Analytical Framework for Clothes-Washer Analysis Approaches Key-Inputs Analysis Key-Outputs

Industry Review derby Firrrnsl Products Preliminary Base Case * Shsnment Forecast Quanlitayve Analysis Historical lnlormalion Qualitative Anelesis-lntervieecs Marked Finn Segmentation
Existing Technology Ophenn Ideetiliwtatiee & Prototype Designs Screening Analysis Design-Opien Approach 05J

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IS EnergylVaarer cares I

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NtP VI Md IlAse 0gb T • Histedcal shipments I Other etticienaixs • Prod cut saturation EtEciency • Voluntary iniliatioes distybuliun

.... Revised Pre-ANOPR Analyses

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Manuiactvrer Prices i h U t i t I d C h flue wt ncera n y r ushy as - Average Monafostorer Impost l C I s Analysis Pouo

• Standard Level Cases . conpessuepoetu • Etficiency Disbihucer (Industry profile, uueutasn l,opours ten all Cases lrteMews Sob-grpanaiys) 

# -LI Impost Assessment SRIM Analysis

________________________ Cost IC), Price )P), -

. Sub-group Cash-flows Quantity 10), FinanUal IF)

NEMS

NOPR Regt.xlatery Impact Analysts

FINAL RULE Revise Analysis



### Figure 2.1 Clothes Washer Rulemaking Framework

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### 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 design options for consideration. All technologically feasible design options are candidates in this initial assessment.
2.2.3 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 distribution by 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 existing standard. To determine the characteristics of the baseline unit in the screening
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### analysis, the Department gathers information from trade organizations, manufacturers, and

consultants with expertise in specific product types. 2.3.3 Design Options/Efficiency Levels Following the development of an initial list of design options during the technology assessment and the screening analysis, the Department, in consultation with interested parties, selects 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 engineering 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(l)(2)(A), 42 U.S.C. §6295(l)(2)(A). Therefore an essential role of the engineering analysis consists of identifying the maximum technologically feasible level. The maximum technologically feasible 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
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### believes that the design options comprising the maximum technologically feasible level must have

been physically demonstrated in at least a prototype form to be considered technologically feasible. Three methodologies can be used to generate the manufacturing costs needed for the engineering analysis. These methods include: 1) the design-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. The 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 variability in 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 the economic 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 standard 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 presumption clause. 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.

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### 2.5.1 LCC Spreadsheet Model

The LCC analysis is conducted using a spreadsheet model developed in Microsoft Excel for Windows 95, combined with Crystal Ba11TM (a commercially available software program). The Model 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 moredetailed analysis. The range of candidate standard levels typically includes: (1) the most energyefficient combination 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 efficiency levels 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 candidate standards levels may be selected. Payback is calculated based on the same inputs used for the 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. 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 BallTM. Payback periods are first calculated between the new standard level and each washer efficiency being sold in the year the new standard becomes effective. 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.) correspond to those outlined in the DOE test procedure, Appendix Jl. The result is a single payback value and not a distribution. The payback is calculated for the expected effective year of the standard. 2.5.2 Price Manufacturer and retail prices are needed to determine how manufacturing costs and associated margins, 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 to determine 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
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### other market experts to predict consumer willingness to pay higher prices; and 3) applying various

mark-ups over manufacturing costs. Consistent with the Process Rule, and building on the estimates generated by various approaches, retail prices are described within a range of uncertainty.

2.6 NATIONAL IMPACTS ANALYSIS The national impacts analysis assesses the net present value (NPV) of total consumer LCC, energy (and water, if appropriate) savings and net employment impacts. An assessment of the aggregate impacts at the national level is conducted for the ANOPR and NOPR. 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 as the difference between two projections: a base case and a standards case. Analysis includes estimated energy savings by fuel type for electricity, natural gas, and 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 net present value (NPV) of total consumer LCC, national energy savings (and water savings, if appropriate) and net employment impacts. Each of the above are determined for selected trial standard 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 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 95to 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.

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### 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. For this rulemaking the shipment and national energy savings models are combined into a single spreadsheet model. Shipments are forecasted by using a accounting/decision tree model. This takes into account the 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. Details of this analysis are described in Chapter 9 of this document.
2.6.3 Net National Employment Impacts The July 1996 Process Rule includes employment impacts among the factors to be considered in selecting a proposed standard. The Department estimates the impacts of standards on employment for appliance manufacturers, relevant service industries, energy suppliers, and the economy in general. Employment impacts are separated into net national and direct impacts. Direct employment impacts would result if standards lead to a change in the number of employees at manufacturing plants and related supply and service firms. Direct impacts are discussed in the section on manufacturer impact analysis. Net national impacts are impacts on the entire national economy. Net national 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). Net national 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 expected 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 are estimated for major sectors of the U.S. economy. Public and commercially available data sources and software will be utilized to estimate employment impacts. Chapter 13 of this document discusses the net national employment impacts in greater detail. All methods and documentation are available for review.

2.7 CONSUMER ANALYSIS The consumer analysis evaluates impacts to any identifiable groups or consumers, such as consumers of different income levels, who maybe disproportionately affected by any national energy efficiency standard level. The Department evaluates 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
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### a standard to consumer sub-populations. To the extent possible, DOE obtains estimates of the

variability in 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 is determined using the LCC spreadsheet model.

2.7.1 Purchase Price Increases The Department is sensitive to purchase price increases to avoid negative impacts to identifiable population groups such as low-income households. Additionally, the Department assesses 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 consumer input.

