u S. GOVERNMENT

PROPERTY OF

~1if1lyTR-353-459

UC CATEGORIES: UC-62, 62a, 62b,

62c, 62d, 62e

AN INVESTIGATION OF LEARNING

AND EXPERIENCE CURVES

FRANK KRAW I EC

JOHN THORNTON

MICHAEL EDESESS

APRIL 1980

PREPARED UNDER TASK No. 5227

SolarEnergyResearch Institute

1536 Cole Boulevard

Golden. Colorado 80401

A Division of Midwest Research Institute

Prepared for the

U.S. Department of Energy

Contract No. EG·77·C·Ol 4042
Printed in the United States of America

Available from:

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfi eld, VA 22161

Price:

Microfiche $3.00

Printed Copy $9.00

NOTICE

This report was prepared as an account of work sponsored by the United States Government. Neither the United States nor the United States Department of Energy, nor any of

their employees, nor any of their contractors, subcontractors, or their employees, makes

any warranty, express or implied, or assumes any legal liability or responsibility for the

accuracy, completeness or usefulness of any information, apparatus, product or process

disclosed, or represents that its use would not infringe privately owned rights.
TR-459 5=~1 - i_II------------------------- ,~~

PREFACE

This final report was prepared for the Division of Energy Technology of the U.S.

Department of Energy (DOE). It is the final product of the Investigation of Learning and

Experience Curves, Solar Energy Research Institute (SERI) Task No. 5227, initiated in

November 1979. The literature review was published in SERI Report RR-52-173, "Solar

Cost Reduction through Technical Improvements: The Concepts of Learning and Experience." Arthur D. Little, Inc., under subcontract to SERI, prepared the final report on

"Development of Surrogate Experience Curves for Costs Associated with the Production

of Heliostats." J. H. Nourse of McDonnell Douglas Astronautics Company, under subcontract to SERI, prepared the final report on the "Prototype Heliostat Costing Scenario."

Primary SERI staff responsible for preparing this report were headed by Frank Krawiec,

project leader and principal investigator. Michael Bdesess and John Thornton wrote

Sec. 6.0. Theresa Flaim was the major contributor to Report RR-52-173. Technical

advice and support were provided by Dennis Costello, James Doane, Dennis Horgan,

David Posner, Dennis Schiffel, Melvin Simmons, and John Thornton. Management assistance was given by Dennis Costello and Melvin Simmons.

The authors wish to express appreciation for the manuscript review and helpful suggestions of Gerald Bennington (MITRE Corp.h John Bigger (EPRI); Kirk Drumheller

(Battelle); Steve Miller (Engineering and Public Policy, Carnegie Mellon University); and

John Thornton (Systems Development Branch, SERI).

~,.4~__:,

FrikKrawiec, Senior Economist

Industrial Applications and Policy Branch

Approved for:

SOLAR ENERGY RESEARCH INSTITUTE

Utilities and Industry Division

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S=~I TR-459 i~.1 -----------------------------=~~

SUMMARY

The applicability of the learning and experience curves to predict future costs of solar

energy technologies here has as its major test case the production economics of heliostats, The literature review indicated that the methods most often employed in past empirical studies to estimate and predict overall cost reduction in manufacturing new products are the "bottoms-up" engineering costs method, the engineering parametric

approach, and aggregate cost estimation techniques. There are practicallimitations to

these methods when applied to predict cost reductions separately.

The unit cost estimated by application of the engineering bottoms-up cost method does

not reflect an overall cost reduction due to labor learning, economies of scale, and technological change. Because of the dynamic nature of cost reduction in new products, generalized statements about specific cost data are meaningless. A thorough manufacturing

cost analysis for a new system or product becomes complex when alternative techniques

having their basis in the engineering bottoms-up cost approach are considered. In applying the engineering parametric approach, it is difficult to select a surrogate part relevant to a given product component in terms of configuration similarity, material correspondence, and fabrication congruency. Even if the surrogate parts or subassemblies are

truly analogous, there are limitations inherent in the use of statistical inference.

In addition, cost histories on prior programs are imperfect indicators of future costs of a

given design concept. The aggregate prediction of cost reductions in manufacturing new

products involves a number of conceptual and analytical issues associated with relationships between changes in the costs of a firm and changes in its output: the cost function. Price theory gives most of its attention to two cost functions-the short-run and

the long-run cost functions. Since price theory is based on the neoclassical theory of the

firm, the short- and long-run cost functions can provide insights into economic and technical issues that influence the behavior of costs and prices over time. However, since

neoclassical theory of the firm is a static, or at best a relatively static, depiction of behavior, it does not exhibit direct applicability of the short- and long-run cost functions to

future cost reductions. The short-run cost function contains the assumption that the

fixed costs, mainly those of the fixed plant and equipment, as well as the state of firm's

technology, are constant.

ThUS, the only short-run manufacturing cost reductions are those due to increasing labor

productivity and improvement in management techniques. It is practically impossible to

specify what the sources of short-run cost reduction will be for a new product. Therefore, in predicting cost reductions in manufacturing new products in the short-run, the

learning curve based on bottoms-up engineering costs has been used as an approximation

tool for future costs. The long-run cost function reflects unit cost changes due to the

technology change and economies of scale.

Changes in scale, technology embodied in the fixed capital, and the inherent form of the

product occur gradually over time. Consequently, the long-run cost function describes

unit cost changes that are relatively smaller than those of the short-term. Although of

great practical importance, it is not possible to estimate the relative proportions of total

decrease in unit cost that are caused by changes in long-run cost reduction sources, or to

characterize the manner in which unit-cost changes occur separately from changes in

specified long-run cost reduction sources. It is not feasible to anticipate with any accuracy how and why the costs of a specific product are going to change in the future. In

practice, the experience curve concept is applied as an aggregate tool to summarize cost

v
TR-459 S=~I'ltl' ------------------~------=-~=

changes resulting from a number of different long-run sources. This concept has been

applied because it allows approximation of future manufacturing costs.

The most widely used definition of the learning concept refers to increased productivity

of a direct single input (e.g., labor). In most industries, tooling changes, redesign of production methods, and improved management techniques are more important sources of

cost reduction than direct labor learning. Broader definitions are too ambiguous to be

used for cost estimation purposes. To reduce measurement problems, the learning concept could be applied to improvements in the production process which occur when the

capital stock is fixed. Given these qualifications, the term learning curve is considered

an aggregate tool that shows some empirical ability to summarize the sources of cost reductions in manufacturing new products within one particular process.

Although learning in the literal sense is restricted to improved productivity of direct

labor input, the learning curve is an analytical tool applied to generalize the combined

effects of both increased efficiency and management innovations. While the logic behind

the learning concept does not suggest a particular functional form, the exponential function is the most widely used functional form. This form is intuitively appealing because

it reflects the fact that in some industries, progress is rapid during the early stages of

production and continues at a decreasing rate.

The learning curve has been used as a basis for facilities and manpower scheduling,

pacing assembly operations, and cost estimation. The use of learning curves for cost estimation is of primary interest. At the center of empirical studies is the concept of separating labor, materials, and overhead costs, the factors that make cost reductions and

concomitant increases in quantity possible. An aggregated cost curve for direct application to different quantities of the end product can be developed by combining factors of

labor, materials, and overhead.

The literature review indicated a greater, wider application of the learning curve model

in labor-intensive production activities. There is a need for a greater application of the

learning curve model in capital-intensive industries. Labor learning as defined here may

be only a minor source of cost reduction in these types of production activities.

However, the literature does not provide empirical evidence to question the applicability

of the learning curve concept in capital-intensive industries.

Learning curves are subject to three important limitations.

• The learning curve is a method of estimating changes in labor productivity that

occur after production operations begin. If substantial efforts are devoted to

preproduction engineering and planning, and if labor productivity is higher than it

otherwise would have been, the learning curve will not reflect the progress that

has been made.

