### illustrative input distributions for incremental manufacturing costs. This figure assumes that data

is provided by three manufacturers.

Efficiency

- LeveIl
0

----- 34 Incremental Cost ($)

Efficiency
Level 2 23456; 8 Incremental Cost ($) Manuf- Mkt acturer .

Share Legend A 50%

- B 30% C 20%

--



### Figure 4.1 Illustrative Input Distributions for Incremental Manufacturing Costs 4.4.2 Variability in Costs between Manufacturers 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 manufacturer's cost distribution. This approach captures the richness of the data provided by manufacturers and ensures that the final cost distributions incorporate each individual manufacturer's cost distributions thereby accounting for variability in the data submittals. 4.4.3 Manufacturer Assumptions for Supplying Data AHAM in conjunction with DOE proposed the following items to be used as data collection guidelines for manufacturers: Each incremental MEF above the baseline is viewed as the prospective future standard level. Thus the data entries for energy and costs are those necessary to convert 100% of the company's production to that minimum standard level. Each incremental step is viewed as a separate entity. For example, if a company has some washing machines which already meet a new level above the baseline, then the costs shown represent the cost of converting the remainder of all machines. • All MEF levels are obtained by using the Appendix Ji test procedure. Whereas, the baseline calculations assumed a warm-rinse, this is not the case with the projected levels. Manufacturers may use whatever means they wish to achieve the individual levels. Cost and efficiency data may have some incremental "step"-type function. Thus, companies 4-3

### may know of technology that would enable then to reach a 20% improvement in efficiency,

but may not have the technology available to reach a 15% efficiency level. In this case, the costs and efficiency improvements for the 20% level would be entered into the line for both the 15%and 20% improvements. Companies may use information on product costs, capital and tooling, or factory improvements which have already been incurred. If a company has made product improvements to energy efficiency as a result of the May 14, 1991, Final Rule and anticipated future energy standards in excess of those implemented on May 14, 1994, then these costs may be used to develop the efficiency and costs for this data collection exercise. In this case, companies have been asked to use information on costs incurred that directly relate to energy efficiency improvements in excess of the 1994 standard. Companies may use this information as a basis with which to calculate conversion of 100% of their production to that minimum standard level, according to the levels shown in the accompanying chart. DOE worked with AHAM in developing a survey instniment which was then sent out by AHAM to manufacturers requesting detailed cost and energy-use data for each efficiency level. The energy and water-use data for the industry was obtained by applying shipment-weights to each manufacturer's data. This data is presented in Table 4.1.

4-4

### Table 4.1 Energy-use Data Product Characteristics Energy Use Water Use

Total Percent MEF Energy Clothes Hot Machine Dryer Improve- cu.ft/kW wldryer Container Water Energy Energy Water Use Water Use ment h/cycle kWh/cycle Cu.ft. kWh/cycle kWh/cycle kWh/cycle gal/cyci gal/cu. Ft. [1] [2] [3] [4] [5] [6] [7] [8] [9] Baseline 0.817 3.227 2.847 1.587 0.209 1.43 39.181 13.779 5 0.860 3.165 2.817 1.543 0.209 1.413 38.613 13.732 10 0.908 3.017 2.822 1.408 0.209 1.4 38.613 13.71 15 0.961 2.833 2.832 1.216 0.209 1.407 38.621 13.67 20 1.021 2.739 2.893 1.113 0.218 1.408 38.446 13.342 25 1.089 2.292 2.866 0.715 0.304 1.273 26.6 9.22

35 1.257 1.866 2.749 0.462 0.133 1.27 21.03 7.601 40 1.362 1.859 2.749 0.462 0.133 1.263 21.03 7.61 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 DATA AGGREGATION The Department prepared a spreadsheet to aggregate cost data obtained from the manufacturers. AHAM used this spreadsheet and transmitted the results to DOE. This procedure helped address manufacturer concerns on the possible disclosure of proprietary manufacturing costs. The aggregation spreadsheet was developed using Microsoft Excel and Crystal Ba11TM software. The following procedure is used to arrive at the industry incremental total variable cost distribution for a particular efficiency level from individual manufacturers' cost submittals: Each manufacturer's incremental material, labor and tooling costs are added to arrive at the

4-5

### manufacturer's "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 a triangular distribution for the incremental total variable cost. Samples are drawn from each manufacturer's incremental total variable cost distributions to create an industry incremental total variable cost distribution. The number of times a particular manufacturer's cost distribution is sampled relative to others is dependent on its relative market share. Figure4.2graphically 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 each having a 50%market share). The final cost distribution can also be presented using percentile values- 0, 10, 25, 50, 75, 90 and 100 percentiles. Crystal Ba11TM software was used to specify individual distributions and to sample them to arrive at the final distribution. Crystal Ba11TM 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 to arrive at the industry incremental total variable cost distribution for each efficiency level. Industry incremental total fixed cost distributions are obtained using the same steps as for the variable cost distribution. In addition, shipment weighted averages of the most likely costs, both fixed and variable, were calculated for each efficiency level as point values for incremental costs.