2.8 MANUFACTURER IMPACT ANALYSIS The manufacturer analysis estimates the financial impact of standards on manufacturers and calculates impacts on employment, and manufacturing capacity. Prior to initiating the detailed manufacturing impact analysis the Department prepares an approach document. In the approach document, the Department describes and obtains comments on the methodology to be used in performing the manufacturer impact analysis. While the general framework serves as a guide, the Department tailors the methodology for each rule on the basis of stakeholder comments. Three important elements of the approach consist of the preparation of an industry cash-flow, the development of a process to consider sub-group cash-flows, and the design of an interview guide. The manufacturer impact analysis is conducted in three phases (see Figure 2.2). Phase 1 consists of two 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 GR11VI is 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, and identifying the
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### differing impact of standards on these sub-groups. Phase 3 also entails evaluating additional impacts

on employment and manufacturing capacity. Manufacturer Impact Analyses PHASE 1: Industry Profile 4 Industry Characterization Issue Identification

Manufacturinq Costs Price Forecasts



### ANOPR 

## .r Shipment Forecasts Estimate Industry Cash Flow

Estimate Manufacturer Costs Estimate Prices and Shipments Cash Flow Analysis I

on 

### Figure 2.2 Flow Diagram of the Manufacturer Impact Analysis Methodology
2.8.1 Industry Cash Flow A change in standards affects the analysis in three distinct ways. Increased levels of standards will require 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 uses manufacturing costs, shipments forecasts, and price forecasts developed for the other analyses. Financial information, also required as an input to GRIM, is 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 depreciation, 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
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### 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 P provides a description of GRIM's operating principles.
2.8.2 Manufacturer Sub-Group Analysis Using industry "average" cost values is not adequate for assessing the variation in impacts among sub-groups of manufacturers. Smaller manufacturers, niche players, or manufacturers exhibiting a 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 uses 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 is 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 conducts detailed interviews with as many manufacturers as is possible to gain insight into the potential impacts of standards. During these interviews, the Department solicits the information necessary to evaluate cash flows and to assess employment and capacity impacts. Firm-specific cumulative burden is also 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 manufacturer impact analysis process draws on multiple information sources, including structured interviews with manufacturers and a broad cross-section of interested parties. Interviews are conducted in any and all phases of the analyses as determined in Phase 1 of the MJA. 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 collects information from relevant industry and market publications, industry trade organizations, company financial reports, and product literature. This information is an aid in the development of detailed and focused questionnaires which are used in 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.
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### The Phase 3 (sub-group analysis) questionnaire solicits information on the possible impacts

of potential efficiency levels on manufacturing costs, product prices, and sales. Evaluation of the possible impacts on direct employment, capital assets, and industry competitiveness also draw heavily on the information gathered during the interviews. The questionnaires solicit both qualitative and quantitative information. Supporting information is requested whenever applicable. Interviews are conducted according to DOE procedures. Interviews are 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 are requested to identify all confidential information provided in writing or orally. Approximately two weeks following the interview, an interview summary is provided to give participants the opportunity to confirm the accuracy and protect the confidentiality of all collected information. All the information transmitted is considered, when appropriate, in DOE's decision-making process. However, confidential information is not made available in the public record.

2.9 COMPETITIVE IMPACT ASSESSMENT Legislation directs the Department to consider any lessening of competition that is likely to result from standards. It further directs the Attorney General to gauge the impacts, if any, from any lessening of competition. The competitive analysis focuses on assessing the impacts on smaller, yet significant, manufacturers. The analysis is based in part on manufacturing cost data and on information collected from interviews with manufacturers, consistent with Phase 3 of the manufacturer impact analysis. DOE makes an effort to gather and report firm-specific financial information and impacts. The Department of Justice (DOJ) assisted in drafting questions used in the manufacturer interviews. These questions seek to assess the likelihood of increases in market concentration levels and other market conditions that could lead to anti-competitive pricing behavior. The manufacturer interviews focus on gathering information that would help in assessing asymmetrical cost increases to some manufacturers, increased proportion of fixed costs that could increase 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 used a version of Energy Information Administration's (ETA) widely recognized National Energy Modeling System (NEMS) for the utility and environmental analyses.
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### NEMS is a large multi-sectoral partial equilibrium model of the U.S. energy sector that has been

developed over several years by the ETA primarily for the purpose of preparing the Annual Energy Outlook (AEO). NEMS produces a widely recognized baseline forecast for the U.S. through 2020 and is available in the public domain. The version of NEMS to be used for appliance standards analysis is called NEMS-BRS, and is based on the AEO99 version with minor modifications.a 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 and 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-BRS is first run exactly as it would be to produce anAEO forecast, then a second run is 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).

2.11 ENVIRONMENTAL ASSESSMENT 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 environmental concern addressed is emissions from fossil fuel-fired electricity generation. Power plant emissions include oxides of nitrogen (NOx) and sulfur (SO2), as well as carbon dioxide (CO2). 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 is also a precursor gas to urban smog and is particularly detrimental to air quality during hot, still weather. CO2 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 uses EIA's widely recognized NEMS for the appliance environmental analyses. The version of NEMS used for appliance standards analysis is called NEMS-BRS, and
aFor more information on NEMS, please refer to the U.S. Department of Energy, Energy Information Administration documentation. A useful summary isNational Energy Modeling System: An Overview 1998, DOE/EIA-058 1(98), February 1998. DOE/EtA approves use of the name NEMS to describe only an official version of the model without any modification to code or data. Because our analysis entails some minor code modifications and the model is run under policy scenarios that are variations on DOE/EIA assumptions, the name NEMS-BRS refers to the model as used here (BRS is DOE's Building Research and Standards office, under whose aegis this work has been performed).
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### is based on theAE099version with minor modifications. NEMS-BRS is run exactly the same way

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 environmental analysis, the output is the forecasted physical emissions. The net benefits of a standard will be the difference between emissions estimated by theAE099version ofNEMS-BRS and those it estimates with a standard in place.