• Learning rates vary substantially among industries, products, and types of work.

A fundamental law of progress such as the 80% learning curve used in the aircraft industry does not exist. Analysts must choose an appropriate model and estimate the rate of progress for a particular product.

• The analyst must determine the range of production over which progress will

occur. Empirical data show that progress does not continue indefinitely. The

application of learning curves is based upon the assumption that progress in labor

hours will be achieved over the range of production specified.

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TR-459 ~.;I -----------------------------=:....-:.:::..::: S=~I ~~,

Unlike learning that refers to the productivity of a single input, the experience concept

has been used to describe changes in total cost as a function of cumulative production.

The experience concept was developed from the observation that per-unit production

costs declined in some industries as a direct, estimable proportion of cumulative production. Experience curves are similar to learning curves.

Unlike the concept of learning, the concept of experience is too ambiguous to be useful

for cost estimation. There is no logical reason to believe that costs will decline purely as

a function of cumulative production, and experience curves do not permit the analyst to

identify logical sources of cost reduction directly. Using an experience curve to estimate future costs of new products will yield only tautological results. All that one can

conclude from using an 80% experience curve to estimate costs is that if the cost of new

products declines 20% each time cumulative production doubles, then new products will

be 20% less expensive to produce each time cumulative production doubles. Clearly,

better methods than experience curves should be used to estimate the future costs of

new products. Empirical applications of learning and experience curves have been based

upon products for which historical production data are available.

Solar energy supply systems represent emerging technologies with components and processes ranging from mature to untested prototypes, fabrication, assembly, and installation. Many solar technologies require significant amounts of on-site construction. Most

learning and experience curves have been developed for manufactured products, rather

than those assembled or constructed on-site. Since the solar industry is young, there is

very little data on production economics. Learning or experience curves must be based

on surrogate products, then, that resemble the new products or systems in configuration,

mater-ials, and production methods. The selection of appropriate surrogate products with

available cost histories permits inferences regarding the probable cost of the new

product. It is assumed that because of similarities of design, materials, fabrication, and

assembly processes, the new product will undergo the same cost reduction pattern as the

surrogate product.

The procedures for developing learning and experience curves for a given solar energy

supply system or its component are:

• A conceptual breakdown of the new product or assembly into components, subassemblies, and processes;

• Identification of the surrogate part, SUbassembly, or assembly;

• Estimation of learning and experience curves for surrogate subassemblies; and

• Evaluation of learning and experience curves for surrogate products in order to

determine whether the same rate of progress can reasonably be expected to

occur in the new system or product.

Historical data collection for surrogates is the most difficult task, not because general

information does not exist, but because detailed historical data concerning labor, labor

costs, materials costs, and investment may not exist in some instances, may be extremely sensitive in others, or may be so interspersed with unrelated data as to be either

unusable or uncollectable. Most of the data involved, except for regulated industries, are

unavailable to the public, In some instances, published retail or wholesale data, broken

down into approximate components, can be used. Once the data are obtained, the technological factors that caused specific changes or trends have to be identified. An analyst

has to determine whether this development path would apply to the new system or

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TR-459 5=~11I.;1 - ~~ ------------------------=-=.::...-=.=

product. This step utilizes past cost history by rationally projecting how it may be

extended for the new product, eliminating those factors that cannot occur, or reducing

the impact of those unlikely to occur. In addition, production volume effects have to be

assessed. Surrogate product volumes probably will differ widely from one another and

from projected volumes for the new product. These volume variations can have a

significant cost impact and have to be standardized to produce realistic expectations.

Based on all of the data gathered and developed, learning and aggregated cost curves can

be estimated. This approach to development of learning and composite curves predicts

overall cost reduction in manufacturing the MDAC and GE prototype heliostat designs, to

project future costs for these designs using surrogate cost history and the learning curve

conceptual and analytical framework.

The heliostat field represents the largest fraction of the cost of a solar central receiver

system. The U.S. Department of Energy (DOE) has placed major emphasis on reducing

heliostat cost through a variety of programs. The goal is to develop reliable costeffective heliostat designs amenable to mass production and meet strict performance

stand:yds. The CUITent cost goal, established in 1975, is to produce heliostats of $72/m2

($7/ft ) reflectivity. This goal is under review and will probably change to reflect newer

cost estimates resulting from recent advances in heliostat design, as well as any innovations by large manufacturing companies interested in heliostats, such as General Motors

or Ford Motor Company. To foster competition, DOE awarded contracts to five separate

contractors to build prototypes. All of these prototypes will be of the glass-mirror

design, because the requisite plastic technology is not yet sufficiently advanced to

promote confidence in the plastic enclosure design. These low-cost prototypes are

second-generation heliostats. Meanwhile, research and development of suitable plastic

rnaterials for use in the enclosed heliostat design will be continued.

While second-generation prototype heliostats are being built, work is proceeding toward a

third-generation design. Twelve special studies are being performed on heliostat component design and maintenance. A new heliostat components solicitation was released in

1978. Resulting design improvements are planned to be incorporated into a thirdgeneration heliostat design in the early part of FY8l. Second-generation glass-steel and

inflated-bubble heliostat design concepts, developed by MDAC and GE, respectively, are

described in some detail in this report.

Critical areas for cost reduction were identified for each generic design. For the "hard"

(glass-steel) heliostat design, the highest cost elements are the drive unit, reflective

unit, foundation, and site development. The elemental cost breakdown for the "soft"

(plastic-bubble enclosed) heliostat indicated the critical areas for cost reductions to be

the foundation and site preparation and structural support and protection units.

The fundamental requirement for estimating and predicting cost reduction is establishment of a detailed costing scenario. In this report, the costing scenarios were determined through description of the basic characteristics of market potential, the production and installation volume, and the general configuration of the manufacturing/

assembly facility module. Points in cumulative production volume where the effective,

steady-state production process may be established, along with the associated first-unit

cost for the steady-state scenario, are provided. Three main costing scenarios were presented: 25,000, 250,000, and 1,000,000 heliostats/yr production rates.

A rate of 25,000 units/yr was selected as the baseline production scenario for the IVIDAC

and GE heliostat designs. This rate represents significant high-volume production situations, yet may be translated easily into higher or lower production rate scenarios. As a

viii
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result, this scenario supports the development of a viable business approach and a strategy for capturing an important share of eventual markets.

Additionally, the design/production, installation, and ~erating plans derived for the

25,000 units/yr scenario support the DOE goal of $72/m reflectivity. The average unit

cost is projected to be about 20% lower than that projected for the initial pilot production. The average unit cost for a 250,000/yr production rate shows a significant reduction compared with its estimated value at the 25,000 heliostats/yr rate. In contrast, the

average unit cost reduction for the 1,000,000/yr production rate is not as significant as

for the 250,000 units/yr scenario.

Changing market growth and location, competition, basic design, available production

technology and resources, and govemment and business policy affect the point in cumulative production volume where the effective, steady-state production process may be established along with the associated first-unit cost for the steady-state scenario. The

manufacturing and installation of the MDAC prototype heliostat design was used as an

example.

The MDAC prototype heliostat study implies that the 125,000th unit is the cumulative

volume where an effective, steady-etateproduction process is established for the baseline scenario. The rationale for this assumption is that certain electronic components

will not be available until 1985, and a market will not develop which will support a rate

of 25,000 units/yr until 100,000 units have been produced. An additional plant start-up of

1-2 years or 25,000 units will stabilize production in the new plant. This is a conservative projection. It is possible that market or fiscal incentives or production alternatives

could allow the facility to be installed well before 100,000 units are reached. Also, many

elements (especially buy items procured from the industrial base) may essentially reach a

steady state well before the first facility goes into operation. On the other hand, designs

and production methods never fully stabilize and breakthroughs will continue long after

the first 100,000 units are produced.

