . 

Appealing features included: overall appearance, quietness, gentleness on Clothes, familiar 

controls, better balance, and the convenient dispensers for bleach/softener. 

Areas of concern included: bending to load and ullload, fear of leaks, accessibility of 

controls to children, and dispenser spills. 

Response to Top-Load Washer (Washer B) 

This was a top-load, horizontal axis washer. 

• Said they preferred the top-loading H-axis washer in theory but preferred the front-loading 

H-axis washer because they felt the overall design was more logical, more familiar from 

laundromat experiences, and more user-friendly. • 

• The majority of respondents who said they preferred a top loader said they would never 

buy Washer B. Reaction to Washer B was dominated by negative response to the latched 

opening used to enter tlie drum, as well as concerns about the machines basic quality and 

finish. 

How Did Participants View H-axis Washers in the End? 

• Respondents were most likely to believe that H-axis wa5hers did reduce wear and tear, but 

were less sure about their superior cleaning/rinsing ability and equivalent capacity. 

• Respondents were evenly split between those who said that the higher purchase price 

($600-$800 purchase price) would deter them from buying an H-axis washer, and those 

who said it might be worth paying more, if the savings, superior performance and reliability 

ofH-axis washers were proven. 

• Two-thirds were impressed with improved efficiency and financial savings; others wanted 

proof or thought the savings were too small. 

• The number of participants who rated themselves extremely/very likely to buy an H-axis 

washer had more than doubled (from 28% to 67%), after the interview and test wash. 

• Most respondents said they would need more information before actually buying an H-axis 

washer. Respondents required proven data about the H-axis washers'track record; ease of 

repair; efficiency/savings; cleaning, rinsing, wear & tear; and capacity. 

3.1.1.3 THELMA Task 4 Draft 

Title: Impact Analysis 

Date: October 1996 

Report leader: SBW Consulting, Inc. (for EPRI Retail Market Tools and Services) 

6 
Objectives: 

To assess energy and water savings in an 'in-home" environment as compared to a 

laboratory setting. Laboratory testing can provide savings estimates but must make 

assumptions about peoples'laundry practices. In-home monitoring can provide estimates 

under more realistic and diverse conditions. 

Methodologies: 

Washers: Two types ofH-axis washers were used. 

• Washer A was il)Stalled in 46of50 sites. It is a front-loading model that wa5 

officially released to the U.S. market in September 1996 by a major U.S. appliance 

manufacturer. It has a maximum spin speed of 650 rpm. 

• Washer Cwas installed in 4 of50 sites. It is a front-loading model manufactured in 

Europe and available in the United States. Washer C requires 220 Volt service and 

only a cold-water connection b~cause it has an internal electric water heater. 

Description: Existing washer and dryer were monitored for a 6-week period. 

Dates: 

An H-axis washer was then substituted for the V-axis washer, and the H-axis 

washer and dryer were then monitored for a 7-week period. 

Phase I (existing washer monitored) Sept. 1995 - Dec. 1995, 

Phase II (H-axis washer monitored) May 1996 - Aug. 1996. 

Locations: 

Phase I: 22 sites within a 10-mile radius in the Greater-Seattle, Washington area 

4 sites in Tacoma, Washington 

Phase II: 19 sites in Salem, Oregon 

5 sites in the Bay Area of California (Fremont, San Rafael, Richmond) 

Sample size: 50 homes were monitored. 

Fmdmgs: J 

• Water savings ranged from 11to74 percent with an average water savings of 36 percent. 

• Energy is saved if warm or hot water is used for washing or if the washer features a high 

spin speed. Average savings were 1.02 kWh/wash load. 

• Participants with the H-axis Washer A reduced their load size by 4 percent. Those with 

Washer C reduced on average reduced their load size by 10 percent. 

• There was no significant change in the wash temperatures selected between the V-axis and 

H-axis washers. 

3.1.1.4 THELMA, Final Report 

Title: Laboratory Testing of Clothes Washers, Final Report 

Date: April 17, 1997 

Contractor: Arthur D. Little, Inc. (ADL) 

Principle investigators: J Dieckmann, Detlef Westphalen 

Project Managers: R. Gillman, J. Kesselring; Customer Systems Group, EPRI 

7 
This report was a continuation of an earlier report of the same title, published in December 

1995. Principal investigators for the earlier report (also by ADL) are: J Dieckmann, W. 

Murphy. An additional washer not previously available was tested in the updated report. 

Objectives: . 

· To test energy and water consumption and other performance parameters discussed below. 

Methodologies: 

Washers: Number of clothes washers tested: seven H-axis and one Y-axis washers. 

Washers were labeled A through H. It is believed that a prototype Neptune 

(Maytag) was included in the testing. Clothes washers could be identified by noting 

their unique specifications. 

Laboratory testing of washer to determine (for 8 washers): 

... DOE washer capacity 

... Hot and cold water consumption 

... Energy consumption by the washer mechanical drive 

... DOE energy factor 

... Water extraction effectiveness 

... Effect of water extraction effectiveness on dryer energy 

... Soil removal effectiveness 

... Rinsing effectiveness 

... Gentleness of action 

Findings: [from report Conclusions] 

... The normal wash cycle times of the eight H-axis machines ranged between 40 minutes and 

110 minutes. The Y-axis normal wash cycle reqμired 3 7 minutes. 

... Th.e energy factor of the H-axis machines, as measured by the DOE test procedure, was 

approximately 95% higher than the Y-axis machine that was tested (i.e., H-axis energy 

consumption normalized to the basket volume is approximately 45% less than Y-axis). 

... The average water consumption of the eight H-axis machines, normalized to the basket 

volume, is 25% less than the Y-axis machine at maximum fill level and l 0% less than the 

Y-axis at minimum fill level. . 

... The H-axis washers tested all had higher spin Gs than the Y-axis machine, resulting in 

measurably lower remaining moisture content (RMC). 

... Electric clothes dryer energy decreases linearly with decreasing RMC. 