2.12 REGULATORY IMPACT ANALYSIS DOE prepared a draft regulatory analysis pursuant to E.O. 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 FR 51735 (October 4, 1993). Eight major alternatives were identified by DOE as representing feasible policy options to achieve consumer product energy efficiency. Each alternativeis 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 examined for this rulemaking. The Department seeks comments on this approach. Table 2.1 Comparison of 1994 and 2000 Non-Regulatory Alternatives to Standards Alternatives Examined in 1994 Alternatives to Examine in 2000 -No action -Consumer tax credits -Manufacturer tax credits -Performance standards -Consumer rebates -Prescriptive standards -Voluntary standards -Enhanced labeling and consumer education -No new regulatory action -Consumer tax credits -Manufacturer tax credits -Performance standards -Consumer rebates
-Voluntary energy efficiency targets Enhanced Public Education & Information Low Income & Senior Subsidy -Mass Government purchases -Early Replacement

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### CHAPTER 3. MARKET AND TECHNOLOGY ASSESSMENT

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### TABLE OF CONTENTS 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 3-5 3.6 PRICE DISTRIBUTION 3-6 3.7 PRODUCT CHARACTERISTICS 3-9 3.8 MARKET SATURATION 3-12 3.9 VOLUNTARY PROGRAMS (TO INCREASE WASHER EFFICIENCYI PROMOTE HORIZONTAL AXIS MACHINES) 3-13 3.10 FUEL USE 3-13 3.11. INDUSTRY COST STRUCTURE 3-15 3.12 INVENTORY LEVELS AND CAPACITY UTILIZATION RATES 3-17 3.13 TECHNOLOGY ASSESSMENT 3-18 3.14 ENERGY EFFICIENCY 3-18 3.15 DETERMINING MEF VALUES FOR VERTICAL AXIS COMPACT CLOTHES WASHERS 3-19
LIST OF TABLES Table 3.1 Market Shares

## (%)and Consolidation in the Washer Industry, 1977-1998

. . . 

### 3-2 Table 3.2. Number of Establishments in Household Laundry Equipment (SIC 3633) 3-2 Table 3.3 Industry Unit Shipments 3-3 Table 3.4 Washer Shipments by Access 3-4 Table3.5 Washer Sales by Type of Outlet 3-5 Table 3.6 Clothes Washer Unit Sales by Price Range 3-6 Table 3.7 Producer Price Index, Annual Average (1982=100) 3-7 Table 3.8 Consumer Price Index (All Urban Consumers); Annual Average

(1982-1984= 100) 3-8 Table 3.9 Washer Characteristics (1995) 3-9 Table 3.10 Brand Names, Price Level and Market Share of Major Manufacturers 3-11 Table 3.11 Washer Market Share by Brand 3-12 Table 3.12 Market Saturation by Fuel Type Among Housing Units with

Clothes Washers 3-14 Table 3.13 Employment and Earnings 3-15 Table 3.14 Information from the 1992 and 1997 Census of Manufactures 3-15 Table 3.15 Summary of SEC 10-K Reports of Appliance Manufacturers 3-16 Table 3.16 End-of-year Inventory, 1977-1997 3-17 Table 3.17 Full Production Capacity Utilization Rates* (%) 3-17 3-i

### Table 3.18 Product Characteristics of Clothes Washer Tested

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### 3-18

Table 3.19 Summary of Appendix J and J 1 Test Results

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### 3-19 Table 3.20 Estimated MEF Values for 1.56and 1.96 Cubic foot Clothes Washers

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### 3-20

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### LIST OF FIGURES Figure 3.1 Washer Market Share by Type, 1998 3-4 Figure 3.2 Producer Price Index for All Commodities and for Laundry Equipment,

1967-1998 3-7 Figure 3.3 Consumer Price Index for All Items and for Laundry Equipment, 1982-1999

. 

### 3-8 Figure 3.4 Washing Machine Market Share by Cubic Capacity 3-10 Figure3.5 Washing Machine Market Shares by Speed 3-10 Figure 3.6 Clothes Washer Saturation Rates (% of Household Units) 3-13 Figure 3.7 Water Heater and Dryer Fuel Use 3-14 Figure 3.8 Washing Machine Industry Cost Structure as a Percentage of Revenues

. . . . 

### 3-16

3-u

### CHAPTER 3. MARKETANDTECHNOLOGYASSESSMENT

3.1 INTRODUCTION This chapter provides a profile of the clothes washer industry in the United States. The preliminary market and technology assessment presented in this chapter is developed from publicly available information. This information is 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 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 of 24 inches or less and a washer tub capacity of less than 2.0 cubic feet and/or a dryer drum capacity of less than 4.4 cubic feet".' This definition is different than DOE's definition of a compact washer which is a washer with a capacity of less than 1.6 cu. ft.. 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 MARKETSHARES As reported in Appliance Magazine, there are five maj or 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. Table 3.2 shows that the number of household laundry equipment manufacturers, as reported in the U.S. Census of Manufactures, has also been relatively constant in the past 10 years.