4-6Manufacturer A

Efficiency Manufacturer B Level X Mkt.share

___________ kt. share

________ 0 

# 1 4 5 6

I I 1Incremental Cost

### ($) ---I I o O u o oi 

### a

>' 

### 2 0 w > IS a, Total Rel. Probability

= 1.0



### Figure 4.2 Industry Cost Distribution Obtained by Sampling Each Manufacturer's 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.

4-7 1.00 2.75 4.50 6.24 8.00

Incremental Cost ($)

### Table 4.2 Aggregated Cost Data using "Most Likely" Values Product Characteristics Variable Costs per Unit Total Variable Total Fixed Increm.

Mfg. Cost No. of

Respondents Percent MEF Materials Labor Overhead Improvement cu.fi./ ($) ($) 

### ($) Costs ($ per Costs (S ($ per

kWh/cycle Unit) per Unit) Unit) ($) [1] [2] [12] [13] [14] [15] [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 15 0.961 3.18 0.10 0.04 3.32 1.01 4.33 5 20 1.021 10.71 (0.02) 1.80 8.89 6.21 15.10 5 25 1.089 36.14 8.61 0.09 44.84 19.29 64.13 5

35 1.257 86.73 10.94 7.78 105.45 22.72 128.17 5 40 1.362 87.15 10.94 7.78 105.87 22.72 128,59 5 45 1.485 125.58 15.52 14.51 155.61 24.67 180.28 4 50 1.634 132.40 15.68 14.51 162.59 24.67 187.26 4


### 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 of labor associated with the new technology to reach the particular standard levels in $ per washer unit. For colunm [14], companies were asked to indicate the incremental overhead cost associated with this standard level. On colunm [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 have variable cost increases necessary to bring all production up to the minimum of MEFO. 817 were asked to include this cost in the5%improvement line.

4-8

### Table 4.3 Industry Cost Distributions for 5%, 10%, 15% 20% and 25% Improvements Applied to an Existing V-axis Clothes Washer Product Characteristics Total Total Incremental Percent Percentile MEF VariableCosts Fixed Costs(q) Manufacturing Improvement cu.ft/kWh!cycle (j)($per Unit) ($per Unit) Costs($per Unit)

Baseline 0.817

_________________ _______________ _________________ 5 ___________

100 (Max.) 0.860 0.24 0.87 1.11 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 10 0.01 0.01 0.02 6 0.00 0.00 0.00 10 100 (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 0.00 0.01

0.00 0.02

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 10 (0.53) 0.24 0.90 0 (12.20 0.21 0.80

______________ 25 100 (Max.) _______________ 1.089 83.28 27.630 102.63 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 10 (0.30) 14.07 16.13

______________ 0

_______________ (12.20 4.85 1.28

4-9

### Table 4.4 Industry Cost Distributions for35%, 40%,45%and 50% Improvements Applied to a H-axis Clothes Washer Product Characteristics Total Total Incremental Percent Percentile MEF VariableCosts Fixed Costs(q) Manufacturing Improvement (b) cu.ft/kWh/cycle (j)($per Unit) ($per Unit) Costs($per Unit) Baseline ________________ 0.8 17 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 10 60.38 17.20 81.74 6 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.) 1.485 300.00 101.60 401.60

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


### The AHAM data submittal was used to develop a cost-efficiency graph. This graph is presented in Figure 4.3.

4-10

## AHAM Clothes Washer Data 450 400 350 0i 0) 250 C.,

. 200 150 

## .E 100 50 0 4 4

-

)

- + . 4

. I I I N- 0) N- C\1 LC)

(0 0) (0 00 (0 CO co

0) 0 cN c•) (0 d d (Baseline) (5%) (10%) (15%) (20%) (25%) (35%) (40%) (45%) (50%) Modified Energy Factor 

### Figure 4.3 Cost-efficiency Graph for Clothes Washers 100% c 90% Y50% 25% 10%

-SWA

4-11

### REFERENCES

1. Title 10, Code ofFederalRegulations, Appendix JandJl to Subpart B or Part 430-- Un?form Test Method for Measuring the Energy Consumption of Automatic and Semi-automatic Clothes Washers, January 1, 1999. 2. Morris, W., Technical Director for Regulatory Affairs, AHAM, personal communication. Letter to Bryan Berringer, Program Manager, Clothes Washer Rulemaking, U.S. DOE. December 2, 1997.

4-12

### CHAPTER 5. MANUFACTURING COST ASSESSMENT

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### TABLE OF CONTENTS
5.1 PROJECT OBJECTiVES 5-1 5.2 MANUFACTURING COST ESTIMATES AND ASSUMPTIONS 5-1 5.3 COST STRUCTURE OF THE SPREADSHEETMODEL 

### 5-4 5.4 RESULTSFROM THE ANALYSIS 5-7 5.4.1 Results for High-Volume Manufacturers 5-8 5.4.2 Results for Low-Volume Manufacturers 

### 5-12
LIST OF TABLES Table5.1 Summary of Clothes Washers Analyzed 5-1 Table5.2 Key Manufacturing Assumptions 

### 5-3 Table5.3 Assumptions for High / Low-Volume Manufacturer 

### 5-4 Table5.4 Major Manufacturing Processes 

### 5-5 Table5.5 Greenfield Investment for and Depreciation Life of Plant, Equipment 

### 5-9 Table5.6 Summary of Sensitivity Analyses 

### 5-12 Table5.7 Greenfield Investment for an Annual Production Volume of 1.5

and 0.3 Million Units 

### 5-14

LIST OF FIGURES Figure5.1 Full Product Cost 5-2 Figure5.2 Manufacturing Cost Assessment Stages 