The production scenario progression was viewed in three parts: (1) test hardware to pilot

plant; (2) pilot production to demonstration and early commercialization; and (3) continuation to Nth unit of production.

The point of steady-state production is defined as that point in cumulative production

volume where the only design changes permitted are those that may modify a part for

more effective production or performance, are for the primary purpose of cost reduction,

and win maintain a stable interface with other part concepts and designs.

The prototype heliostat baseline production scenario is a steady-state scenario. As conceived, this scenario provides for the production and installation of 25,000 heliostats/yr,

but by simply operating on a one-shift basis, reducing line speed somewhat and cutting

the number of field crews in half, the identical facilities and equipment may be operated

with a minimal and acceptable economic penalty at a rate of 10,000 heliostats/yr or

less. It was concluded that the prototype baseline scenario, with minor modifications,

also may be used to describe a steady-state production situation that could occur at a

fairly early stage of cumulative production. A review of demand growth projected for

the modified scenario indicated the following production requirements:

• By the end of 1982, all suppliers will have produced 10,000 heliostats.

• During 1983 and 1984, an additional 10,000 heliostats will be supplied each year.

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S=~I - '1.;1 -------------------- TR-459

~:::.:t;>lil ,~~

• In 1985, all suppliers must produce an additional 20-25,000 units in order to meet

demand projections.

• During 1986, 1987, and 1988, demand will approach 100,000, 150,000 and 300,000

heliostats, respectively.

Accepting this market projection, an aggresive supplier designing 40-50% of the market

could plan its new facility initial operating capability in 1985, in time to produce its

lS,OOO-and-first heliostat in accordance with the modified baseline scenario. Assuming

approximately a 12-month startup during which 5,000 heliostats are produced, the actual

steady-state will be achieved when the supplier has produced a cumulative total of

20,000 heliostats.

It was argued that if cost reduction efforts in preproduction, pilot, and demonstration

programs were successful, by 1983 a manufacturer should be able to demonstrate that a

move into baseline facilities will permit heliostat price quotes that are quite competitive

with alternative energy costs. As a result, an effective, steady-state production status

for an aggressive and dominant supplier at the 20,OOOth unit is not an unreasonable projection under the circumstances.

The steady-state unit cost for the modified prototype heliostat scenario was estimated.

The two methods applied were a resource impact approach and cost reduction curves.

The development of learning and composite curves for the MDAC and GE prototype

heliostat designs proceeded with (1) the selection of those subassemblies, assemblies, and

processes that have significant potential cost reductions; (2) selection of surrogate components or processes that can provide a learning slope for the component of the heliostat

designs; (3) collection evaluation of data for the selected surrogates; (4) derivation of

learning and experience curves for surrogates; and (5) application to heliostat assembly.

In selecting heliostat components that have significant potential cost reduction, emphasis

was put on the reflective and drive units of both MDAC and GE heliostat designs.

Surrogates for cost analysis were chosen for selected components (e.g., reflectors, elevation actuator, drive controls, etc.) on the basis of the following criteria:

• The relative importance of the component to the contractor's design;

• The relative contribution of the component to the total cost of the design; and

• The likelihood of availability of a meaningful surrogate having a body of historical data.

The choice of surrogates was based largely on similarity of the manufacturing operations

between the surrogate and the heliostat components and the availability of reliable cost

data. All automotive-related surrogates had to be discarded because cost information

could not be obtained. Moreover, specific components investigated for this study have

varying degrees of validity as surrogates of their heliostat component counterparts, and

varying levels of cost data quality.

For the overall product, no reliable surrogate data were available. The radio telescope

antenna assembly represents the penultimate surrogate for a heliostat. Unfortunately,

none were ever mass-produced.

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5=~1 TR-459 'la~1 ~~, -----------------------------~~~

Surrogate products were identified and selected under the assumption that historical

(relative) cost patterns exhibited by these products could be anticipated for the corresponding heliostat components as well. Also, in order to be applicable to the heliostat

assembly, the relative cost behavior for each surrogate must be independent of ancillary

conditions such as:

• years of production;

• annual production rates, and changes in ra tes;

• absolute unit costs of surrogate;

• economic factors;

• production volume, lot sizes, and sequence number used; and

• extraneous factors that might distort the constant rate of reduction in unit costs.

The data acquisition task proved formidable, and cost data were of limited quality. For

the surrogate approach to be credible, it is imperative that accurate and reliable cost

data be used, and that the manufacturing history of each component be well understood..

After all relevant data had been accumulated, unit costs and corresponding cumulative

production quantities were calculated. In most cases, the average cost of producing the

Nth unit was considered; however, a few cumulative cost curves were also generated,

primarily for comparative purposes.. A least-squares linear fit (on log-log scale) was performed using the BMDP Biomedical Computer Program subroutine BMDPlR, Multiple

Linear Regression.

The effect of variability in slope estimates, as well as overall precision in the estimation

techniques, was demonstrated. There were limitations in applying historical learning

trends to related but different components. The following concerns are noted:

• Learning curve slope estimates are highly dependent on the particular surrogate

selected; different surrogates intended to characterize the same heliostat component can yield vastly different estimates of learning effects.

• Identical surrogates (such as plywood) lead to widely varying estimates, depending on the data source, age of industry, and extraneous variables, such as geographical location.

• There is evidence that learning dampens out for mature industries, and therefore

may not represent expected patterns for similar components with different system configurations and/or requirements.

• Estimated slopes appear to depend on units of measure, even in situations that

should be invariant.

• Many of the surrogates used in this study indicated little or no measurable

learning. This condition manifested itself in two ways: 0) the data did not

appear to be linearly related as measured by standard goodness-of-fit test statistics, and/or (2) unit costs were constant, and apparently unrelated to quantity

produced over the range of production units considered.

• Estimates for heliostat components are highly subjective, even if the surrogate

cost/quantity data were reliable in all respects.

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• Historical raw cost data frequently are contaminated with hidden effects that

only extraordinary and detailed investigation studies will uncover.

In view of unexpected and unavoidable difficulties encountered in acquiring reliable cost

data for many surrogate products, it is recommended that the derived learning percentages be accepted with caution, and that absolute values and ranges be considered illustrative rather than definitive.

The procedures for using learning and cost curves to predict cost reduction in manufacturing solar technologies provided that learning and cost curves were to have been derived and aggregated over labor, material, and overhead cost elements for each heliostat

part. Since the data collected were not amenable to treatment at this level of detail,

the conceptual framework was illustrated with total cost data only.

Composite cost curves were constructed for each heliostat design by aggregating at similar production quantity levels over the 11 individual component parts. The developed aggregate cost curves for the GE and MDAC heliostat designs indicate that the conceptual

estimation technique examined in this study would almost certainly yield a nonlinear

cost/quality for the overall assembly since slopes are not additive over components in a

log-log scale. The MDAC heliostat assembly composite unit cost curve starts out at

approximately 94%, changes to 96%, and finally reaches 97%. For the same production

scenario, the GE heliostat assembly composite unit cost curve starts out at approximately 89%, changes to 92% and 94%, and finally reaches 99%.

The analytical method described in this report was intended to yield a predictive cost/

quantity relationship that can be applied to the heliostat production program. In this

context it should be recognized at the outset that the estimation of surrogate learning

and experience curves to predict cost reductions for any new product or system is a difficult and complex process, particularly for a system designed to perform a unique function (such as the Solar-Electric Power Tower). The literature presents underlying theories, conjectures, and plausible explanations for many past programs in which estimated

production costs deviated dramatically from what was actually experienced.