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### 'table 3.1 Market Shares (%) and Consolidation in the Washer Industry, 1977-199S Miiith1thiir 1977 1982 1990 1998 Whirlpool 45 41 51 53
General Electric 20 20 16 15
White Consolidated * 14 * * (bought Frigidaire in

__________________ ______________ 1979)

_________________ Electrolux * * 12 ______________________ 7 (bought White Consolidated in

___________________ ______________ ___________________ 1986) Magic Chef * 5 * ______________________ * Maytag 15 15 18 21 (bought Magic

__________________ _____________ __________________ Chef in_1986) Raytheon! * * 3 _____________________ 4 Goodman * Insignificant market share or no production Source: Share-of-Market Picture for (year), Appliance Magazine, September 1978, 1983, 1991, 1999•4


### Table 3.2. Number of Establishments in Household Laundry Equipment (SIC 3633) Firms entering(+); Firms exiting (-) 1963 1967 1972 1977 1982 1987 1992 1997 All establishments 39 35(-4) 29 (-6) 31 (+2) 25 (-6) 18 (-7) 17 (-1) 17 Establishments with 20 or more employees 28 28 24 (-4) 24 20 (-4) 16 (-4) 15 (-1) 15 Source: U.S. Bureau of the Census, Census of Manufactures, 1977, 1992, 1997.



### 3.4 HISTORICAL SHIPMENTS Clothes washer shipments in the U.S. have been increasing at an average rate of2.15%over the last 10 years. Table 3.3 provides industry unit shipments and industry dollar sales of clothes washers for the twelve year period 1987-1998.

3-2

### Table 3.3 Industry Unit Shinments

Year Shipments (Domestic+ Exports) Estimated Industry Dollar Sales Shipments

(in 000's) (Manufacturer dollar value in (Exports) $mil) (in 000's) 1987 5,998 - 247.6 1988 6,190

- 403.3 1989 6,252

- 385.3 1990 6,192 1,175.2 601.2 1991 6,197 1,783.5 692.7 1992 6,515 1,898.0 760.8 1993 6,793 1,986.1 769.7 1994 7,035 2,106.3 894.2 1995 6,901 2,090.2 824.6 1996 7,129 2,049.0 917.6 1997 6,326* 2,286.2 1998 6,835* 2,151.4** 1999 7,313* 2,276.8** :?omestic shipments only Domestic dollar sales oniy 

### In recent years 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 publically available data shows a horizontal axis market share of 6.6% (see Table 3 4)•6These data may not include all major retailers and therefore has a margin of error. In the last several years 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 such as the The Energy Star program also promote high efficiency washers, encouraging the sales of resource efficient clothes washers.7 Major U.S. manufacturers offering H-axis machines at the time of this TSD (August 2000) are as follows: Maytag, Electrolux (Frigidaire), and General Electric. Goodman Manufacturing (Amana) also had a horizontal axis machine on the market which at the time this document was written was not in production. Whirlpool recently introduced an energy efficient vertical axis (V-axis) machine on the market. General Electric offers a horizontal axis machine made by another manufacturer.

3-3

### Table 3.4 Washer Shipments by Access

Access 1998 8 19996 Top Load! Vertical 93.9% 93.4% Front Load! Horizontal 6.1% 6.6%

Portable

- Regurde



### Figure 3.1 Washer Market Share by Type, 1998 Source: Statistical Survey and Report, Dealerscope, June, 1999.8

3-4

### 3.5 RETAILERS

Distributors are virtually non-existent in this market. Manufacturers deal directly with retail outlets or buying groups formed by smaller appliance stores (see Table 3•5)•9
Table 3.5 Washer Sales by Type of Outlet

Outlet 1997 Market Share8 1998 Market Share1° Mass Merchandiser 32.6 35.3 Appliance Store 28.8 21.0 CE Superstore 10.4 12.2 Department Store 7.9 9.3 Furniture Store 5.0 1.8 Discount Dept. Store 3.7 5.1 Home Center 3.7 5.9 Hardware Store 1.1

- Home Builder 1.0 3.9 Warehouse Club 1.0 3.2 Kitchen Remodeler 0.2

- Catalog Showroom 0.1

- Landlord 0.1

- Mail Order/Direct 0.1

- Other 4.3 2.3

3-5

### 3.6 PRICE DISTRIBUTION

Table 3.6 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 3.6 Clothes Washer UnitSales byPrice Range

Price Range Market Share 1997 Under $200 1.2% $200-299 6.3% $300-399 3 8.2% $400-499 3 1.7% $500-599 13.7% $600-699 5.6% $700-799 0.7% $800 andabove 2.6% Note: Neptune, the Maytag H-axis washer, is not covered in this table. 

### Tables 3.7 and 3.8 provide the producer and consumer price index for laundry equipment. Average producer and consumer prices for laundry equipment rose less steeply than for other commodities.