... . As a group, the soil removal effectiveness of the H-axis washers, as measured by the 

AHAM soil removal test, was 50% greater than for the Y-axis machine. 

... The average rinsing effectiveness of the H-axis washers was 50% greater.than the Y-axis 

washer. 

... The AHAM gentleness of action test results were comparable for H-axis and Y-axis 

machines. 

8 
3.1.2 Bern I Kansas Study 

Title: Bern Clothes Washer Study - Final Report 

Date: March 1998 

Report: ORNL/M 6382 

Sponsors: Department of Energy, Maytag 

Contractor: Oak Ridge National Laboratory 

Contacts: 

• John Tomlinson, Oak Ridge National Laboratory (ORNL), Phone: (423) 574-0291 

• D.T. Rizy, Oak Ridge National Laboratory 

• Linda Eggerss, Maytag, Phone: (515) 791-8518 

Web Site: www.energystar.gov 

Objectives: 

• To evaluate the energy and water savings of high-efficiency, H-axis washers in a 

community which has been converted to the new design. 

• To determine the impact of the new washer on customers'laundry habits and perceptions 

• To demonstrate the findings and to develop information helpful to utilities (energy and 

water) and others with an eye towards moving the current clothes washer market to higher 

efficiency options. 

Methodologies: 

Washers: Monitoring was done with the existing V-axis waslting μiachines in Phase 1 and 

with the Maytag Neptune H-axis washer in Phase 11. 

Description: Demonstration/monitor & metering study. 

• · H-axis washers were installed in 105 homes in Bern, Kansas .. 

• Interviews and questionnaires were used to determine the machines users 

satisfaction with the washer. 

Dates: monitoring took place between June, 1997 and September 1997. 

Location: Bern, Kansas; a rural community with a population of about 200 

Sample size: 105 homes 

Conditions: participants were allowed to keep washers after the study free of charge 

Data collected on: 

• impacts of load size 

• water consumption 

• energy consumption 

• detergent use and consumption 

• impacts on other additives 

• impacts on laundry habits 

• impacts on machine settings 

• cleaning performance 

• impacts on load dampness 

9 
.. impacts ~>n drying habits 

• municipal utility impacts 

Findings:. [from Executive Summary and Conclusions] 

.. Overall, it was found that the changeover to the H-axis washer reduced the average water 

consumption from 41.5 gallons/load to 25.8 gallons/load - a water savings of about 38%. 

• The H-axis washers energy consumption including washer energy and hot water energy 

fell by 58% due to hot water savings and the impact of a highly efficient motor in the H-

.. 

.. 

.. 

axis. 

The remaining moisture content of damp loads removed from the H-axis washers was, on 

average, 7% lower than for loads removed from participants'V-axis washers. 

The data and subsequent analyses also showed that across all loads, temperature settings, 

use of detergent and other additives, participants found the cleaning performance of the Haxis technology to be generally superior to their Phase I washer irrespective of its age. 

Participants seeined to adapt easily to the H-axis design, and laundry habits (average load 

weights, detergent use, how loads were dried, when loads were washed during the week, 

wash/rinse temperatures and other .factors) remained largely unchanged from Phase I to 

·Phase II. 

• Taken together, these findings suggest that a changeover to H-axis technology delivers 

large savings in energy and water to the customer with an improvement in cleaning 

performance and utility. . 

.. The fraction ofloads in which participants were 'tompletely satisfied" with the.cleaning 

performance increased thr~efold from 15 to 45% in the changeover from the average Vaxis washer to the H-axis washer in the study. 

.. Overall, the study showed that the changeover to the H-axis washer has little impact on the 

way in which customers washed and dried clothes. 

3.1.3 WasbWise 

Title: Coming Clean About Resource-Efficient Clothes Washers: An Initial Wash Wise 

Program and Market Progress Report - Final Report 

Report Number: E98.,003 · 

Date: January 28, 1998 

Commissioned by. 

• Northwest Energy Efficiency Alliance (NWEEA); comprised primarily of electric utilities in 

the Pacific Northwest. 

· Contractors: 

• Portland Energy Conservation Inc. (PECI) 

• Gilmore Research 

• Pacific Energy Associates (PEA) 

10 
Contacts: 

• Northwest Energy Efficiency Alliance 

Tom Eckman, Phone: (503) 827-8416 

Gabriel Foulkes, Phone: (503) 827-8416 x225 

Ben Bronfinan, Phone: (503) 827-8416 x226 

• Portland Energy Conservation Inc. (PECI) 

Lois Gordan, Phone: (503) 248-4636 x207 

• Pacific Energy Associates (PEA) 

David Hewitt, Phone: (360) 571-0497 

Gary Smith, Phone: (503) 590-4609 

• Gilmore Research Group 

. Denise Bauman, Phone: (503) 236-4551 

• Oregon Department of Energy 

Charlie Stevens, Phone: (503) 229-5625 

Web site: http://www.nwalliance.org 

Objectives: 

• To dramatically increase the market share of resource-efficient-clothes washers (RECWs) 

in the ·program area over the next three years. 

• To influence the development and support of maximum energy and water efficiency levels 

for revised federal clothes washer standards. 

Methodologies: 

Washers: At the time the surveys below were done, the Maytag, Neptune H-axis washer 

was not on the market. Frigidaire, Staber and Europeans had horizontal axis · 

washers on the market. 

Description: This project has several aspects to it: 

... a telephone survey of 419 participants/purchasers ofRECWs as part of the 

Wash Wise rebate program (a rebate program funded by utilities) 

... an intercept survey of 42 consumers shopping for laundry equipment 

... a telephone survey of205 appliance retailers 

• a telephone survey of qualified representatives of nine major appliance manufacturer 

and importers of European products 

Location: Pacific Northwest; Washington, Oregon, Idaho, and western Montana 

Gilmore Research conducted the following surveys: 

... Participant Survey - , those who participated in the rebate program and bought an 

H-axis clothes washer. Consumers got a $130 instant rebate and dealers 

got a $20 rebate from the sponsoring u~jlity. 