Although it is recognized that estimating production costs is an important and necessary

element in program planning and evaluation, it is equally important to understand the inherent limitations and constraints of the estimation process itself. The value of the procedure illustrated in this report is essentially dependent upon the following key considerations:

• The validity of the relationship between surrogates and the actual heliostat components;

• The quality of unit cost data available for surrogate components;

• The applicability of relative unit cost reductions observed for surrogates to the

heliostat components' counterparts;

• The ability to express, accurately and precisely, unit cost-quantity relationships

in a mathematical form; and

• The understanding of manufacturing methods and technology related to the production of surrogates and heliostat components.

The surrogate concept of cost estimation used in this study combines qualitative steps

that are highly subjective with quantitative techniques that require thorough knowledge

xii
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'I.~I '~~I --------------------------------

and understanding to justify their use. As such, the results, interpretations, and inferences must be qualified by an understanding of the process through which they were

developed.

The analysis conducted for this study indicates a learning effect that varies with production unit number of the heliostat assembly. Moreover, the learning effect is different for

each of the two heliostat designs.

The method of surrogate learning curves had limitations in both the data acquisition and

data analysis phases of activity. Improvements in the validity of cost data and in the

tools used for this type of study are necessary to enhance the reliability of unit cost predictions resulting from this technique.

The task of analyzing solar energy systems' costs is made complex by considering alternative cost reduction techniques which have their basis in bottom-to-top costing. An

approach that combines a neoclassical production function with a learning-by-doing hypothesis is needed to yield a cost relation compatible with both the learning curve frequently encountered in empirical studies and the traditional cost function of economic

theory. Despite the popularity of each concept, there has been a remarkable lack of

success in integrating the two approaches in empirical studies. Previous attempts to integrate learning with production theory have been more conceptual than analytical. An

approach constructed from a neoclassical production function and a learning-by-doing hypothesis will generate better or more accurate information to address such issues as (1)

establishment of research and development programs; (2) design and evaluation of

government programs to accelerate the commercialization of solar energy; and (3) more

accurate assessments of the potential market for solar energy.

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TABLE OF CONTENTS

1.0 Introduction. . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . • . .. .. 1

2.0 Methods for Predicting Cost Reductions in the Manufacture of

New Systems 3

2.1 Overview. . . .. . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . .. .. . . . . . . . . . . . . . . 3

2.2 Engineering Bottoms-Up Cost Analysis. . • • • • • . • • • • • . • . . . . • • • . • • • • • • 3

2.3 Engineering Parametric Approach ..••••••••••••••••••••••••••••••• 5

2.4 Aggregate Prediction of Cost Reduction. • . • . • . • . . • • . • • • . • • • • • • • . • • • 5

3.0 Conceptual Foundation of Learning.. • • • • • • • • • • • • • . • • . . • • . . • • • . • • • • • • • • • 7

3.1 Background, .. • • • • • • .. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • .. • • • • • • • • • • • 7

3.2 Deft nition of Learning •• . • . • . . • • . . . • • • • • . • • • . • • • • . . • . . . . . • • • • • • • . 7

3.3 The Learning Curve •.•...•.••...•••.•••••••.•••••..•.••.•..••.•• 8

3.4 Learning Curve Estimation ..•• • . • • • • • • . • • • • . • . . . • . . . • • • • • • • • . • . • • 12

3.5 Empirical Applications of Learning Curves. . . . . . . . • . . . . . . • • • • • • • • • . • 15

3.5.1 Labor • • . . . . . . . . . . . . . . • . • . .. . . . . . .. . . . . . . • . . . • . . . . . • . . . . . . . 15

3.5.2 Materials... .. ..... .. .. .. ... .. . .. .. .... .. .... .. ... ... 16

3.5.3 Overhead. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.5.4 Obtaining Total Production Costs. • • . • • . • • • • • • • • . • • ••• • • . • . . 16

3.6 Limitations of Learning Curves ••.•••••....•...••••.••••••.•.•••.• 16

4.0 The Concept of Experience. . . . • • • • • • • . • • • • . . . . . • • • • • • • • . • . • . • • • . • . • • • • 19

4.1 Experience Curves .•.•...•.•••••.•••••.....•.••••••••••.•••••••. 19

4.2 Applications of Experience Curves. • • . • . • . . . . • • . . • • . • • • • • • • . • . • • • . • 20

4.3 Experience Curves and Cost Estimation ••.•. . • . . • • • • • • • • • • • . • . • . • • • 20

5.0 Development of Learning and Experience Curves for Solar Energy

Technologies 25

5.1 Background. . • . • . • . . . . . • . . . . . . . • . . . . . . . . . . . . • • • • • • • . • . • . • . . . . . . . 25

5.2 Data Limitations... . .. .. •. .•.. .•• . . . .• .• . •. •••••• ••. •• ••••• •• ••• 25

5.3 Estimating Learning and Experience Curves for Surrogate

Products or Systems •...•...•.•••...•••.•.••••••••••••••-. • • • • • • . • 25

5.4 Estimating Experience Curves for Surrogate Products

or Syste ms .•.•.•.•.........•.•.•••••.•••.•.•....•.•.•.•.•••.•.• 27

6.0 Technical Aspects of Heliostat Designs. . • . • • • • . . . • . . . . • . • . • • • • • • • . • . . •.• 29

6.1 Basic Elements of a Solar Central Receiver System.. . • • • • • • • • • • • • • • • 29

6.2 The DOE Heliostat Development Program •••.•...•.•••••.•••.•••.•. 33

6.3 Descriptions of Generic Heliostat Designs... • • • . • • •• •• . ••• • • • • • • • • • 34

6.3.1 The McDonnell Douglas Prototype Heliostat Design.... .• •• .•• 34

6.3.2 The General Electric Prototype Heliastat Design.. • • • • • • • • • • • 43

6.4 Key Cost F actors in Heliostat Design ••••..•.••••••••••..•.....•... 48

xv
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TABLE OF CONTENTS (concluded)

7.0 Prototype Heliostat Cost Scenarios. .. . .•. •...••. .•••• .••. ..••. .•.•.•..• 51

7.1 Introduc tion • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • 51

7.2 Factors Influencing Cost Scenarios................................ 51

7.2.1 Market Potentials.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

7.2.2 Production Volume Progression... .••.•.•..•.. .•.•. .•.•... .. 52

7.2.3 Design and Development Status ...••..•••.•.•..•..•.....•.. 54

7.2.4 Plant and Equipm ent Impact. . . . . • . . • • . . • • . . . . . . . . . • . • . . • • . 54

7.2.5 Management Alternatives and Concern..... •• ••. . .. .. .• .• . .. 56

7.3 Descriptions of the Baseline Production Scenario ....•.. . • . . . . . . . . . • • 56

7.3.1 The McDonnell Douglas Prototype Heliostat Design

Production Scenario 56

7.3.2 The General Electric Prototype Heliostat Design

Production Scenario .......•.............................. 80

7.4 Steady-State Production Volume and Cost Variation... . .•. .. . . . . . .. .. 88

7.4.1 The MDAC Prototype Heliostat Design. . . • . . . . . . . . . . . . . • . . . . 88

7.4.2 The Point of Steady-State Production. • . • . . • . • . . . . . . . . . . . . . . 97

8.0 Development of Learning and Composite Curves for Costs Associated

with Heliostat Production...... . . . .. . .. . .. .. . . . .. .. .. . .. .. .. .. . . . . . . .. 105

8.1 Selection of Surrogates 105

8.2 Derivation of Learning and Experience Curves for Surrogates 111

8.2.1 Data Acquisition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. III

8.2.2 Surrogate Learning Curve Estimation. . . . . • . . . . . . . . . . . . . . . .. 113

8.3 Application to Heliostat Assembly ..........•.•..•.••.•.•..•....•.. 115

8.3.1 Unit Cost Estimates for Relevant Components.... . . ..•.• . •.. 115

8.3.2 Development of Heliostat Component Cost Curves. . • . . . . . . . •. 115

8.3.3 Aggregation of Costs... ..... ... . . .. . .. . . .. .... .. . . .. .. . .. 115

9.0 References 121

Appendix A. Summary of Data Acquired for Selected Surrogate Products

(Tables) . . . . . . . • . • . . . . . . . . . . • . . . . . . . . . . . • . . • • . . . . . • . . . . . . . . •. 125