### 5-4 Figure 5.3 Range of Cost Premiums for H-axis, high efficiency V-axis over

V-axis Baseline 

### 5-7 Figure5.4 Cost Composition by Washer Type 5-8 Figure5.5 Full Production Cost Breakdown by Sub-Assembly Level 5-8 Figure5.6 Investment Cost Breakdown by Major Manufacturing Process 

### 5-9 Figure5.7 Monte Carlo Simulation of Aggregated Baseline Cost 5-10 Figure 5.8 Monte Carlo Analysis: Relative Importance of Each Input Factor 5-11 Figure5.9 Range of Cost Premiums for Low Volume Manufacturers 5-13 Figure5.10 Averaged Cost Differentials by Category and Washer Type

from the Means 

### 5-14 Figure 5.11 Investment Requirements by Category 

### 5-15

5-i

### CHAPTER 5. MANUFACTURING COSTASSESSMENT

A manufacturing assessment was conducted to estimate the manufacturing costs associated with an increase in energy efficiency for clothes washers. This approach involved the disassembly of eight clothes washers, analysis of the materials and manufacturing processes, and the development of a spreadsheet model. The model was run using a Monte Carlo simulation to approximate industry manufacturing costs and to determine sensitivities to key inputs. The model contains separate manufacturing assumptions for both high-volume manufacturers (producing 1.5million units per year) and low-volume manufacturers (producing 0.3 million units per year). The analysis of low-volume manufacturers was done to capture the differentiated cost structure of this market group. Table 5.1 summarizes the type of clothes washer analyzed and whether or not it was analyzed for a high or low volume manufacturer. Table 5.1 Summary of Clothes Washers Analyzed

Clothes Washers High Volume Manufacturer Low Volume

Manufacturer Analyze two vertical-axis (V-axis) washers (baseline) X X Analyze four high-efficiency V-axis washer designs X Analyze two horizontal axis (H-axis) washer designs X X 5.1 PROJECT OBJECTIVES The project had five core objectives which were followed throughout the assessment: • Develop a clear and consistent manufacturing cost assessment methodology. • Build a detailed manufacturing cost assessment model that accurately predicts the cost differential of selected models. • Develop a model that estimates the cost premium of low-volume versus high-volume manufacturers of V-axis and H-axis washers. • Report the differential manufacturing costs in aggregated form or as a range to maintain confidentiality of the data. • Obtain input from stakeholders on the manufacturing cost estimates and assumptions to confirm accuracy. 5.2 MANUFACTURING COST ESTIMATES AND ASSUMPTIONS The categories used to summarize the manufacturing costs developed closely parallel the cost categories used by the Association of Home Appliance Manufacturers (AHAM). Full production cost is defined as the sum of direct material, direct labor and overhead (including investment

5-1

### depreciation).a Other cost elements (SG&A, R&D, and interest) are grouped under the nonproduction cost category. Together these costs make up the full factory cost of the product as shown in Figure5.1.

Full Cost of Product

Full Production Cost * Non-Production Cost

________ ________ 

## Iovertieaci Selling

I'idirect Latmr

- 

## raiKi. ieurcji

- Ca o

- IPcliCI MteriaI

- Afvertisirig & reff 1.111 Wih erts Mrtriuri:

- laos to ITild new orftflçvr] Depreialcori - . . . Sales person ror' I prCJdias.Dc produciori roinp. ± Towel TaxeS wp. etc. processes L1ranc related - Lpeitirw Warehousinq - compliance '0 essoto

- Dehvery

-

- cold I keepirg - Order anlryl pr000sslrlq. * Tax Reform Act of 1986, requires companies to measure cost of goods sold as the full production cost of the goods sold. 

### Figure 5.1 Full Product Cost 

### It is understood that the cost of specific models - or costs to individual manufacturers - will vary, depending on elements such as the product's precise characteristics, actual manufacturing processes, and the product mix in the factory (multiple clothes washer and dryer products). Also there are considerable differences in the levels of vertical integration as some manufacturers prefer purchasing partially complete sub-assemblies to manufacturing themselves. However, as this set of assumptions remain constant, the differential cost estimation methodology reduces the impact of plant-specific characteristics. In developing the cost model for the manufacturing cost assessment, several assumptions were made. These assumptions are based on general industry practice, determined from manufacturer

aOverhead costs in the AHAM data collection do not include investment depreciation
5-2interviews, 1992 U.S. census data, and internal Arthur D. Little sources. Table 5.2 summarizes the

key manufacturing assumptions used in the analysis.