... Intercept Surveys - what people thought of H-axis washers before they became 

participants and before they used the H-axis washers. 

• Retailer Survey (dealers)- how-many they think they could sell. 

... Manufacturer Survey- predicted future H-axis sales. 

11 
Pacific Energy Associates evaluated the program and discussed key findings in the report: 

Coming Clean About Resource-Efficient Clothes Washers: An Initial Wash Wise Program 

and Market Progress Report.-· Final Repor~; January 28, 1998 

This report includes analysis of: 

• reasons for purchasing a RECW; 

• satisfaction with Wash Wise program; 

• product pricing and RECW cost barrier; 

• manufacturers perception of the RECW market; 

• opportunities for increasing RECW sales. 

Findings: 

Purchaser Survey 

This consisted of 419 interviews with people who had purchased a RECW in the beginning 

months of Wash Wise. 

• Demographics: 36% had incomes of less than $40;000 per year, 39% had 

household incomes between $40, 000 and $75,000 per year. 

• Main reasons for purchasing a RECW were: energy or water savings, 74%; cleaner 

clothes, 18%; and gentler on clothes , 15%. 

• 76% said that they would have purchased a front loading washing machine even if 

the rebate was not offered. 

• 95% said they were at least somewhat satisfied with each RECW feature 

• 81 % said the washer was worth the price 

., 91 % said they would recommend the machine to others 

Intercept Surveys ( 42 interviews) 

Of the forty-two (42) intercept interviews conducted, thirty-two (32) were customers 

shopping primarily for top-loaders, and ten (I 0) interviews were with customers looking 

for tumble-action washers. 

• Top-load shoppers mentic~ned 'high cost" more than anything else as a reason for 

buying a top-loading washer rather than· a tumble-action washing machine. 

• Thirteen (13) top-load shoppers said they were shopping for a top-loader rather 

than a tumble-action washer because the tumble-action machines were too 

expensive. 

• Customers also reported that they did not want to bend over to load and unload the 

clothes (mentioned by 8 top-load shoppers). 

• Six (6) top-load shoppers reported that they were simply used to top-loaders. 

Retailer Survey (205 dealers) 

• Retailers believed that about half of the RECW purchasers. would have purchased a 

standard, top loading machine ifthe rebate were not available. 

• lfthe rebate were not offered, 57% thought the demand would slacken, then pick 

up again. 

12 
... When retailers were asked to estimate thf: average price of top loading machines 

that have similar capacity and features to the most popular tumble actions washers. 

The median response was $500. 

... Retailers thought 3'0% of customers would be willing to pay $200 more for a 

RECW, and 50% would pay $100 more. 

... About half of the retailers believe that tumble action washers will dominate the 

market as consumers become more familiar with them. 

Manufacturer Survey (6 U.S., 3 Importers) 

"" Six U.S. based manufacturers of clothes washers and three major importers were 

interviewed. 

... Manufacturers'estimates for RECW market share ranged from 2% to 25% in five 

years. 

Future Work: 

Follow-up participant, consumer awareness/acceptance, appliance dealer, and 

manufacturers surveys will be conducted in the Spring of 1998, and an 

evaluation/marketing report will be published in the second quarter of 1998 to document 

program success in terms of market indicators and program accomplishments. 

3.1.4 Northeast Energy Efficiency Partnership (NEEP) 

Reports: None at this time 

Sponsors: A consortium of utilities. 

Contractors: Sub-contractor: RL W Analytics 

Contact: Bruce Wall, Phone: (860) 620-0076 

Web Site: future web site to be called: www.tumblewashi~r.org 

Objectives: 

The market for high efficiency clothes washers will be evaluated. High efficiency clothes 

washers will be promoted. 

Methodology: 

Washers: Any washer meeting Energy Star performance levels is eligible; currently this 

means H-axis washers. 

Description: 

... Could characterize as a market pull program; some participants will have incentives, 

also consumer education will be done, point of sales marketing 

promotions/education. 

13 
... Baseline market research will include: surveys, focus groups, interviews with 

reta.ilers, intercept interviews with consumers leaving the store. ( will follow up 

with repeat interviews of the same people 12 months later, who bought or didnl 

buy a horizontal-axis washer) 

Location: 

... Currently there are 18-20 participating utilities and retailers covering 5.4 million 

customers in all of New England, excluding Maine. . 

... New Jersey is expected to join in 1999. New Jersey's 1998 program will include 

only new construction. • 

Dates: Program will officially start on April 21, 1998, although some members of the 

partnership have already begun the program (e.g., Con-Electric). 

Rebates: Some participants will have incentives to purchase efficient washers. 

Findings: 

The baseline market research is completed. It is anticipated that results of research will be 

made available to the public in the future. 

3.2 Individual Utility Demand Side Management I Market Transformation Programs 

3.2.1 Pacific Gas & Electric (PG&E) - Utility Survey 

Contact: Ted Pope, Phone: (415) 973-4856 

Methodologies: 

Washers: 

... Currently all eligible machines are H-axis. 

... . The Neptune was available in the June/July time frame. 

Description: 

... Sur\rey questions focused on participants satisfaction with their purchased H-axis 

machines. 

... Rebate program participants responded to an l l question survey. 

Dates: Results from April through August 1997 

Location: PG&E service territory (northern California) 

Sample size: 643. 

Rebates: 

... PG&E had a customer program where they gave rebates to consumers who bought 

energy efficient washers. ($75-$175 rebates). 

... In addition, water utilities also provided rebates up to $75. 

Findings: 

Over 90% of participants in a rebate program responded excellent or very good when 

asked if they were satisfied with their washer. 

14 
Future work: 

Additional survey data is being collected. 

3.2.2 Commonwealth Electric - Utility Survey 

Contact: Dick Moran, Phone: (508) 291-0950 x 3548 

Methodologies: 

Washers: energy efficient washers 

Description: survey of participants in a rebate program 

Date: a rebate program has been running since September 1997. 

Sample size: 175 

Rebates: yes 

Questions relevant to this study include: 

• Would you have purchased this washer without a rebate? 