Appendix B. Estimations of the Surrogate Learning Curve (Figures) ..•......... 143

Appendix C. Cost Curves for the MDAC and GE Heliostat Designs

(Figures) .. . . . . . . . . . . . . . . . . . . • . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 151

xvi
-------------------~-------:;;..;;;.;:......;;~ TR-459 -~~~,

LIST OF FIGURES

3-1 Hypothetical Unit Learning Curve on Arithmetic Coordinates...... •••.••.. 10

3-2 Hypothetical Unit Learning Curve on Logarithmic Coordinates.. . . • • • • • . . • • 10

3-3 Hypothetical Learning Curve on Logarithmic Coordinates. . • • • • • . • • • • • • • . • • 11

6-1 Schema fer a Typical Central Receiver System.. . . . • . • • . • • • • • • • • • • • • • • • • • 30

6-2 Heliostat Field and Central Power Tower. • . . • . . . • . . • . . . . • . . . . . • . . . . . .. .. • • 31

6-3 Cost Breakdown fer a Typical Central Receiver Plant. • • • • . • . .. . • • • • • • • • • . • 32

6-4 Typical Two-Axis Tracking Heliostat. • .. . • • • . • • • • .. • • • . .. • . • • . • . . • .. • .. .. .. • .. •• 32

6-5 Generic Heliostat Development 35

6-6 McDonnell Douglas Second-Generation Heliostat 36

6-7 Laminated Mirror Module Assembly. • .. . • • • . • . • • • • • • • • . • • • • • . • . • • • .. • • • • • • 40

6-8 Reflector Support Structure Assembly •...•.•.•..•.•.••••••••••••••••••• 41

6-9 General Electric Enclosed Heliostat 44

6-10 General Electric Heliostat Design 45

6-11 Stretch Frame Reflector Subassembly... • .• .•• .•.•. .•.. . .•••• •. •. •• •• •• • 46

6-12 Support and Drive Assembly •.•.•••••••••..•.•••.•.•••••••••••..•.••••• 47

7-1 Cumulative Heliostat Market Scenario. • • • • • • • • . • • • • • . • . • . • . • . • • • • • • • • • .. 53

7-2 Present Value of Capital Equipment 55

7-3 Block Flow Plant Layout... . . . .. . .. . .. . .. . .. • .. • .. . • • • .. . . • • • • • . • .. • .. • • • • • • . • . • . 65

7-4 Reflector Panel Assembly Manufacturing Flow... .• 66

7-5 Drive Assembly Manufacturing Flow. • • . • . • • • .. • • • • • • .. .. • .. • • • • . .. • • .. • .. .. . • . • 68

7-6 Final Assembly Joining Area Drive Unit to PedestaL..... . .••.. 71

7-7 Heliostat Controller Manufacturing Flow Chart 72

7-8 Data Distribution Interface (DDI) Manufacturing Flow Chart. • • . • . .. . .. • • .. • . . 72

7-9 Heliostat Control Harness Manufacturing Flow Chart 73

7-10 Installation Task Sequence. • • • . . . . . .. . . . .. • . . . • • . .. . • . • • .. • .. .. • .. .. • • • • . • .. • . .. • 75

xvii
S=~I TR-459 :~I' ----------------------.....:..::.~=

LIST OF FIGURES (concluded)

7-11 Drive Unit Installation Machine ••••••••••••••••••••••.•••••••.•.••••.•• 77

7-12 Reflector Installation Equipment..... •••••••••• • ••• ••••••••• ••• ••• ••.• • 77

7-13 Cost Elements of MDAC Heliostat (25,000 Units/Yr) •••••• ••••• •• ••••• •••• 82

7-14 Cost Elements of MDAC Heliostat (250,000 Units/Yr) ••• • • •. • • • • • • • • •• • • • • 83

7-15 Cost Elements of MDAC Heliostat 0,000,000 Units/Yr) ••••••••••••••••••• 84

7-16 Cost Elements of GE Heliostat (25,000 Units/Yr) ••••••••••••••••••••••••• 90

7-17 Cost Elements of GE Heliostat (250,000 Units/Yr) •••••••••••••••••.•••.•• 91

7-18 Cost Elements of GE Heliostat 0,000,000 Units/Yr)....................... 92

7-19 Manufacturing Facility for 25,000 Heliostats/Yr) .•••••••••••••.•••••••••• 93

7-20 Manufacturing Facility for 250,000 Heliostats/Yr) ••••••••••.••.••••••.•.• 94

7-21 Typical Heliostat Installation Sequence. • . • • • • . • • • • . • • • • • . • . • • • . • • • • . • • . • 95

7-22 Unit Curve Plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 102

8-1 Heliostat Assembly Unit Cost Curve (GE) Design •••••••••••.•••••••.•.•.• 118

8-2 Heliostat Assembly Unit Cost Curve (MD) Design... •••• ••••• •• • •••••• •••• 119

xviii
TR-459 S=~I 'IWII--------------------------- -~ ~~

LIST OF TABLES

4-1 Experience Curves Identified by the Boston Consulting Group 22

6-1 Technical Descriptions-Collector Subsystem ••.•.......•.••••••••••••••• 37

6-2 Drive Unit Requirem ents • • • • . • . • . • . • • • . • . • . • • • . . . . . • . • . • . • • • • • • • • • • • • • 42

6-3 General Electric Heliostat Weight Breakdown. . . • • • • • • • • • • • • • • • . • • • . • . • • • 48

6-4 Design Parameters Affecting Heliostat Elemental Cost.. . ..•.•.•.•••••• ••• 49

7-1 Prototype Heliostat Hardware Tree. • • • . . • • . • . • . • • . . . • . . • . • • . • • • . • • • • • • • 59

7-2 Installation Tasks and Crew Size ..••••..••••••.•••.••••••...•.••...•.•• 76

7-3 MDAC Prototype Heliostat Cost Breakdown •.•........•...•.•.•.•••••••• 81

7-4 GE Heliostat Cost Comparison. • • . • • • . • . . • . • • • • . • . • • . . • • • • • • • • • • • • • . • • • 87

7-5 Projected Costs for GE Heliostat Design ..••••••••••..••••••••.••••••••• 88

7-6 10,000 Heliostats/Yr Steady-State Preliminary Cost Breakdown ••...••••••. 89

7-7 Steady-State Costs.. . . . . . . . • . • . • . . . • . • . . . • . . . • . . • • • • • • • . • • • • • • • • • • • •• 100

8-1 Heliostat Design Components. • • . • • • . • • . • . . . . • . . . • . • . • • • • • • • • • • . • • • . • .• 106

8-2 Choice of Surrogates for Heliostat Components ...•..•••.•.•.••••.••••••• 107

8-3 Surrogates Used in the Study. . • . . . . . . • • • . . • • • • • • • . • • . • • • • • • • • • • . . • . • . .. 108

8-4 Cost Estimate-25,000th Unit, GE Design... •• •• •• . .•••• •••• •••• •• ••.• .•. 116

8-5 Cost Estimate-25,000th Unit, MDAC Design ••.•••.•.•••..••••••....•..• 117

8-6 Estimated Costs and Slopes fet" the MDAC Heliostat Design. • . • . • • • • • • . • •.. 120

8-7 Estimated Costs and Slopes for the GE Heliostat Design. • • • • • • • • • • • . • • • ••• 120

xix
~~ TR-459 S=~I 11.'1,

~~. ------------------------------__

SECTION 1.0

INTRODUCTION

A crucial issue in the commercial development of solar energy technologies concerns the

cost of solar technologies and whether this cost can be reduced. Many solar technologies

are now too expensive to be economically feasible. Some of these technologies may

achieve substantial cost reductions with improved product design and production techniques.