_

_

_ 

### Table5.2 Key Manufacturing Assumptions Category Assumption Work Days per Year (days) 250 Average Shifts per day (shifts) * 2.0 Runtime per Shift (hours) 8 Lot Size (work days) Assembly Worker Downtime _____________________ 20% E i U i kd

% F Monte Carlo mean 85% qu pment ptme( wor ays) 

# I 75%to95% B d d W h 

# rMonte Carlo mean $23 ur ene our)

ages @er 

# 1 Range $17 to $28 Building Depreciation Life (years) 25 Conveyor Depreciation Life (years)

_____________________________________ 15 (Small Stamp 0-60 ton, (years) j 5 

# I Medium Stamp 60-600 ton, (years) t 5 T li D i ti Lif Large Stamp 600-1500 ton, (years) 10 oo ng epreca on e 1 Small IJM 0-15 0 ton, (years) t 5



# I Medium IJM 150-500 ton, (years) t 5

_______________________________

# L Large IJM 500-1250 ton, (years)

### t 10 Average Depreciation Life

' Monte Carlo mean 100% (adjusts equipment, tooling dep. lives5 1 Range 75%to 125% Maintenance Cost 

# j Monte Carlo mean 4% (% of Equipment Cost) Range 2.5to5.5% Utilities Cost 

# J Monte Carlo mean 2.1% (% of Materials Cost) Range ito3.5% Uili i C i JMonte Carlo mean 85%

t zaton apac ty Range 60 to95% I R ui Monte Carlo mean



# J 100%

- nvestment eq rements Range 50 to 150% Taxes (% of Factory Cost) 0.9% Insurance (% of Factory Cost) 0.8%

* Fabrication, 2.5 Shfls, Assembly 1.0 Shfls t Tooling depreciation l?fe is the maximum number of years that a stamping or molding die is expected to be used before a design iteration makes it obsolete. However, dies may wear out from use before their maximum design life. For metal stamping dies, we assumed two million hits, for injection molding (IJM) dies we assumed one million hits. For the purpose of depreciation, we select whichever leads to a shorter life. The design life of a die is factored by the average depreciation life variable which thus may increase or decrease its design (but not hit) life.

5-3

### Manufacturing plants are assumed to require investment for a "green field" site, including

all new equipment, tooling, installation, and building. It is also assumed that manual final assembly is necessary to reflect the need for flexibility in production of multiple models. Table 5.3 characterizes the two scenarios used in this analysis - high volume and low volume manufacturers. Low volume manufacturers suffered from several disadvantages, such as purchased parts costing 10% more than for their larger counterparts and higher overhead cost structures. Raw materials, on the other hand, were assumed to be price equivalent. The manufacturing volume for the low volume manufacturer was set at the lower limit of what industry sources consider to be a viable production capacity to compete in the overall mass market. 

### Table 5.3 Assumptions for High / Low-Volume Manufacturer Manufacturer 

### High Volume Low Volume Plant Size (total square feet) 1000000 250000 Conveyor Length (feet) 57600 9400 Production capacity (maximum units / year) 1500000 300000 Indirect Labor Penalty (on top of already increased assembly
time) 

### n.a. 10% Low Volume Part Cost Penalty __________________ 

### n.a. _________________



### 10%



### 5.3 COST STRUCTURE OF THE SPREADSHEET MODEL The manufacturing cost assessment methodology used is a detailed, component-focused technique for rigorously calculating the manufacturing cost of a product (direct materials, direct labor and some overhead costs). Figure5.2 shows the three major steps in generating the clothes washer manufacturing cost.
Raw Fabrication Finished

Materials Materials 

## Bill of Materials Purchased Parts 

## Processes Assembly Processes Figure 5.2 Manufacturing Cost Assessment Stages

5-4

### First, a baseline was established to compare against the high efficiency models. Two clothes

washers were chosen due to their high popularity in the 1997/8 market and the fact that they came from two different manufacturers. Following is a list of the key characteristics describing the baseline clothes washers:

• 27" overall width • 3 wash/rinse temperature settings • Unheated rinse • 3 Settings for Load Size • 2 wash/spin speeds • 8 or more wash cycles • Super capacity wash basket The first step in the manufacturing cost assessment was the creation of a complete and structured bill of materials (BOM) from the disassembly of the two baseline and six high efficiency models. The washers were dismantled and each part was characterized according to weight, dimensions, material and quantity. The BOM incorporates all materials, components, and fasteners with estimates of raw material costs and purchased part costs. Assumptions on the sourcing of parts and in-house fabrication were based on industry experience, information in trade publications and discussions with manufacturers. Interviews and plant visits were conducted at major manufacturing facilities to ensure accuracy on methodology and pricing. For more information on the BOM, see Appendix D. Following the development of a detailed BOM, the major manufacturing processes were identified and developed for the spreadsheet model. These processes are listed in Table5.4. 

### Table5.4Major Manufacturing Processes Fabrication Finishing Assembly / Joining Fixturing Washing Adhesive Bonding Stamping / Pressing (Large, Medium,_Small) Powder Coating

___________________________ Spot Welding Brake Forming Enameling ___________________________ Seam Welding Cut & Shearing Deburring Integral Fastner (i.e. clinching) Machining Other Fasteners Injection Molding (Large, Medium,_Small)