• Rate your washer (in comparison to old washer) in terms of ease of use, 

appearance, time to wash, and quality of wash). 

• Have you had any problems? 

Findings: 

Responses have not been officially tabulated, however, responses are leaning toward easier 

to use and overwhelmingly towards cleaner clothes. 

3.3 Consumer Publications 

3.3.1 Consumer Reports I Consumers Union 

Contact: Mark Connelly, Division Head- Home Environmental Products, Phone: (914) 378-

2325 

Publication: July 1997 issue of Consumer Reports magazine 

Methodology: 

• Clothes washers were evaluated and rated 

• Evaluates: noise, washability and features 

• Compared 16 top-loading (Y-axis) and 2 H-axis clothes washers 

• H-axis washers evaluated were: Frigidaire FWT445GE and ASKO 8005 (compact) 

Findings: 

• 'To get the best results with front-loaders, you need to use special detergent that can be 

expensive". 

15 
• 'Little is yet known about their [H-axis models] reliability". 

• Spot checks on wear and tear didn t verify that front-loaders were more gentle on clothes. 

• The Frigidaire did not produce dryer clothes at the end of the spin cycle. 

• Front-loaders are generally quieter during filling and washing and a bit noisier during the 

spin cycle. 

• The two front-loaders used less energy. 

• H-axis washers handled unbalanced loads better than others. 

3.3.2 Consumer- New Zealand publication 

Contact: Hamish Wilson, email: hamish@consumer.org 

Publication: September 1997 issue of Consumer magazine. 

Methodology: 

• Rated soil removal, spin efficiency, energy use, water use, gentleness, ease of use, cycle 

time, noise, detergent used. 

• Rated more H-axis washers than U.S. consumer publications. 

• Tested four (4) H-axis washers and six (6) Y-axis washers 

Findings: (results presented in bar chart form) 

• Overall, H-axis machines scored better on energy and water usage. 

• Overall, H-axis machines scored better on gentleness. 

• Soil removal for H-axis and Y-axis were similar. 

3.3.3 CHOICE-(Australian consumer magazine) 

Publication: September 1997 issue of CHOICE magazine 

Methodology: 

• 21 washing machines were rated; 7 H-axis, front-loaders and 14 Y-axis, top-loaders 

• Washers were rated/tested for: wash capacity, cycle time, noise, water used, energy 

efficiency, spin efficiency, gentleness, and dirt removal. 

Findings: 

• Based on a combination of reasonable performance, purchase price and 10-year running 

costs, the Best Buys were the GE and a Hoover front-loaders. 

• "Notably higher scores than the top-loaders (Y-axis) for gentleness and water and energy 

efficiency." 

• Top-loaders (Y-axis) wash and spin just as well as front-loaders, and they tend to have 

faster cycle times." 

• Top-loaders (Y-axis) are usually less gentle on your clothes. 

16 
.-

---------~~---·---------- ---- --- -- ---

:t4 Trade Organizations 

3.4.1 The Soap and Detergent Association (SDA) 

Publication: Detergents & Laundry Additives in High-Efficiency Washers 

Contacts: 

Janet Donohue, Director of Communication, Phone: (212) 725-1262 

Jane Meyer, Director Consumer Education, Phone: (212) 725-1262 

Background: 

The Soap and Detergent Association is a national trade association (139 member), founded 

.. in 1926. The Association represents the manufactlirers of household, industrial and 

institutional cleaning products and those companies that supply ingredients and finished 

packaging to the industry. Over 90% of the cleaning products sold in the United States are 

made by SDA members. 

Methodology: 

Not described, however, this document relies largely on data from tumbler washer use in 

Western Europe. Powder detergent is used as the basis of comparison. 

Summary: 

The Soap and Detergent Association made available a publication detailing information on 

detergents and their use in H-axis machines. (October 1996) 

Discussed are: 

• excessive sudsing 

• formulating detergents for high-efficiency H-axis machines 

• · water temperature 

• lower water volume 

• clothes-to-water ratio 

• powder detergents and laundry additives 

• -load sizes 

• · amount of detergent required 

• availability of high-efficiency detergent 

Findings: 

• High-efficiency tumbler washers raise special challenges when formulating effective 

detergents and laundry additives 

a. Suds are more readily created. Excessive suds can overflow the machine or 

interfere with the washers s proper operation. 

b. Water temperature can be lower because there is less water to heat the wash tub 

and the clothes. 

c. Lower water volume makes it more difficult to keep soils from redepositing on 

fabrics. 

17 
• "Little is yet known about their [H-axis models] reliability". 

• Spot checks on wear and tear didnt verify that front-loaders were more gentle on clothes. 

• The Frigidaire did not produce dryer clothes at the end of the spin cycle. 

• Front-loaders are generally quieter during filling and washing and a bit noisier during the 

spin cycle. 

• The two front-loaders used less energy. 

• H-axis washers handled unbalanced loads bett~r than others. 

3.3.2 Consumer - New Zealand publication 

Contact: Hamish Wilson, email: hamish@consumer.org 

Publication: September 1997 issue of Consumer magazine. 

Methodology: 

• Rated soil removal, spin efficiency, energy use, water use, gentleness, ease of use, cycle 

time, noise, detergent used. 

• Rated more H-axis washers than U.S. const.imer publications. 

• Tested four (4) H-axis washers and six (6) Y-axis washers 

Findings: (results presented in bar chartform) 

• ·Overall, H-axis machines scored better on energy and water usage. 

• Overall, H-axis machines scored better on gentleness. 

• Soil removal for H-axisand Y-axis were similar. 

3.3.3 CHOICE -(Australian consumer magazine) 

Publication: September 1997 issue of CHOICE magazine 

Methodology: 

• 21 washing machines were rated; 7 H-axis, front-loaders and 14 Y-axis, top-loaders 

• Washers were rated/tested for: wash capacity, cycle time, noise, water used, energy 

effic_iency, spin efficiency, gentleness, and dirt removal. 