An assessment of future solar energy costs* is necessary for several reasons. First, an

assessment of solar technologies with the greatest potential for cost reductions would

help establish research and development priorities. Second, such an assessment is useful

for designing and evaluating government programs to accelerate the commercialization

of solar energy. Finally, identifying possible reductions in costs leads to more accurate

assessments of the potential market for solar energy.

Most of the U.S. Department of Energy (DOE) programs for estimating and predicting

cost reductions in solar technology manufacturing have been through: (1) the low-cost

Solar Array Project at the Jet Propulsion Laboratory (JPL) and (2) the Repowering Strategy Analysis Supply Task of the Solar Energy Research Institute (SERI).

JPL has been investigating possible sources of cost reductions in all aspects of the production and assembly of flat-plate photovoltaic cells, including extensive SUbcontracting

for detailed design and engineering improvements in each step of the array production

process. This has led to refining a process to achieve the $2/Wp goal. Work remains to

be done in support of the $O.SO/Wp goal.

SERI has been analyzing the production economics of heliostats and investigating the

effects of changes in production quantity and processes on heliostat cost. Some changes

in design and manufacturing processes may be necessary to attain low-cost, high quality

production. Also, SERI has been identifying significant cost components in heliostats and

applying these to an investigation of new heliostat concepts to determine the value of

the new designs. A thorough bottoms-up engineering cost analysis has been performed.

The methods most often employed in past empirical studies to estimate and predict overall cost reductions in manufacturing new products are: 0) engineering bottoms-up cost

analysis; (2) engineering parametric approach; and (3) aggregate cost estimation techniques (short- and long-term cost functions, learning and experience curves based on

bottoms-up engineering costs analysis, ete.). Cost estimates for solar energy technologies typically are made assuming a fixed production process characterized by standard

capacity factors, overhead, and labor costs.

The concepts of learning and experience have been developed in the business management and industrial economics literature as ways to explain cost reductions observed in

some industries; learning and experience curves were developed in order to estimate the

magnitude of reduction in costs [9,10,12,13]. In the broadest sense, the concepts of

learning and experience are based on the premise that, as people gain experience with

methods of producing a particular product, improvements will be identified and implemented. Reductions in per-unit costs have been observed with such regularity in some

*Cost refers strictly to market cost in constant dollars, unless otherwise noted.

I
TR-459 .,;;: It.~~;, ~~ ----------------------------_......=...:::.::-.=.::..::

industries that a number of authors believe that costs can be expected to decrease at

some constant, estimable proportion of cumulative production [26,27]. Because the solar

industry is relatively new, it could be argued that costs will decline as firms gain experience with different methods of producing solar energy equipment [9]. Consequently, the

objective of this report is to assess the applicability of learning and experience curves

for predicting the future costs of solar technologies. The major test case here is the production economics of heliostats.

A brief discussion of alternative methods for predicting cost reductions in the manufacture of new systems resulting from learning by doing, economies of scale, and technical

change is summarized in Sec. 2.0. Learning concepts, including the factors affecting initial production costs, procedures for estimating learning curves and their empirical applications, and limitations of learning curves are presented in Sec. 3.0. Section 4.0

describes the concept of experience and its applicability to cost estimation. Procedures

for developing learning and experience curves for solar energy technologies are outlined

in Sec. 5.0. Section 6.0 summarizes the technical description of the McDonnell Douglas

and General Electric heliostat designs and describes the basic elements of a solar central

receiver system, the DOE heliostat development program, generic heliostat designs, and

key cost factors in heliostat designs. The factors influencing a costing scenario, a description of the baseline production scenario, and steady-state production volume and

cost variations are provided in Sec. 7.0. Section 8.0 describes the selection of surrogates, derivation of learning and experience curves for surrogates, and application to

heliostat assembly.

Appendix A contains a summary of the data acquired for the selected surrogate products. An example of surrogate learning curve estimations is presented in Appendix B.

Figures illustrating the cost curves for the MDAC and GE heliostat designs are provided

in Appendix C.

2
TR-459

-~ ;.'1~~, --------------------------=-=~'

SECTION 2.0

METHODS FOR PREDICTING COST REDUCTIONS

IN THE MANUFACTURE OF NEW SYSTEMS

2.1 OVERVIEW

The rapid pace of technology development since the end of World War IT and improved

analytical techniques are two of the reasons why analysts pay close attention to product

costs.

Alternative methods for predicting cost reductions in the manufacture of new products

include consideration of learning by doing; economies of scale; and technical change.

The tools most often employed in past empirical studies to estimate and predict overall

cost reductions in manufacturing are:

• engineering bottoms-up cost analysis;

• engineering parametric approach; and

• aggregate cost estimation techniques (short- and long-term cost functions,

learning and experience curves based on bottoms-up engineering costs).

These methods, and the conceptual and analytical framework of learning and experience

curves, are discussed in the following sections.

2.2 ENGINEERING BOTTOMS-UP COST ANALYSIS

The most widely used method to estimate cost in manufacturing a new product or system

is the engineering bottoms-up costs approach. This method has been successfully used to

estimate manufacturing costs for various solar hardware systems [1-4]. Its analytical

framework is provided in detail in Ref. 1 and 2.

The hardware system design and performance specifications of the product, the production rates and schedules, and the location sites for facilities must be determined. First,

equipment designs are evaluated to determine any potential manufacturing problems and

to investigate the possibility of using less costly materials and processes. Make-versusbuy studies are conducted to determine availability and relative costs of outside sources

for components or subassemblies and the level of in-plant integration is determined.

Proper equipment, tooling fixtures and auxiliaries are specified and the necessary facilities are laid out, and environmental impact studies and marketing and distribution analyses are completed.

Once this initial capital equipment and facilities cost estimate effort is completed, initial engineering feasibility studies begin, followed by capital equipment and assessment

facility cost. Capital equipment and facility costs are estimated by pricing manufacturing and testing equipment, floor space and building needs, and unique tooling systems

outlined in specifications. This costing process uses standard equipment manufacturing

catalogues, quotes, or estimates published in various manufacturing studies. The cost of

highly specialized equipment and tools is usually determined from similar equipment

costs. If necessary, escalation factors are applied to project these costs into the future.

3
TR-459 S=~I.' -----------------------------~::..::.-:.:::..:

The next step is to estimate direct and indirect costs, including materials, labor, transportation, depreciation, and overhead.

Direct materials cost includes purchased parts, subassemblies, and all materials used to

fabricate parts. The direct materials cost is stated in standard cost for parts manufacture. Material quantity standards are based on system/product specifications as to size,

shape, appearance, desired performance characteristics, and tolerance limits. The detailed parts drawings and manufacturing specifications provide the required quantity for

the various materials and parts that make up the finished product. Direct materials cost

is based on market information. Reductions in direct material costs are estimated by

analyzing available quantity and lot discount rates for the total materials bill. Direct

material cost is also affected by visibility, scrap and rework, and fee. The cost of purchased parts and subassemblies is mainly determined from vendor quotes for parts and

subassemblies and from catalog prices.

Estimates of direct labor cost begin with manufacturing specifications. They determine

the operations to be performed on the fabricated part and the time required to man the

factory equipment required to make the part. Industrial engineering methods (based on

historic data obtained from similar products) and efficiency factors are applied to determine the person-hours required to complete each operation. Direct support hours for

planning, tooling, and product support are estimated by application of standard factors.

Time required for materials handling and supervision is usually covered within the applied

burden rates. The direct labor cost is the product of person-hours in a given operation

times pay rate. The sum of all the direct labor costs associated with each operation

yields the total direct labor cost of producing a part. Hourly rates for labor are based on

prevailing costs in similar industries for a particular region.