___________________________ Press Fitting Casting

___________________________ Inspection & Testing

5-5

### Information on equipment and tooling costs, typical processing cycle times, and materials

used for fabrication were obtained from Arthur D. Little 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 entered in the BOM and linked to the specific parts being fabricated. Each part / process had a time and labor cost assigned. For this analysis $23.00 per hour was used as the average fully burdened labor rate based on typical yearly wages and benefits for union employees. This parameter was investigated using sensitivity analysis because labor rates can vary considerably, depending on geographic location, availability of skilled labor, and level of union representation. In the final step of the cost assessment, assembly times and associated direct labor costs were estimated. Assembly cycle times were primarily derived from the disassembly of the selected 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 were finalized, a detailed summary was prepared for relevant components, subassemblies and processes. The BOM 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 bottom-up analysis, from the current average 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 conforms with data from the 1992 Census of Manufacturers. The 1992 Census indicates an average labor content in the household laundry equipment segment to be 2.1 labor-hrs per unit. However, experts estimate 1.8 hours is more accurate for 1999, as a savings of 0.3 hrs per unit, or approximately 14% for the industry over the past 7 years, could be expected. The indirect labor-hour per unit was multiplied by the average labor rate ($23) in order to estimate the indirect labor cost. The average direct labor hour per unit was 1.32 hours; therefore the indirect labor content was calculated to be on average 0.48 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$25per unit, while the indirect labor costs are estimated to be $13.50 per unit at the fully burdened $23 per hour labor rate.

5-6

### 5.4 RESULTS FROM THE ANALYSIS

5.4.1 Results for High-Volume Manufacturers 

### In total, eight washing machines were disassembled and analyzed - two V-axis baseline models, four high-efficiency V-axis washers and two H-axis models. Compared with the baseline, Figure 5.3 shows the total incremental cost range that the H-axis and the high efficiency V-axis exhibited from the Monte Carlo analysis.

$250.00
$200.00 0 Cl) D $150.00

- $100.00 $50.00 $0.00 -$50.00 I ____________________

_________________

_______ -J

1 ______________________________________

H-axis Washer Machine Type HE V-axis 

### Figure 5.3 Range of Cost Premiums for H-axis, high efficiency V-axis over V-axis

Baseline
The breakdown of costs by category also varies by washer type. Of the total costs, the proportion attributable to material increases for the high efficiency model, while the other components decreased. In Figure 5.4the breakdown is reported using average values by washer type.

5-7100% 90% 0 80% 70% 60% C55) cD 50% 40% 0 30% a) 20% 10% 0% V-axis H-axis HE V-axis Washer Type 

### Figure 5.4 Cost Composition by Washer Type
Figure5.5illustrates the cost breakdown of sub-assembly by washer type.

100% 90% 80% 70% U, 0 

# e 60%

50% 8 40% 5, 0 30%

20% 10% 0% 

### Figure 5.5 C a b in e IPum ps, Valves, & sc.&FInalAssy

_ C on tro Is

- I Dsuspension LTranny & Motor

_______ __ _____________

_ Tub & Basket

_ 

# H H

V-axis Baseline H-axis HE V-axis Washer Type 

### Full Production Cost Breakdown by Sub-Assembly Level

5-8100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 

### Figure 5.6 Investment Cost Breakdown by Major Manufacturing Process Figure5.6provides a breakdown of investment costs by major manufacturing process. Depreciation costs from fixed investments in equipment and tooling, a major overhead cost element, were estimated based on the requirements derived from the component cycle times and production volumes. Production volumes, in conjunction with equipment process times, determine the number of pieces of equipment necessary and the plant investment costs. Table5.5compares investments required to build manufacturing facilities and the depreciation lives of those investments. Table 5.5 Greenfield Investment for and Depreciation Life of Plant, Equipment 

### Hiqh Volume Manufacturer Investment Requirements (USD MM)

Equipment Tooling Bldg Total V-Axis H-axis HE V-axis $114.40

$96.02 $97.22 $34.17

$38.85 $35.20 $100.00

$100.00 $100.00 $248.58

$234.87 $232.42

___________ 

### Average_Dep reciation_Lives_(Years)

___________



### Equipment fooiing Bldg Total V-Axis H-axis HE V-axis 10.1

11.0 10.7 2.7

2.8 3.5 25.0

25.0 25.0 15.1

15.6 15.9

5-9 Baseline V-axis H-axis HE V-axis

### To evaluate the key 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 that then completes numerous iterations (10,000) to quantify the range of possible outcomes and the probability of each occurrence. Figure5.7shows the output from this simulation for the baseline V-axis washers.

10,000 Trials Forecast: High Vol: V-axis Baseline Frequency Chart 19 Outliers .024
- 239

.018 179.2 -Il -' rD .012 1195

-= = .006 59.75

.000 0 $200.00 $210.00 $220.00 $230.00 $240.00 USD 

### Figure5.7 Monte Carlo Simulation of Aggregated Baseline Cost Figure5.8illustrates the relative importance of all input factors on the final product cost of the V-axis baseline. Investment requirements and wages are the most important variables in determining the per unit washer cost. However, the importance of the investment factor is also due to its wide range(±50%).