Findings: 

• Based on a combination of reasonable performance, purchase price and l 0-year running 

costs, the Best Buys were the GE and a Hoover front-loaders. · 

• "Notably higher scores than the top-loaders (Y-axis) for gentleness and water and energy 

efficiency." 

• Top-loaders (Y-axis) wash and spin just as well as front-loaders, and they tend to have 

faster cycle times." 

• Top-loaders (Y-axis) are usually Jess gentle on your clothes. 

16 

.:; 
Future work: / 

Additional survey data is being collected. 

3.2.2 Commonwealth Electric - Utility Survey 

Contact: Dick Moran, Phone: (508) 291-0950 x 3548 

. Methodologies: 

Washers: energy efficient washers 

Description: survey of participants in a rebate program 

Date: a rebate program has been running since September 1997. 

Sample size: 175 

Rebates: yes 

Questions relevant to this study include: 

• Would you have purchased this washer without a rebate? 

• Rate your washer (in compatjson to old washer) in terms of ease of use, 

appearance, time to wash, and quality of wash). 

• Have you had any problems? 

Findings: 

Responses have not been officially tabulated, however, responses are leaning toward easier 

to use and overwhelmingly towards cleaner clothes. 

3.3 Consumer Publications 

3.3.l Consumer Reports I Consumers Union 

Contact: Mark Connelly, Division Head- Home Environmental Products, Phone: (914) 378-

2325 

Publication: July 1997 issue of Consumer Reports magazine 

Methodology: 

• Clothes washers were evaluated and rated 

• Evaluates: noise, washability and features 

• Compared 16 top-loading (V-axis) and 2 H-axis clothes washers 

• H-axis washers evaluated were: Frigidaire FWT445GE and ASKO 8005 (compact) 

Findings: 

... "fo get the best results with front-loaders, you need to use special detergent that can be 

expensive". 

15 
.. Baseline market research will include: surveys, focus groups, interviews with 

retailers, intercept interviews with consumers leaving the store. ( will follow up 

with repeat interviews of the same people 12 months later, who bought or didnt 

buy a horizontal-axis washer) 

Location: 

.. Currently there are 18-20 participating utilities and retailers covering 5.4 million 

customers in all of New England, excluding Maine. 

.. New Jersey is expected to join in 1999. New Jersey's 1998 program will include 

only new construction. 

Dates: Program will officially start on April 21, 1998, although some members of the 

partnership have already begun the program (e.g., Con-Electric). · 

Rebates: Some participants will have incentives to purchase efficient washers. 

Findings: 

The baseline market research is completed. It is anticipated that results of research will be 

made available to the public in the future. 

3.2 Individual Utility Demand Side Management I Market Transformation Programs 

3.2.1 Pacific Gas & Electric (PG&E)-. · Utility Survey 

Contact: Ted Pope, Phone: (415) 973-4856 

Methodologies: 

Washers: 

.. Currently all eligible machines are H-axis. 

.. The Neptune was available in the June/July time frame. 

Description: 

.. Survey questions focused on participants satisfaction with their purchased H-axis 

machines. · 

.. Rebate program participants responded to an 11 question survey. 

Dates: Results from April through August 1997 

Location: PG&E service territory (northern California) 

Sample size: 643 

Rebates: 

.. PG&E had a customer program where they gave rebates to consumers who bought 

energy efficient washers. ($75-$175 rebates). 

.. In addition, water utilities also provided rebates up to $75. 

Findings: 

Over 90% of participants in a rebate program responded excellent or very good when 

asked if they were satisfied with their washer. 

14 

.. 
.. "Little is yet known about their [H-axis models] reliability". 

.. Spot checks on wear and tear didnt verify that front-loaders were more gentle on clothes. 

.. The Frigidaire did not produce dryer clothes at the end of the spin cycle. 

.. Front-loaders are generally quieter during filling and washing and a bit noisier during the 

spin cycle. 

• The two front-loaders used less energy. 

• H-axis washers handled unbalanced loads better than others. 

3.3.2 Consumer - New Zealand publication 

Contact: Hamish Wilson, email: hamish@consumer.org 

Publication: September 1997 issue of Consumer magazine. 

Methodology: 

• Rated soil removal, spin efficiency, energy use, water use, gentleness, ease of use, cycle 

time, noise, detergent used. 

• Rated more H-axis washers than U.S. consumer publications. 

• Tested four (4) H-axis washers and six (6) Y-axis washers 

Findings: (results presented in bar chartform) 

• Overall, H-axis machines scored better on energy and water usage. 

• Overall, H-axis machines scored better on gentleness. 

• Soil removal for H-axis and Y-axis were similar. 

3.3.3 CHOICE - (Australian consumer magazine) 

Publication: September 1997 issue of CHOICE magazine 

Methodology: 

• 21 washing machines were rated; 7 H-axis, front-loaders and 14 Y-axis, top-loaders 

• Washers were rated/tested for: wash capacity, cycle time, noise, water used, energy 

effic.iency, spin efficiency, gentleness, and dirt removal. 

Findings: 

• Based on a combination of reasonable performance, purchase price and 10-year running 

costs, the Best Buys were the GE and a Hoover front-loaders. 

• "Notably higher scores than the top-loaders (Y-axis) for gentleness and water and energy 

efficiency." 

• Top-loaders (Y-axis) wash and spin just as well as front-loaders, and they tend to have 

faster cycle times." 

• Top-loaders (Y-axis) are usually less gentle on your clothes. 

16 
• 

• 

• 

• 

Washing machine price is the major detenninant of consumer utility or satisfaction . 

No substantial differences in utility patterns exist between environmentally ~ensitive and 

average consumers. However, low and medium income sub-groups of consumers have 

greater expected sensitivity to price. 

Conjoint Results (Appendix A) : Consumer preference for purchasing a washing machine 

are: 

• price, 49 .1 %; 

• energy costs, 20.3%; 

• ability to add more clothes, 11 %; 

• loading method, 10. 7%; 

• water use, 5.6%; and 

• detergent use, 3.3%. 