3-6

### Table 3.7 Producer Price Index. Annual Average (1982=1 O(fl Year 411 ciuitdItie 

# ) Lauiuliy 

### ej!!!Is1i1 1967 33.4 .. 48.5 1972 39.8 52.2 1977 64.9 73.1 1982 100 100 1987 102.8 107.7 1988 106.9 107.6 1989 112.2 109.7 1990 116.3 112.4 1991 116.5 112.5 1992 117.2 111 1993 118.9 110.5 1994 120.4 109.6 1995 124.7 108.8 1996 127.7 109.5 1997 127.6 105.4 1998 124.4 104.2 Source: U.S. Bureau of Labor Statistics, Producer Price Indextz



### 140 --AII

___ ___ 

### index 80 

# J

____ _____ --

### Laundry

__________________ 

### uidex

0 i I I I I I I I I I I

N- N- 0)

- C) LU N - (ON-cO cc 0)0 

### c 0)0)0) 0)0)0)0) 0) '- '- '- '- '- - '- - 

### Figure 3.2 Producer Price Index for All Commodities and

for Laundry Equipment, 1967-1998

3-7

### Table 3.8 Consumer Price Index (All Urban Consumers); Annual Average (1982-1984 = 100)

_ 

# J ..!1 ei Lttndr Ijipueid 

## j



# )

_______________________________

1982 96.5 

### 96.3 1983 99.6 100.6 1984 103.9 103.2 1985 107.6 104.0 1986 109.6 104.8 1987 113.6 104.1 1988 118.3 105.1 1989 124.0 105.9 1990 130.7 107.2 1991 136.2 106.2 1992 140.3 105.8 1993 144.5 106.7 1994 148.2 109.0 1995 152.4 109.6 1996 156.9 111.9 1997 160.5 113.1 1998 163.0 112.5 1999 166.6 110.5 Source: U.S. Bureau of Laoor Statistics, Consumer Price Index'3



## 200

_________________ 

### -i-AII items index 

## 150
- ______________ _____ 

## 100
- ____ 

### --Laundryequipment index 

## 50

0- II 11111 I liii II 

### c.'J , 

### co

- 

### co co co o a, a, C) 0) 0) 0) 0) 0)

- - - - - - 

### Figure 3.3 Consumer Price Index for All Items and for

Laundry Equipment, 1982-1999

3-8

### 3.7 PRODUCT CHARACTERISTICS

Clothes washers of different capacities and configurations are sold in the market. A survey by Elrick and Lavidge for the year 1995, provided information in Table 3.9 below.

Table 3.9 Washer Characteristics (1995)14

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

3-9

### Figure 3.4 shows market shares by capacity, and Figure 3.5shows market share by number

of washer speeds.

37%/

____ 

# / [I3.0cuft E < 3.0 Cu ft

43%


### Figure 3.4 Washing Machine Market Share by Cubic

Capacity Source: Statistical Survey and Report,Dealerscope, June 2000[, 2000 #405]

Variable 1 speed 23%

>2sPeeds(

# \L_

.

2 speeds 40%
Figure 3.5 Washing Machine Market Shares by Speed Source: Statistical Survey and Report, Dealerscope, June 2000[, 2000 #405]

3-10

### Table 3.10 provides the brand names used by the different manufacturers and the price level

for a given brand. Table 3.11 shows the market shares by brand. Manufacturers also build dryers, usually marketed as a set washer with matching dryer. See Appendix A for a list of manufacturers and addresses.
Table 3.10 Brand Names, Price Level and Market Share of Major Manufacturers'5

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 Raytheon* Amana Mid Speed Queen Value EPRI sources: Statistical Survey and Report,DealerScope, May 1996, p. 60; Quality (i.e., price) levels taken from

Smith Barney Whirlpool/Maytag Company Report 1/17/96. *Raytheon sold the Amana brand to Goodman Holdings, Inc. and licences the SpeedQueen brand to them.

3-11

### Table 3.11 Washer Market Share by Brand Brand 1997 Market Share 10 1998 Market Share'6 1999 Market Share 17 Sears/Kenmore 25.6 26.8 31.0 Whirlpool 20.4 20.7 19.5 Maytag 17.1 20.4 19.2 General Electric 12.4 12.3 11.6 Amana 4.5 4.0 7.1 Frigidaire 4.3 5.9 6.9 Roper 3.3 3.1

- Hotpoint 2.8 2.1 1.2 White-Westinghouse 2.1 1.9 1.6 Admiral 1.3 1.0

- Speed Queen 1.2

- - Gibson 1.0

- - Kitchen Aid 1.0 1.0

- Magic Chef 0.5

- 1.1 RCA 0.5

- - Montgomery Ward 0.4

- - Kelvinator 0.3

- - Asko 0.2

- - Crosley 0.2

- - Other 1.9 0.8 0.8



### 3.8 MARKET SATURATION The saturation of clothes washers varies with the type of housing and income level. The 1990 U.S. Census gives a clothes washer penetration of 76%. Appliance Magazinegives a saturation for 1998 of 79.8%.18 The saturation rates for 1987 through 1998 are shown in Figure 3.6.16

3-1285 80______

- 

# _____ 0 

## I 75

## -f C 70 

# 65 filiTi

# 11111,

1987 1992 1993 1994 1995 1996 1997 1998 

### Figure 3.6 Clothes Washer Saturation Rates(% of Household Units), 1987-

1998 3.9 VOLUNTARY PROGRAMS (TO INCREASE WASHEREFFICIENCY/PROMOTE HORIZONTAL AXIS MACHINES) The Department researched voluntary 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 summarized. The entire report is located in Appendix I. 3.10 FUEL USE Although clothes washers are all electric powered, most of the energy used by clothes washers is for heating water. 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. Figure 3.7 shows the distribution of water heater and dryer fuel for households having a clothes washer and is taken from RECS which uses data collected in l993.' The data are also shown in Table 3.12. The data presented is for households having a clothes washer. Approximately53%of households having a washer use natural gas to heat water and 39% use electricity. Approximately 73% of households have a electric dryer and 18% have a gas dryer.