5-10Sensitivity Chart Target Forecast: High Vol: V-axis Baseline

-1 -0.5 0 0.5 1

Measured by Rank Correlation Investment Relativity Factor .67

- Burdened Wages ($/hr) .67 

### Jf I Average Depreciation Life (%) -.21 Maintenance (% of Equipment Costs) .12 Actual Output (units/year) -.09 

### 1 Utility Cost(%of mat) .08 Uptime(%) -.08 

### [1

Figure 5.8 Monte Carlo Analysis: Relative Importance of Each Input Factor

In addition to the Monte Carlo simulation, which allows all parameters to vary simultaneously within the ranges given in Table 5.2, a sensitivity analysis was also conducted while holding other variables constant. The results of this analysis are presented in Table 5.6. Although Monte Carlo found wages to be the most important variable, this analysis identified Plant Investment factor as the critical variable. For the Plant Investment factor, we assume that 100% represents the baseline equipment cost. Actual higher or lower investment levels (on a percentage basis) can then be investigated.

5-11

### Table 5.6 Summary of Sensitivity Analyses Variation Found in

MC Variable Studied Input Manufacturing Cost Rank Parameter Variation V-axis H-axis HE

___________ 

### (baseline) V_axis*

_______ 

### 1 _______________________________ Labor Rate (vary hourly labor $17-28 $17 $19 $16
rate)

____________ 

### 2 Plant Investment (vary as % ±50% 

### $24 $23 $20

investment) 3 Equipment and Tooling (vary 7-12 yrs. $5 $5 

### $4

________ 

### depreciation)

____________ ____________ ___________ ___________ 

### 4 Actual Plant Output (vary output) 60-100% $5 $5 

### $4 5 

### Machine Availability (% work 70-95% 

### $4 $3 $3

_______ 

### days)

___________ ___________ __________ __________ 

### 6 Utility Costs - electricity, gas, 1-3.5% 

### $3 $6 $5 

### steam (% of washer material

costs) 7 Equipment Maintenance Costs 2.5-5.5% 

### $3 $2 $2

(vary as % of installed

______ 

### equipment)

__________ __________ _________ _________ *HE V-axis stands for the High Efficiency V-axis models studied 

### In summary, the estimate of the mean production cost of the high volume baseline vertical axis washer is $219. The cost premium for horizontal-axis technology ranges from $78 to $203 over the vertical-axis baseline (based on two samples) while the cost premium for high efficiency vertical axis technology ranges from $-34 to $187 (based on four samples). It should be noted that some of the high-efficiency V-axis models analyzed represent large departures from current V-axis baselines and would require complete retooling of most manufacturing facilities. Furthermore, patent protections could restrict adoption of these designs by other manufacturers. Finally, as a group, the high efficiency V-axis clothes washers considered are less efficient than the H-axis designs analyzed. 5.4.2 Results for Low-Volume Manufacturers The results from the analysis of low volume manufacturers (0.3 million units per year) are presented in Figure5.9. The figures present costs for low volume manufacturers relative to the mean value for the high volume manufacturer. Figure 5.9compares Monte Carlo simulation ranges from the low volume manufacturer to the high volume baseline V-axis cost.
5-12$300.00 0 Ci) 

### 2. $250.00 (I) 0 0 a $200.00 > $150.00 0(1) ax sioo.oo >E $5000 $0.00 a a -$50.00 Rrngeore boed on 2,000 Monto V-axis H-axis Washer Machine Type 

### Figure 5.9 Range of Cost Premiums for Low Volume Manufacturers Figure 5.10 presents the cost differential, by cost category, of low volume manufacturers compared to high volume manufacturers for both baseline V-axis and H-axis washers. Comparing low-volume to high volume manufacturing costs at the means of all input variables, it is to be expected that low-volume manufacturers' depreciation, and material costs are higher. Low volume manufacturers' labor costs increase mainly due to lower allocation efficiency and the additional 10% indirect labor cost penalty. Other costs (insurance, taxes, utilities, maintenance) also have a positive, but lesser impact.

5-13$40.00

$30.00

$20.00 o

$10.00

$0.00 Washer Type 

### Figure 5.10 Averaged Cost Differentials by Category and Washer

Type Depreciation costs from fixed investments in equipment and tooling, a major overhead cost element,were estimated based on the requirements derived from the component cycle times and production volumes. Production volumes, in conjunction with equipment process times, determine the number of pieces of equipment and the plant investment. To size of the plant and the number of pieces of equipment, we assumed an up-time of 90%. Table 5.7compares the investments required to build high and low volume V-axis and the H-axis plants and the depreciation lifetime of those investments.


### Table 5.7 Greenfield Investment for an Annual Production Volume of 1.5 and 0.3 Million

Units

Hard tooling for stamping and injection molding operations is not included in indirect material costs for either small or large manufacturers. A per-hit or simple straight-line depreciation

5-14 V-axis H-axis

### assumption is used to calculate the yearly amortized per unit cost, whichever leads to a shorter life.

These assumptions can vary greatly, and have a significant effect on the total investment and the depreciation element of the overhead costs. Low volume manufacturers face a proportionally higher investment hurdle than their high volume counterparts. A key driver is tooling costs which are more than 60% of the investments required for high volume manufacturers while the volume is just 20%. While large manufactures can wear out tooling in less than a year, small manufacturers may have to scrap tooling due to design changes after several years of use. On average, tooling expenses are 50% higher for small manufacturers and the average tool life is twice as long. Thus, large manufacturers have an inherent advantage in how quickly they can cycle through tooling changes since the penalty is lower.