See (Appendix B) for detailed results of the self administered survey results . 

3.4.3 EPRI (Electric Power Research Institute) Journal 

Publication: EPRI Journal, November/December 1997 Issue 

"The New Line on Laundry" by Leslie Lamarre 

Findings: . 

• 'Consumers also like the absence of the agitator (on H-axis machines), which frees up 

room for comforters, rugs, and other bulky items they might otherwise have to take to the 

laundromat." This is an insightgleaned from the Four-year EPRI, THELMA project. 

• At this time, 84% of consumers are very satisfied or somewhat satisfied with their own 

washing machines. Their biggest complaints are unbalanced loads and clothes tangling 

around the agitator. · 

• After in-home use, tests. indicated that the capacity concern was more visual than actual. 

• The biggest barrier cited to buying an H-axis machine was the higher cost of the machines. 

3.4.4 Mechanical Engineering - Journal of the American Society of Mechanical Engineers 

Article title: Energy-efficient appliances 

Author: Steven Ashley, Associa~e Editor 

Date: March 1998 

Summary: 

• The author discusses the design of the Neptune washer (Maytag). 

Findings: 

• The Maytag, Neptune has a maintenance-free, low-noise, high-efficiency motor with 

infinitely variable-speed control. 

• The Maytag team solved the bend-and stoop problem by tilting the spinner axis of the 

stainless.:steel tub up 15 degrees and making the opening as large as possible. 

19 

.. 
3.5 Government Projects 

3.5.1 Energy Star Program 

Contacts: 

Bill Noel- Partnerships manager, DOE, Phone (202) 586-6149 

Lee Bodner - Program manager; D&R International, Phone: (301) 5 88-93 87 

Mary Myers- D&R International, Phone: (301) 588-9387 

Jamie Treworgy - D&R International, Phone: (30 I) 588-9387 

Marc Lafrance - DOE, Phone: (202) 586-8423 

Background: 

Energy Star is a joint project of the U.S. Department of Energy (DOE) and the 

Environmental Protection Agency (EPA). This collaboration concentrates on educating 

consumers about the benefits of highly energy efficient home appliances, consumer 

electronics, office equipment and building materials. 

Programs: 

• Demonstrating new Technologies in 'teal world" settings 

Example: Bern, Kansas I Maytag Washer study 

• Volume Purchases 

Large institutional buyers, such as housing authorities, are organized to pool their 

resources. 

• Energy Star Appliance Program (formerly the Retailer Initiative) 

A retailer initiative pilot program to promote high efficiency washers. The DOE 

will place "Energy Star" labels on efficient washers to help consumers differentiate 

them from other machines; making it easier for consumers to recognize efficient 

appliances. A list of energy efficient clothes washers is listed at the energy star web 

site: http://www.energystar.gov. 

• Financing Programs 

The DOE is also working on financing programs with Fannie Mae, and working 

with utilities and retailers to determine the best ways to communicate efficiency 

benefits to consumers. 

• Energy Star Manufactured Homes Program 

DOE, Washington State University, and the Idaho Department of Water Resources 

are working with regional manufacturers to include high efficiency residential 

appliance, including clothes washers, in manufactured homes. 

Energy Star Participants reported August 97: 

• SMUD (Sacramento Municipal Utility District) 

20 
• Southern California Edison 

• SDA&E (San Diego Gas & Electric) 

• Potomac Electric Power Co. 

• Wisconsin Electric Power Company 

• Wisconsin Public Service 

• Portland Energy Conservation Inc. 

3.S.2 Commercial Laundry Field Study 

Commissioned by: Cooperative Research and Development Agreement(CRADA) with Maytag 

Contractor: John Tomlinson, ORNL,Phone: (423) 574-0291 

Sub-contractor: Ayers Associates, CH2M Hill 

Objective: 

To measure and compare energy and water use before and after changing to an energy 

efficient clothes washer. 

Methodology: 

Washers: 10 residential V-axis washers were replaced by Maytag H-axis washers 

Description: 

• Energy monitoring and metering study 

• Main laundry room selected with 10 washers 

• Washers average 115 cyeles per month 

Location: Tampa, Florida apartment complex with 100 units 

Findings: (Contact Tomlinson for more detail) 

After replacing V-axis washers with H-axis washers: 

• water use was reduced by 38%, 

• hot water use was reduced by 40%, 

• machine energy reduced by 4 7%, 

• 69% were satisfied or very satisfied with the cleaning performance of the V -axis 

washer, 

·• · 97% of customers interviewed were either satisfied or very satisfied with the 

cleaning performance of the H-axis machine. 

3.S.3 MacDill Air Force Base 

Commissioned by: Department of Defense 

Contact: John Tomlinson, ORNL, Pbone: (423) 574-0291 

21 

• 
Subcontractor: Ayers Associates 

Objectives: [from presentation by Tomlinson on July 18, 1997] 

• Improved awareness of benefits. 

• Information dissemination to facility managers. 

• Information on load size. 

• Technical evaluation 

Methodology: 

Washers: Y-axis large Speed Queen replaced by 5 Frigidaire, 1 Creda 

Description: 

.~ • Energy and water use study in two apartment laundry rooms used by Army 

barracks. 

• Measure energy/water consumption; measure load size 

... Customer impressions/habits 

Incentives: H-axis machines contributed to project by manufacturers 

Findings: (Contact Tomlinson for more detail)( from presentation by Tomlinson on July 18, 1997) 

• . H-~is washer (B) showed an increase in cu.ft I kWh of 190% over V-axis washer 

(A). 

• H-axis washer (B) showed an increase in lb I kWh of 230% over V-axis washer 

(A). 

3.5.4 Fort Hood Demonstration Study (metering study) 

Commissioned by: Department of Defense, Forces Command- Department of Army 

Contacts: 

• Greg Sullivan, Pacific Northwest National Laboratory (PNNL), Phone: (509) 372-6212 

• Graham Parker, PNNL, Phone: (509) 375-3805 

Methodology: 

Washers: 

• Baseline washers are Roper residential washers. 