3-13

### Saturation d Fuel Type usina Uuits Wth acthesvbshe.

40 35
c30 0 •25 (1)20 4- 15 0- 10 5 0 Rpeds
ter H ne 

### Figure 3.7 Water Heater and Dryer Fuel Use Bi
Fuel



### Table 3.12 Market Saturation by Fuel TVDC Among Housing 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 LPG 3.32 2.53

- 0.79 Other 0.46 0.36 0.05 0.04 None 0.08 0.01

- 0.07

3-14

### 3.11. INDUSTRY COST STRUCTURE The washing machine industry cost structure was developed from publicly available information from the Census of Manufactures, 20(Tables 3.13 and 3.14) and the Securities and Exchange Commission (SEC) 1 0-K reports filed by publicly owned manufacturers (summarized in Table 3.15). 

### Table 3.13 Emnlovment and Earnings

I Fr,du nwcn-kers COIJO) AU I1lIIoec 19OO Pa'riiD for II enipIoe5 I uiIUoi doJIar) 1997 12.9 14.8 480 1992 12.1 14.2 423.1 1987 14.1 16.7 465.8 1982 13.4 16.5 335.4 1977 15.9 19.4 262.4 Source: U.S. Census of Manufactures 1992, 1997



### Table 3.14 Information from the 1992 and 1997 Census of Manufactures Census tus of i%1 era1s as a Cost of 1a roll [or production Cost of Total Payroll ear pcrcuil

## or •ale ol vorker as a jwrceul ol value of productioui -i-adniiii. as a

shipEHCH S 511 fl111 ru tN ]er(LUI 01 valut o[ (pereeiit peEcent) 1upuirtiis

_________________________ ________________________________ 'pe1.e11'"

__________ 1997 55.9 10.6 12.9 1992 52 10.2 12.7 1987 54 12.3 15.3 1982 53 12.3 15.8

3-15

### Table 3.15 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. Items $2952.8 5.6% Net Income $1094.2 2.1% Capital Expenditures $2,368.0 4.4%


### The resulting cost structure is shown in Figure 3.8. 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.

Income from Ope rations 6.0% SG 18.

R&D 1.8% MAq.iaI 52.0% Overheac 11.7%

10.2% 

### Figure 3.8 Washing Machine Industry Cost Structure as a Percentage of Revenues

3-16

### A detailed financial analysis of the clothes washer industry to identify key financial inputs

like cost of capital, working capital, depreciation, capital expenditures, etc. is presented in Chapter 11 of this document. 3.12 INVENTORY LEVELS AND CAPACITY UTILIZATION RATES Table 3.16 shows the year-end inventory for the washer industry. Table 3.17 shows the production capacity utilization rate.

Table 3.16 End-of-year Inventory, 1977-1997
C.1r

_____________ Ed-of-eai lnve1CO!

(.'ii1l DJI1aT)

________________________________ I'ijd-o1-cr Iuven(Irv

### as a Percent oE \aliic ol &hulrHeIis

## (c) 1997 368.1 - 9.9 1996 346.9 8.2 1995 328 7.9 1994 341.6 7.4 1993 338.6 8.7 1992 264.4 7.9 1991 326 10.2 1990 393.8 12.2 1989 367.3 11.8 1988 350.6 11.2 1987 342.9 11.3 1986 325.5 10.6 1985 306.6 10.8 1984 323.2 11.9 1983 313.9 13.2 1982 286.4 13.5 1981 173.4 7.7 1980 170.1 7.9 1979 185.7 8.4 1978 157.7 8.1 1977 170.6 9.5 Source: U.S. Bureau of the Census, Census of Manufactures, 1992, 1997; Annual Survey of Manufacturers, 1993, 1994, 1995, 199621 

### Table 3.17 Full Production Capacity Utilization Rates* (%) 1997 1996 1995 1994 1993 1992 1991 1990 80 81 83 89 nla n'a nla 78 *Ratio of the actual value of production to the level of production at full production capability Source: U.S. Bureau of the Census, Survey of Plant Capacity, 1997, 1996, 199422

3-17

### 3.13 TECHNOLOGY ASSESSMENT

Theefficiency 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 Washers23and Draft Report on Design Options for Clothes Washers.24



### 3.14 ENERGY EFFICIENCY The test procedure on which this rulemaking is based will only take effect when a new standard is enacted. Therefore, publically available efficiency data are 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 J 1 and how they correlate to current test procedure ratings using EF, thirteen clothes washers were tested according to the new test procedure Appendix Ji. A summary of the washers tested is shown in Table 3.18. Test results for Appendix J and J 1 are shown in Table 3.19. Table 3.18 Product Characteristics of Clothes Washer Tested Clothes Washer Type Capacity (Cu. ft.) Warm Rinse A V-axis 2.42 yes B H-axis 2.90 yes C V-axis 2.78 yes D V-axis 3.26 no E V-axis 3.03 no F H-axis 2.54 yes G V-axis 3.00 yes H V-axis 3.02 no I V-axis 3.00 yes J V-axis 3.20 no K V-axis 3.20 yes L V-axis 3.00 no M V-axis 3.00

3-18

### Table 3.19 Summary of Appendix J and Ji Test Results J Results Ji Results Clothes Washer EF

(cu. ft./kWh) per cycle % Efficient

over NAECA Minimum (EF=1.18) MEF

(cu. ft./kWh) per cycle % Efficient

over AHAM Baseline (MEF=O.817) WCF (Ji) RMC (Ji)