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

# L I P

_

_______ ______ [IQuality

- -

_______ DJoining IMetaIWork 

# ± 0 Tooling OIJM 9 Stamping 9 Coating

High Vol V-axis Low Vol V-axis High Vol H-axis Low Vol H-axis 

### Figure 5.11 Investment Requirements by Category In summary, the estimate of the full production cost of the baseline vertical axis washer was $219 for large manufacturers. The cost premium for low manufacturers over the mean high volume baseline V-axis ranges from -$1 to $60 over for the V-axis design and $103 to $253 for the H-axis designs, depending primarily on design and variations in key input parameters (Figure 5.9).At the mean of all input variables small manufacturers producing V-axis washers have a cost disadvantage of $26. For H-axis washers this disadvantage increases to$35. The greenfield assumption possibly overstates the V-axis penalty as depreciation costs as observed in 10K reports are lower than calculated in this analysis. This indicates that at least a portion of current V-axis facilities are able to use fully depreciated equipment. This equipment may not be reusable to produce H-axis washers however. The cost disadvantages presented in Figure 5.10 is for production cost only and does not include any additional scale disadvantages that exist in non-production costs such as selling, general 5-15

### and administrative and R&D. Also the designs investigated may be patented, making them

impossible for small manufacturers to replicate, and increasing the cost disadvantage even more.

5-16

### CHAPTER 6. MARK-UPS FOR PRICE DETERMINATION

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### 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 (CES) 6-3 6.2.2.2 Dealerscope Merchandising Statistical and Marketing Reports 6-4 6.2.2.3 ELCAP Clothes Washer Sales Information 6-5 6.2.3 Manufacturer Price Information 6-5 6.2.3.1 Current Industrial Reports (CIR) 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 Sec's Financial Statements 6-8 6.3 SALES TAX 6-10 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 6-11 6.4.3 Manufacturer Price and Mark-ups for Different Efficiency Levels 6-12 6.4.3.1 Pre-ANOPR Analysis 6-12 6.4.3.2NOPRAnalysis 6-12

LIST OF TABLES Table 6.1 Retail Prices of Clothes Washers from 1986 through 1994 obtained from the

Consumer Expenditure Surveys 6-4 Table 6.2 Retail Prices of Clothes Washers from 1986 through 1995estimated from

Dealerscope Merchandising's Annual Statistical Reviews 6-4 Table 6.3 Shipment Weighted Retail Prices of Clothes Washers from 1994 through

1997 Based on ELCAP Retail Sales Data 6-5 Table 6.4 Manufacturer Prices of Clothes Washers from 1986 through 1996 Estimated

from Current Industrial Reports 6-6 Table6.5 Manufacturer Prices of Clothes Washers from 1986 Through 1996 Estimated

Using Data from the Aham Fact Book 6-6 Table 6.6 Retail Mark-ups for Clothes Washers from 1986 Through 1994 Using Retail

Prices from the Consumer Expenditure Surveys, and Manufacturer Prices from the Current Industrial Reports 6-7 Table 6.7 Retail Mark-ups Estimated from Different Data Sources 6-8

6-i

### Table 6.8 Gross Margins for Retailers of Electronics, Home Office Equipment and Major Appliances

................................................ 

### 6-9 Table 6.9 Manufacturing Cost of a Baseline Clothes Waher Estimated from the

Industry Cost Structure

.......................................... 

### 6-11

............

................. 

### LIST OF FIGURES Figure 6.1 Average Costs and Mark-ups for the Baseline Clothes Washer 6-1 Figure 6.2 Retail Mark-ups Estimated from Different Data Sources 6-8

6-u

### CHAPTER 6. MARK-UPS FOR PRICE DETERMINATION

6.1 OVERVIEW The following figure illustrates the representative prices and mark-ups for a typicala V-axis clothes washer being sold in the U.S. marketplace:



### Figure 6.1 Average Costs and Mark-ups for the Baseline Clothes Washer
To perform LCC analysis, the Department used a 2-step mark-up approach where: (a) the manufacturing costs (i.e., full production costs) are marked up to the manufacturer price using 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 taxilladdition to the retail price. This sales tax is accounted for by using a sales tax mark-up over the retail price of the clothes washer.The Department used a fixed retail mark-up of 1.40, and a fixed mark-up of 1.052to cover the sales tax. The manufacturer mark-up over full production costs was found to be 1.35 for the baseline unit.

am 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 of 0.817 (AHAM baseline). Currently no data exists that would allow the Department 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, typical values are used as default values for the baseline model. Since the life-cyclecost (LCC) analysis uses differential costs only, the absolute value of the baseline unit cost 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

### Based on the information above, the total mark-up from full production costs to consumer

price is 1.990. Section 6.2 of this document presents a discussion on retail mark-ups, presenting current and historical retail mark-ups for clothes washers. Section 6.3 presents a brief discussion on sales tax and its effect on consumer price. Section 6.4 outlines the Department's 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 consumers is the change (increase) in the purchase price of the appliance. A more stringent standard usually results in 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 of lowering consumer LCC which 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: (1) Consumer Expenditure Surveys, conducted by the Bureau of Labor Statistics in conjunction with the Bureau of Census' (2) Dealerscope Merchandising's Annual Statistical Surveys2 (3) ELCAP price database3 6-2

### Sources of information for the manufacturer price of clothes washers include:

(1) Current Industrial Reports, U.S. Department of Commerce4 (2) Association of Home Appliance Manufacturers's Fact Book, 1997 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 are divided by manufacturer prices from each of two sources for manufacturer price, thereby resulting in six different sets of estimates for retail mark-ups. Section6.2.5presents 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 characteristics. 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 non-prescription 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 50 states, a shipment weighted average sales tax of 5.2% was calculated).