• On premise laundry machines (like commercial washers without a coin box). 

• Plan to monitor Staber, Maytag, & Amana washers (a total of 18 washers). 

Descriptipn: 

• Will monitor: hot & cold water and mechanical energy. 

• Will examine operation ·and maintenance expense (washers expected to be treated 

roughly). 

Dates: 

• · A 6 month project starting in December 1997. 

22 
• Will monitor H-axis washers January-June. 

Location: Fort Hood - Texas (near Austin), Army Base. 

Sample size: 

• On-premise laundry for the barracks. 

• Each barrack has 6 machines. 

• Each machine is estimated to do 5-7 turns a day. 

Findings: Current Status (3-23-98): 

• Have monitored/metered Roper baseline washers for two months. 

• Are currently monitoring Staber washers and have two months worth of data. 

• Report expected in early swiuner of 1998. · 

3.S.S Fort Hood Government Procurement - Department of Defense 

Commissioned by: Department of Defense, Department of Army 

Contact: 

Summary: 

• Government procurement program. 

• Army bought approximately 1,200 Staber H-axis washing machines during the summer/fall 

of 1997. 

23 
APPENDIX G: MANUFACTURER INTERVIEW GUIDE 

G.1 INTRODUCTION 

The Department proposes to conduct interviews with manufacturer representatives to gather 

key information on the effects that potential new energy efficiency standards for clothes washers 

might have on their company's revenues and finances, and on their workforce. This interview guide 

• provides a starting point to identify relevant issues and qualitatively and quantitatively describe the 

impacts of possible candidate standards levels. 

G.2 CURRENT ORGANIZATIONAL CHARACTERISTICS 

The Department's interest concerning manufacturer impact is focused at the plant or profit 

center level directly pertinent to clothes washer production. The context within which the plant 

operates and the details of plant pro.duction and costs are not readily available from the published 

literature, however, so the Department invites the manufacturers to provide these details 

confidentially in their own words to the extent practical. The organizational setting around the 

clothes washer profit center is ofinterest to round out the Department's understanding of the probable 

future of the clothes washer manufacturing activity without and with energy efficiency standards. 

G.2.1 Identify names of parent companies and any subsidiaries relevant to the clothes washer 

industry. 

G.2.2 Please provide the locations of your clothes washer production facilities and the product mix 

·manufactured there. 

G.2.2.1 How would energy efficiency standards affect each production facility mix? 

G.2.3 How has your company incorporated energy efficiency improvements in clothes washer 

technology into its product line and marketing strategy? 

G.2.3.1 Are horizontal-axis clothes washers currently being produced by your 

company? 

G.2.4 How would a new energy efficiency standard on clothes washers affect your product mix arid 

. marketing strategy? 

G.3 INDUSTRY INFRASTRUCTURE 

As part of an attempt to better understand the industry infrastructure and the impact energy 

efficiencystandards might have on it, the Department would like for manufacturer representatives to 

discuss their major distribution channels and major sources of supply. 

G-1 
Major Customer Types 

The following questions are intended to gain an understanding of how potential energy standards 

levels might affect customer mix and distribution channels. 

G .3. I Generally how would a new clothes washer standard impact your customer mix, distribution 

channels and corresponding profit margins? 

G.3.2 Do you see any changes in emphasis on export sales, and would the presence of new energy 

efficiency standards have an impact on production of clothes washers for export sale? 

Major Suppliers 

The Department recognizes that manufacturers do not operate in insolation and that changes 

in production levels or economic health of a manufacturer can have significant impacts on its suppliers 

and other trade allies. The Department plans to be available to meet with representatives of these 

trade allies and to consider their impacts. · 

G.3.3 Who are the suppliers that may be affected by changes in your product mix as a result of 

standards? How might they be affected? 

G.4 MANUFACTURER CASH FLOW ANALYSIS 

Each manufacturer has been given the Government Regulatory Impact Model (GRIM) to 

develop its own cash flow model to compare with that of the "industry" cash flow. Thiswill allow 

each manufacturer to use its own financial information to arrive at the expected differences in cash 

flow due to new energy efficiency standards. These results, together with an adequate understanding 

ofhow they were developed. will help the Department to estimate industry impacts for different types 

of manufacturers. 

G.4.1 Costs 

How do you explain differences. if any, between your manufacturing costs with and without 

new energy efficiency standards and those presented in the sample results for the industry as . 

a whole? 

G.4.1.1 

G.4.l.2 

Is the "Industry" cost structure (materials, labor, overhead, tooling, etc.) 

representative of your firm's costs? What are the differences and their causes? 

How might new standards alter this cost structure? 

G-2 
G.4.2 Prices 

How do you explain differences, if any, between your expected product prices with and 

without new energy efficiency standards and those presented in the sample results for the 

industry as a whole? · 

G.4.2.l 

G.4.3 Shipments 

Are your gross margins.(% revenue) different post standards? If so, what are 

the causes? 

How do you explain differences, if any, between your expected product shipments with and 

without new energy efficiency standards? · 

G.4.3.l 

C.4.3.2 

G.4.4 Financial 

What assumption(s) did you make about your firm's market share 

post-standards? What Was your reasoning? 

Is your product mix significantly different from the overall industry 

proportions? 

How do you explain differences, if any, between your financial data and those presented for 

the industry as a whole? 

G.4.5 Impacts on the Business 

Please discuss y~ur interpretations of the cash flow analyses results for the standards cases. 

What do the results imply about the future of the business? Availability of capital? 

Are there additional financial impacts on new standards that are not being captured in the cash 

flow analysis? Please explain? (i.e., Tax Rate, Discount Rate, Working Capital, SG&A, 

R&D, Depreciation) 

G.4.6 Additional Economic Measures 

Please discuss any additional' measures, if any, that you might have developed that provide 

insight into the economic effects of changes in energy-efficiency standards. 