Indirect manufacturing costs include items such as supervision and clerical help, materials handling, operating supplies, maintenance, janitorial work, process engineering,

transportation, hand tools, quality assurance not included in inspection operations, and

spare parts.

Transportation cost is determined from obtaining transportation costs of firms located in

the same region. Other indirect manufacturing expenses are included in the overhead

rates. The amortization of the asset, return on investment, income taxes, property

taxes, and capital repairs are included in the fixed-charge rate. The fixed-charge rate

converts capitalized cost into a series of uniform end-of-year payments to repay the investment at the stated interest rate over the life of the asset.

Once this cost process is completed, the first unit cost of a given design concept, including material, labor, transportation, depreciation, and overhead, is estimated. Its

practical value is limited to providing a solid base for application of the aggregated cost

estimation techniques. The unit-cost estimate does not reflect an overall cost reduction

due to labor learning, economies of scale, and technological change over time. Because

of the dynamic nature of cost reductions in manufacturing new products, generalized

statements about specific cost data are meaningless. A thorough manufacturing cost

analysis for a particular new product becomes complex when alternative cost reduction

techniques that have their basis in the engineering bottoms-up cost approach are

considered.

4
TR-459 -------------------------------- -~ ~~

2.3 ENGINEERING PARAMETRIC APPROACH

The engineering parametric approach combines scaling laws, direct analogy, cost factors,

and cost estimating relationships (CERs) to estimate and predict cost reductions in manufacturing new products. Various government studies show this approach applies to communications equipment and advanced weapon systems. The most fundamental component

is the CERs. Based on engineering-economic theory basis and principles of statistics and

econometrics, cost estimating relationships are quantitative expressions of cause and

effect between cost, the dependent variable, and selected design and performance characteristics (e.g., weight, horsepower volume, material type, conversion efficiency) the

independent variables. G. H. Fisher and R. L. Petrushel present a broad description of

the derivation of cost estimating relationships [5,6].

Detailed parametric costing relationships can be particularly useful for a new product

during its early phases of planning and development when only mission and performance

parameters are defined. These relationships can also be applied in evaluating many

design options. During the early phases of a particular design concept, limited and uncertain physical and performance characteristics are available regarding new product development and manufacture. Therefore, relationships between aggregated components of

product cost and the physical or performance parameters of the product are derived from

cost histories on prior programs or from surrogate parts resembling the pieces to be manufactured in configuration, material, and production methods.

There are practical limitations to the engineering parametric approach. It is difficult to

select a surrogate part relevant to a given product component in terms of configuration

similarity, material correspondence, and fabrication congruency. Even if the analogous,

surrogate parts or subassemblies are truly analogous there are limitations inherent in statistical inference. In addition, cost histories on prior programs are imperfect indicators

of future costs of a given design concept.

2.4 AGGREGATE PREDICTION OF COST REDUCTIONS

The aggregate prediction of cost reductions in manufacturing new products involves a

number of conceptual and analytical issues associated with relationships between changes

in a firm's costs and changes in its output; the cost function. Economic theories of the

production function of a firm and the prices it pays for its inputs determine the firm's

cost function. Since a production function may have different forms, with either one,

some, or all of the input variables, cost functions may also have different forms. Price

theory gives most of its attention, however, to two cost functions-the short-run and the

long-run. Since price theory is based on the neoclassical theory of the firm, the shortand long-run cost functions can provide insights into economic and technical issues that

influence the behavior of costs and prices over time. However, since neoclassical theory

of the firm is a static, or at best a relatively static depiction of behavior, it does not

exhibit direct applicability of the short- and long-run cost functions to predict future

cost reductions. The cost functions have been extensively used in empirical studies of

production. Walters [7] provides a survey of applications of cost and production

functions.

The simplest short-run relation between changes in the costs of a firm and changes in its

output assumes that the fixed costs-those of the fixed plant and equipment, as well as

the state of technology of the firm-are constant. The firm's decision in the short run involves only determining the optimal quantity to produce. In the short run, fixed costs are

5
TR-459 S=~II.'1i - ------------------------------=-=:..:.....::.:..: ~~

relatively high, since little is done to achieve economies of scale due to expanded plant

capacity; improve economics of production technology and utilize optimum forms of

current manufacturing techniques; introduce new production methods; and change the inherent form of the product. Therefore, the only short-run manufacturing cost reductions

are those due to increasing labor productivity: (1) as workers repeat an operation, they

learn, and the number of person-hours required per unit of production declines; and (2)

management techniques such as planning, purchasing, control, and supervision improve.

Other variable costs may also decline as production increases. For example, direct materials costs may be subject to volume discounts, more efficient purchasing, or reduction

of rejects.

It is practically impossible to specify what the sources of short-run cost reduction will be

for a new product. Therefore, in predicting cost reductions in manufacturing new products in the short-run, the learning curve based on bottoms-up engineering costs has been

used as an approximation tool for future costs.

In the long run, all factors of production are assumed to be variable. The firm's production function has no fixed inputs; the firm has no fixed costs. Hence, the firm's longrun decision involves a simultaneous determination of the optimal level of output, as well

as the optimal mix of factor inputs. Greater opportunities exist in the long run for a cost

reduction in manufacturing of new products due to (1) technological change (Le., substitution of more efficient production equipment, processes, or products for less efficient

ones); and (2) economies of scale (i.e., expanded plant capacity, increased labor efficiency resulting from worker specialization, or more efficient combination of production

factors). Changes in scale, technology embodied in the fixed capital, and the inherent

form of the product occur gradually. Consequently, the long-run cost function reflects

unit cost changes that are relatively smaller than those of the short term.

To estimate cost reductions in the manufacturing of a new product, the experience curve

concept is applied as an aggregate tool to summarize cost changes resulting from a number of different long-run sources. This concept has been applied because it allows approximation of future manufacturing costs. Although of great practical importance, it is

not possible to estimate the relative proportions of total decrease in unit cost that are

caused by changes in long-run cost reduction sources, or to characterize the manner in

which unit cost changes occur separately from changes in the specified long-run cost reduction sources. It is not feasible to anticipate with any real accuracy how or why the

costs of any specific product are going to change in the future.

6
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SECTION 3.0

THE CONCEPTUAL FOUNDATION OF LEARNING

3.1 BACKGROUND

The learning curve is a generalization about the sources of short-run cost reductions. A

literature survey of learning curve applications [8,9,10] indicates that different authors

have presented a wide variety of definitions of the sources of cost reduction in manufacturing new products that are generalized in this phenomenon. There is no consensus in

the literature as to whether a given process innovation will result in a shift in the

learning curve or a movement along the curve. This distinction is of critical importance

for policy makers.

Any changes in variable inputs to the production process are defined as modifications of

a given process and hence a movement along the learning curve. Variable inputs are

those production factors whose cost varies with short-term fluctuations of output. Any

change in fixed inputs to the production process is defined as a switch in processes and,

hence, a shift in the learning curve. Fixed inputs are production factors in which cost

does not vary with short-term fluctuations of output. Thus, improvements in the efficiency or organiaation of the work forces, management, or other variable inputs are regarded as movements along the learning curve. Changes in scale or technology embodied

in fixed capital are considered shifts in the learning curve. This definition makes movements along the learning curve analogous to short-run adjustments in traditional economic theory and shifts in the learning curve analogous to long-run adjustments. The

separation of those sources causing shifts in the learning curve from those causing movements along the learning curve, allows a precise distinction to be made between the experience concept and the learning curve effect. Also, policy recommendations are made

regarding optimal actions to take to ensure the achievement of cost goals.

The learning curve is considered here to be an aggregate tool that empirically summarizes the sources of cost reductions in manufacturing new products within one particular

process.

The experience curve herein is restricted to the analytical tool that collectively generalizes the shifts in the learning curve that result from changes in scale or technology embodied in fixed capital.