6-3

### Table 6.1 Retail Prices of Clothes Washers from 1986 through 1994 obtained from the Consumer Expenditure Surveys Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 Purchase Price ($) 431.98 432.29 437.60 442.23 444.21 444.05 435.21 419.43 435.06 RetailPrice($) 410.63 410.92 415.97 420.37 422.25 422.10 413.70 398.70 413.56 

### 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. Dealerscope reports 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. 

### Table 6.2 Retail Prices of Clothes Washers from 1986 through 1995 estimated from

Dealerscope Merchandising's Annual Statistical Reviews Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 Retail Price($) 439.3 440.4 440.4 406.1 410.2 415.0 382.8 382.7 394.2 394.2

6-4

### 6.2.2.3 ELCAP Clothes Washer Sales Information The ELCAP service collects sales data (retail prices) on clothes washers 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.) 

### Table 6.3 Shipment Weighted Retail Prices of Clothes Washers from 1994 through 1997

Based on ELCAP Retail Sales Data Year 1994 1995 1996 1997 Retail Price

## ($) 393.00 371.92 390.07 388.21 

### 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 shipments 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 a 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 company's 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.
a ELCAP service was discontinued at the end of 1997. Clothes washer retail sales are currently being tracked by INTELLECT ASW. 6-5

### 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 Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 Mfgr. Price ($) 272.6 269.9 262.0

_______ 277.3

_______ 278.1

_______ 275.9

________ 277.6

_______ 283.8

________ 279.0

________273.2

_______ 275.2

________ 

### 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 Table6.5. 

### Table 6.5 Manufacturer Prices of Clothes Washers from 1986 Through 1996 Estimated

Using Data from the Aham Fact Book Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 Manuf. Price(S) 265.7 263.9 269.1 277.7 286.7 287.8 291.3 292.4 299.4 302.9 287.4 

### 6.2.4 Retail Mark-ups from Manufacturer and Retail Price Information 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 Manufacturer Prices from the Current Industrial Reports Year Retail Price (CES) Manuf. Price (CIR) Retail Markup 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 maximum of 1.62 (in 1988).

6-7Table 6.7 Retail Mark-uns Estimated from Different Data Sources

Year Retail Mark-up (Dealerscope) AHAM) Retail

Mark-up (Dealerscope) (CIR) Retail

Mark-up (CES) (AHAM) Retail

Mark-up (CES) (CIS) Retail

Mark-up (ELCAP) (AHAM) Retail

Mark-Up (ELCAP (CIR) Average

Retail Mark-up
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

l.8

_______

_______ - 

# = -e-ELcAPAHAM

1986 1988 1990 1992 1994 1996 Year 

### Figure 6.2 Retail Mark-ups Estimated from Different Data Sources 6.2.5 Retail Mark-ups Estimated from Sec's 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
6-8

### appliance retail companies (Best Buy, Circuit City Stores, Lowe's, 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 environment.a 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.b 

### Table 6.8 Gross Margins for Retailers of Electronics, Home Office Equipment and Major

Appliances Company

_________________________ Gross margin (Avg.: 1990-1996) % Revenue from Appliances Retail

mark_up* Retailer A 

### 25.2% 12.0 1.34 RetailerB 26.3% 

### 15.0 1.36 Retailer C 16.2% 9.0 1.19 

### RetailerD 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 "cost 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 include 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 earlier in 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.

aSource: Standard and Poor's Stock Reports on Circuit City Stores and Best Buy. b Source: Standard and Poor's Stock Reports on Best Buy. 6-9

### 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 year1995, 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 is used to cover the sales tax. In the year 1994, for which retail price is available from all the three sources, namely, CES,' Dealerscope2 and ELCAP,3 the average clothes washer retail price is $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 0.817), the Department used $400.25 as the baseline retail price for the clothes washer rulemaking analysis. Adding a5.2%sales tax results in a baseline consumer price of $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 of5.2% on top of the retail price results 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 entitled "Financial Impact of DOE Top Loading Horizontal Axis Standards on U.S. Washing Machine Manufacturers." 6 Hence, the Department of Energy used a retail mark-up of1.40, and a sales tax mark-up of 1.052 to arrive at the consumer price for the clothes washer rulemaking analysis. 

### 6.4 MANUFACTURER MARK-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 manufacturers's 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
6-10

### eventually to consumers; whereas a lower mark-up implies 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-up of 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 enough both (1) to recover costs and (2) to earn a profit that provides a satisfactory return on investment. Table 6.9 Manufacturing Cost of a Baseline Clothes Waher Estimated from the Industry

Cost Structure Per Unit 

## Cost($) Material 148.7 Labor 29.3 Overhead 33.5 Full Production Cost 211.5 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 costof 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% of revenues). 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 Chapter5,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:

Manufacturer Price

Baseline Manufacturer Mark-up
= Full Production Cost

6-11