________ A ____________

1.161 0.0 ____________ 0.819 0.2 _____________ 12.88

# J ____________

- B 2.339 49.6 1.594 48.7 8.00 49.6 C 1.166 0.0 0.876 6.7 12.02 54.6 D 1.326 11.0 1.148 28.8 11.16 54.8 E .149 0.0 1.050 22.2 

## 1 11.96 54.3 F 3.929 70.0 .451 43.7 [ 9.46 55.5 

# [ G 1.141 0.0 0.870 6.1 

## I 13.43 61.4 H NT

____________ 0.966 15.4 8.13 52.1

2.650 55.5 1.320 38.1 8.40 51,3

________ J* 1.440 18.1 1.080 24.4 11.30 63.9

- K 1.500 21.3 0,864 5.4 11.97 59.8 L NT

____________ 1.310 37.6 12.81 47.8 M NT

____________ 1.780 54.1 8.87 41.9 Source: Intertek Testing Services25'26'27'28 • J 1 results not re-tested using cloth correction factor NT Not Tested



### 3.15 DETERMINING MEF VALUE FOR VERTICAL AXIS COMPACT CLOTHES WASHERS Due to the small market share of washers under 1.6 Cu. ft., a separate analysis was not Conducted for this product class. Since after the clothes washer rulemaking a new test procedure and metric will become effective, a way to determine the future Modified Energy Factor (MEF) is needed. The current minimum efficiency standard level, expressed as EF, is based on DOE Test Procedure Appendix J; the new efficiency standards, expressed as MEF, will be based on DOE test procedure Appendix Ji. The new test procedure will become effective after the proposed efficiency standard is enacted. There is no simple conversion factor to convert from the Appendix J, EF to the Appendix J 1, MEF. Therefore, a spreadsheet with both the Appendix J and Appendix Jl test procedure versions has been constructed. Identical parameters can be inputted into the spreadsheet along with additional information needed for Appendix Ji. This allows calculated results for each test procedure to be compared. The main additional information required to determine an MEF is the remaining moisture content (RMC) of the test cloth after the final spin. When actual test data is unknown or if generic results are desired, inputs can be based on estimates or rules of thumb.

3-19

### Table 3.20 shows estimated MEF values for a 1.59Cu.ft. and a 1.96cu. ft. capacityclothes

washer. The Energy Factors used were based on the following: 

### • 0.90 current minimum EF for top-loading compact washers (compact means <1.6 cu.ft.) • 1.18 current minimum EF for standard capacity washers • 1.20 EF of an existing model washer with a 1.96 cu. ft. capacity • 1.32 EF of an existing model washer with a 1.96 cu. ft. capacity Table 3.20 Estimated MEF Values for 1.56 and 1.96 Cubic Foot Clothes Washers Basket Capacity EF (Energy Factor)* Max. Gallons Fill Estimated MEF Cu. Ft. No warm rinse With warm rinse 1.59 0.90 15.0 0.75 0.65

0.99 13.3 0.79 0.69 0.90 19.0 0.76 0.66 1.96 1.18 13.9 0.86 0.76

1.20 13.6 0.86 0.77 1.32 12.1 0.90 0.81 *Assuming a Alt. II or Alt. III cycle configuration 

### Additional detail on the spreadsheets, assumptions and results are presented in Appendix B.

3-20

### REFERENCES

1. Matimore, M. K., Manager, Market Data, AHAM,personal communication. Conversation with Peter Biermayer, LBNL. July 7, 1997. 2. 1997: Slow Growth Ahead.Appliance Magazine, 1997. 54(1): P. 38 3. Association of Home Appliance Manufacturers, 1997 Major Appliance Industry Fact Book. 1998: Chicago, IL. 4. The Share-of-Market Picture.Appliance Magazine, September, various years 5. U.S. Department of Commerce - Bureau of the Census, Census of Manufactures, various years. Washington, DC. 6. Statistical Survey and Report, data source: NBD INTELECT. Dealerscope,2000. 42(6): p. 19 (June)
7. Revised Draft Report on Consumer Research for Clothes Washers, DOE Comment 85, Docket No. EE-RM-94403, April 3, 1998. 

### 8. Statistical Survey and Report, data source: NBD INTELECT. Dealerscope, 1999. 4 1(6): p. 28 (June) 9. EPRI-The High-Efficiency Laundry Metering and Marketing Analysis Project ( THELMA), Distribution System Analysis. 

### 10. Statistical Survey and Report, data source: Dealerscope Consensus Survey. Dealerscope, 1998. 40(6): p. 23(June) 11. Statistical Survey and Report, data source: NBD INTELECT. Dealerscope, 1998. 40(6): p. 22 (June) 12. U.S. Department of Labor-Bureau of Labor Statistics, Producer Price Index. <http://stats.bls. gov/ppihome.htm> 13. U.S. Department of Labor - Bureau of Labor Statistics, Consumer Price Index-A 11 Urban Consumers, <ftp ://ftp.bls.gov/pub/special.requests/cpi/cpiai.tx> 14. Elrick and Lavidge, Washer Sales Percentage of Units Sold by Features, personal communication. One page memo. 1996. 15. EPRI, Marketing Strategies for Horizontal Axis Washers, December, 1996. Boston, MA. Prepared by Dove Associates, Inc. pp. 2-8. Report No. EPRI TR-107612.
3-21

### 16. Statistical Survey and Report, data source: Dealerscope Consensus Survey. Dealerscope,

1999. 41(6): p. 28(June) 17. Statistical Survey and Report, data source: Dealerscope Consensus Survey. Dealerscope, 2000. 42(6): p. 19 18. The Saturation Picture.Appliance Magazine, 1999. 56(9):p. 80 19. U.S. Department of Energy - Energy Information Administration, Residential Energy Consumption Survey: Household Energy Consumption and Expenditures 1993, October, 1995. Washington, DC. Report No. DOE/EIA-0321(93). 