G.S COMPETITIVE IMPACTS ASSESSMENT 

Competitive impacts are assessed mainly in terms of market power, which is defined as the 

ability to set a given product price within a limited range. The Department is interested in 

manufacturer viewpoints on the extent to which new energy efficiency standards would increase 

market power and thereby lesson competition within the clothes washer industry. 

G.5.l How would new energy efficiency standards affect your ability to compete m the 

marketplace? 

G-3 
G.5.2 Do you believe that new energy efficiency standards would unfairly assist or harm the ability 

of some firms in the industry to compete? 

G.5.3 Please discuss examples, if any, wherein substantial consumer subgroups would experience 

economic or performance penalties as a result of new standards. 

G.5.4 Can your firm meet potential efficiency levels without changing your products in ways that 

consumers might care about? 

G.5..S Does your company have specific information for the Department on consumer preferences 

for product attributes potentially affected by new standards? 

G.6 EMPLOYMENT IMPACTS ASSESSMENT 

The impact of new energy efficiency standards on employment is an important consideration 

in the rulemaking process. This section of the interview guide seeks to explore current trends in 

clothes washer production employment and solicit manufacturer views on how domestic employment 

. patterns might be affected by new energy efficiency standards. 

G.6.1 How would your employment levels change under new energy efficiency standards? 

G.6.2 Would the workforce skills required under new energy efficiency standards be different 

enough to require extensive retraining or replacement of employees~ 

G.7 MANUFACTURING CAPACITY IMPACTS ASSESSMENT 

The information discussed here is important for developing an understanding of the investment 

cost inputs to GRIM. 

G.7.1 How would the imposition of new energy efficiency standards affect your capacity utilization 

and manufacturing assets levels at your domestic production facilities? 

G. 7 .1.1 Would any facilities be closed or downsized? 

G.7.1.2 Would any facilities be added or upgraded? 

G. 7 .2 If current capacity were to be made obsolete, would it be remodeled to accommodate a new 

product mix? _-

G. 7.3 Please estimate the value of stranded assets resulting froma new standard, and/or the costs 

of refitting the facility and carrying out the research, engineering, and other supporting 

activities required to accommodate the new product mix. 

G-4 
APPENDIX H: LIST OF ACRONYMS AND ABBREVIATIONS 

§: Section 

Act or EPCA: Part B of Title III of the Energy Policy and Conservation Act, Public Law 94-163, 

as amended by the National Energy Conservation Policy Act, Public Law 95-619, the National 

Appliance Energy Conservation Act, Public Law 100-12, the National Appliance Energy 

I' Conservation Amendments of 1988, Public Law 100-357, and the Energy Policy Act of 1992, Public 

Law 102-486 

ADL: Arthur D. Little 

AEC: Annual Energy Consumption 

A WC: Annual Water Consumption 

AEO: Annual Energy Outlook . 

AGA: American Gas Association 

AHAM: Association of Home Appliance Manufacturers 

ANO PR: Advance Notice of Proposed Rulemaking 

ARIMA: Auto Regressive-Moving Average 

ASME: American Society of Mechanical Engineers 

A WW A: American Water Works Association 

BLS: Bureau of Labor Statistics 

BOM: Bill of Materials 

Btu: British thermal unit 

CAPM: capital asset pricing model 

CEE: Consortium for Energy Efficiency 

CES: Consumer Expenditure Surveys 

H-1 
CIR: Current Industrial Reports 

CRADA: Cooperative Research and Development Agreement 

CRF: capital recovery factor 

CFR: Code.of Federal Regulations 

C02: carbon dioxide 

cu.ft.: cubic feet 

CW: clothes washer 

DOD: Department of Defense 

DOE: Department of Energy 

DOJ: Department of Justice 

· EA: Environmental Assessment · 

EEi: Edison Electric Institute 

EF: Energy Factor 

EIA: Energy Information Administration 

EJ: exajoules (1018 joules) 

ELCAP: Elrick & Lavidge Computerized Audit Program 

E.O.: Executive Order 

EPA: Environmental Protection Agency 

. EPRI: ·Electric Power Research Institute 

FASH: Financial Accounting Standards Board 

FEMP: Federal Energy Management Program 

ft.3

: cubic feet 

H-2 
f.o.b.: free on board 

FR: Federal Register 

G & A: General and Administrative 

GAAP: Generally Accepted Accounting Principles 

GJ: gigajoules (1<>9 joules) 

GRI: Gas Research Institute 

GRIM: Government Regulatory Impact Model 

H-axis: horizontal axis 

kWh: kilowatt-hour 

LBNL: Lawrence Berkeley National Laboratory 

LCC: Life-Cycle Cost 

LPG: Liquid petroleum gas 

MIA: Manufacturer Impact Analysis 

MEF: Modified Energy Factor 

MMBtu: million Btu 

Mt/a: megatons (106 metric tons) per year 

NAECA: National Appliance Energy Conservation Act 

nC)·cic: Cycles per year 

NEEP: Northeast Energy Efficiency Partnership 

NEMS: National Energy Modeling System 

NEPA: National Environmental Policy Act of 1969 

NES: National Energy Savings 

H-3 
NWEEA: WashWise- Northwest Energy Efficiency Alliance 

NOPR: Notice of Proposed Rulemaking 

NOx: nitrogen oxide 

NPV: net present value 

NYSE: New York Stock Exchange 

OIRA: Office oflpformation and Regulatory Affairs 

OMB: Office of Management and Budget 

ORNL: Oak Ridge National Laboratory · 

PBP: Payback period 

PEA: Pacific Energy Associates 

PECI: Portland Energy Conservation Inc. 

PG&E: Pacific Gas & Electric 

PNNL: Pacific Northwest National Laboratory 

PVC: Present Value of Costs 

PVS: Present Value of Savings 

Pwh: petawan-hours (10 15 wan-hours) 

Quads: quadrillion Btu 

R&D: Research and Development 

RECS: Residential Energy Consumption Survey 

·RECW: resource-efficient clothes washers 

RMC: remaining moisture content 

S & P: Standard & Poor's 

H-4 
Working Group: Clothes Washer Consumer Analysis Working Group 

H-6