### 20. U.S. Department of Commerce - Bureau of the Census, 1992 Census of Manufactures, 1995. Washington, DC. Report No. MC92-I-36B. 21. U.S. Department of Commerce .- Bureau of the Census, Annual Survey of Manufacturers, various years. Washington, DC. <http ://www.census.gov/econlwww/maO300 .html> 

### 22. U.S. Department of Commerce - Bureau of the Census, Survey of Plant Capacity, various years. Washington, DC. <http://www.census.gov/prodlwww/abs/plant.html> 23. Draft Report on the Preliminary EngineeringAnalysis for Clothes Washers, DOE Comment 55B, Docket No. EE RM94-403, November, 1996. 24. Draft Report on Design Options for Clothes Washers, DOE Comment 55C, Docket No. EERM-94-403, November15, 1996. 

### 25. Intertek Testing Services,ITS Report J98004015-OO1 (April 1998), 1998. Cortland, NY. 26. Intertek Testing Services,ITSReport J98029012-OO1 (October 1998), 1998. Cortland, NY. 27. Intertek Testing Services,ITS Report J20006542-OO1 (March 2000), 2000. Cortland, NY. 28. Jntertek Testing Services,ITS Report J20008389-001(May 2000), 2000. Cortland, NY.

3-22

### CHAPTER4. ENGINEERING ANALYSIS

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### TABLE OF CONTENTS 4.1 iNTRODUCTION 4-1 4.2 PRODUCT CLASSES 4-1 4.3 BASELiNE UNIT 4-1 4.4 COST-EFFICIENCY DATA COLLECTION 4-2 4.4.1 Characterizing Uncertainty 4-2 4.4.2 Variability in Costs between Manufacturers 4-3 4.4.3 Manufacturer Assumptions for Supplying Data 4-3 4.5 DATA AGGREGATION 4-5 LIST OF TABLES Table 4.1 Energy-use Data 4-5 Table 4.2 Aggregated Cost Data using "Most Likely" Values 4-8 Table 4.3 Industry Cost Distributions for5%, 10%, 15%20% and25%Improvements

Applied to an Existing V-axis Clothes Washer 4-9 Table 4.4 Industry Cost Distributions for35%, 40%,45%and50%Improvements

Applied to a H-axis Clothes Washer 4-10

LIST OF FIGURES Figure 4.1 Illustrative Input Distributions for Incremental Manufacturing Costs 4-3 Figure 4.2 Industry Cost Distribution Obtained by Sampling Each Manufacturer's Cost

Distributions 4-7 Figure 4.3 Cost-efficiency Graph for Clothes Washers 4-11

4-i

### CHAPTER4. ENGINEERING ANALYSIS

4.1 INTRODUCTION The Engineering Analysis determines the maximum technologically feasible energy efficiency level and develops cost-efficiency relationships to show the manufacturer costs of achieving increased efficiency. As described in this section, manufacturers were asked to provide clothes washer performance and manufacturing cost information to conduct 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 while providing a high degree of confidentiality of design. This approach consists of letting manufacturers supply cost and corresponding efficiency data using any design option or design option combinations they choose. The collected information describes manufacturer specific costs of achieving predetermined efficiency levels. This format has the additional benefit of being entirely compatible with data requirements for the manufacturer impact analysis.

4.2 PRODUCT CLASSES The Department DOE divides clothes washers into classes based on the size and features, e.g., suds saving. For the extant standards, DOE defines residential clothes washers in the following classes:• top loading, compact (less than 1.6 cubic feet capacity); • top loading, standard (1.6 cubic feet or greater capacity); • top loading, semi-automatic; • front loading; and • suds saving. For this rulemaking the Department has not analyzed compact, semi-automatic and suds saving product classes and therefore no engineering analysis was performed. All analyses was performed assuming a standard class, top loading, baseline efficiency washer. 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. or greater capacity) 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 in theFederaiRegisteron August 27, 1997, during the course of this rulemaking: Appendices "J" and "Ji" (62 FR45484)which have been 4-1

### incorporated into the Title 10 Code of Federal Regulations (CFR), Subpart B, Appendix J and J 1.1

The Engineering Analysis presented 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 Ji test procedures. Given these different testing methods and variables, there is no computational relationship between the EF and MEF descriptors. In order 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 Ji 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=1.18).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 option or design option combinations of their choice. The collected information describes manufacturer specific costs of achieving predetermined efficiency levels in addition to the energy-use characteristics. AHAIvI members provided costs and energy data for the following levels: Baseline (MEF=.817),5%(MEF=.860), 10% (MEF=.908), 15% (MEF=.961), 20% (MEF=1.021) and25%(MEF=1 .089) improvement applied in an existing V-axis clothes washer and improvements of 35% (MEF=1.257), 40% (MEF=1.362),45%(MEF1.485), and50%(MEF1.634) applied in an H-axis clothes washer. Manufacturers believe that 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 DOE requested that manufacturers provide a range of costs associated with achieving each efficiency level. Separate ranges were requested for variable and fixed costs. To characterize variable costs, each manufacturer was asked to provide its best point estimates ("most likely" values) for incremental material, labor and overhead costs to achieve 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. A similar procedure was adopted for fixed costs. Figure 4.1 shows
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