This section extensively discusses the definition of learning, approximation function, procedures for estimating learning curves, empirical applications of learning curves, and

limitations of learning curves, in addition to the conceptual foundation of learning.

3.2 DEFINITION OF LEARNING

The expression itself is borrowed from psychology, which has found through experimentation that humans and many animals learn at certain rates by repeated trials.

The conceptual foundation of learning observed for manufacturing operations originated

around 1920 in the aircraft industry and was subsequently reported by T. P. Wright in

1936 [17]. Its basic concept, that the direct labor input required to produce each of a

series of airframe orders for a particular plane model diminished at a uniform rate as the

7
TR-459 S=~I 'Il'--------------------------------

orders accumulated, is still accepted theory. During subsequent years, it was found that,

once production on a plan commenced: the fourth plane required only 80% as much

direct labor as the second; the sixteenth plane, only 80% as much as the eighth; the

twentieth, only 80% as much as the tenth; and so on. Based on this uniform pattern, it

was concluded that the rate of learning to assemble aircraft was approximately 80%

between doubled quantities.

The learning concept, its properties and uses, has been the subject of extensive study and

discussion [8-16] producing relevant data and theoretical knowledge of the subject. The

learning concept, also called manufacturing progress function [8], is presently used to

characterize an increasing labor efficiency: again, as workers repeat an operation, they

learn, and the number of person-hours required per unit of production declines; the efficiency improvement is regular and predictable [11, p. 410]. This definition has been used

in various empirical studies [8,9,11,14-16]. Conway and Schultz state that it "holds

promise of providing a better basis for the budgeting of engineering and other efforts in

cost reduction activities, for the budgeting of production vs. product engineering, and

possibly for providing some objective indication of organizational achievements" [8,

p. 39].

Variations of the preceding learning phenomenon definition used by a number of authors

[12, pp. 25-27; 10, p, 24] are of relatively limited value because of the conceptual and

empirical uncertainty involved [9, p, 4] •

The defined concept of learning emphasizes an increased direct labor efficiency as the

principle causal factor in short-run manufacturing cost reduction. Conway and Schultz

believe that "operator learning in the true sense of performance of a fixed task is of negligible importance in most manufacturing progress" [8, p. 42] , although it significantly influences the number of subsequent opportunities for cost reduction. In most industries

tooling changes, redesign of production methods, and improved management techniques

such as planning, purchasing control, and supervision are more important sources of cost

reduction than direct labor learning. "Such changes are usually the result of management

and engineering effort rather than operator learning in any sense" [8, p. 42].

In this report, a learning concept is restricted to mean improved productivity of a direct

single input (e.g., labor).

3.3 THE LEARNING CURVE

A curve depicting the learning curve concept "was worked up empirically from the two or

three points which previous production experience of the same model in differing quantities made possible" [17, p. 122]. To represent the functional interdependencies between

labor hours per unit of output and cumulative production, the following approximation

function is suggested [8,16J:

Y - xa-b

x- (1)

where

Yx = labor hours required to produce the xth unit of production;

x = cumulative production, between l st and xth units;

a = estimated labor hours required to produce the 1st unit; and

8
TR-459

-~ ~~ -------------------------------

b = a measure of the rate of reduction in labor hours as cumulative production increases.

Parameter a is obtained by extrapolating the learning curve to x =1. If x =I the numerical value of the parameter a is given by:

y1 =a(U-b =a •

The numerical value of the parameter b describes the rate of decrease in direct labor

input only of a new product. Its past values have normally been in the range -1 < b < O.

The logarithmic transformation of Eq. I is:

log Yx = log a - b log x

which is a straight line when graphed on logarithmic coordinates. Figure 3-1 portrays a

hypothetical learning curve on arithmetic coordinates; Fig. 3-2 illustrates the same curve

on logarithmic coordinates [9, p. 7].

The learning curve applies only to the range of production over which learning occurs

[8,15,16]. In actual experience, a given operation eventually approaches a plateau or a

steady-state phase, during which direct labor input remains constant as cumulative production increases [8,16]. This is often encountered with large output volume. In practice, this phase should be estimated independently U6, pp. 330-331]. The start-up and

steady-state phases are illustrated in Fig. 3-1 and 3-2.

In practice, the rate of reduction in labor hours is often replaced by the learning-curve

slope, that is, "the percent of learning that occurs each time output is doubled" [16,

p. 330]. The learning-curve slope is often called the progress (pI) [I6, p. 330]. Its mathematical expression is defined as follows:

if Xl and x2 are two points in production and x2 =2xI ,

-b

Y2 aX2 -b

then PI = --- = --=b = 2

Yl axI

(3)

or b =log PI/log 2 •

The rate of reduction is usually described by giving the complement of the r~duction that

occurs when the production quantity is doubled. This is equal to the PI = 2- Hence, in

an estimated learning curve with b equal to -0.322, its PI would be 80% (2-0.~22 =0.80),

indicating that each time production output doubled the direct labor input would be reduced to 80% of its for mer value.

The learning curve defined by Eq. 1 is called a unit curve. In practice, a cumulative

average curve is often applied [8,16,18].

I

9


TR-459 S=~I - '.~I \~." -----------------------------...::..:..:..-

It is characterized by the following approximation function [8, p. 40]:

-b

x (4)

where

Yx = cumulative average labor input per unit, averaged over all units of production from the first to the Xth.

x = the production count (x =1, 2, ••• , X).

N = cumulative production between the 1st and Nth unit.

All other symbols are the same as in Eq. 1.

For values of N greater than 100, Eq, 4 can be approximated by [12, p. 40]:

-b

ax

1 - b

where all symbols are the same as in Eq. 4.

The various empirical studies [19-22] report substantial difficulties in determining the

pragmatic superiority of the preceding curves by logic and empirical evidence. Conway

and Schultz observed that "the choice in usage has been largely a matter of computational convenience; when the unit curve is of primary interest and use, the first model is

selected; when the cumulative average curve is primary, the alternative model is used.

Since in either case the two curves are parallel for large quantities of production the difference is important only during the initial stages of production and hence for many

applications, not crucial" [8, p. 41]. Figure 3-3 illustra tes two hypothetical curves drawn

on logarithmic coordinates. The upper curve shows the cumulative average curve, while

the lower line represents the unit curve.

In applying the cumulative average curve, an analyst should keep in mind that "the averaging process has tremendous power to smooth the data and enhance the appearance if

not substance of the curve" [8, p, 41] .

Although learning in the literal sense here is restricted to improved productivity of

direct labor input, the learning curve is an analytical tool applied to generalize the combined effects of both increased efficiency of workers and management innovations [15,

p. 89].

3.4 LEARNING CURVE ESTIMATION

The method used to estimate the learning curve structural parameters depends upon the

nature and format of the data required. The use of the learning curve as a predictor requires some clear definition of the quantitative measures relating to the variables used in

estimating its structural parameters. The variables used in estimating learning curve

12
TR-459 S=~I -~ "*, ~< --------------------"'--------

structural parameters (i.e.; the estimated direct labor input required to produce the first

unit and the rate of reduction in direct labor input) are: direct labor hours required to

produce the xth unit of production and cumulative production between the first and xth

unit.

It is imperative that accurate, reliable, and properly aggregated "data be used for each

variable. Conway and Schultz summarize the difficulties encountered in obtaining the

production count.· The most important are [8, pp. 43-44]:

• Varying lot sizes, lead times, and schedules make it difficult to associate specific

costs with specific production quantities of the end product.

• Some components are produced in relatively large volume in initial lots and la ter

are split in production.

• Actual labor times are seldom accurately recorded; considerable doubt exists as

to the validity of operator times charged to direct vs. indirect labor accounts.

• Actual product costs in terms of dollars or labor time are, in many cases, unobtainable.

• Design and model changes make it difficult to judge if a change is significant

enough to justify treatm ent of the product as a new model, and when it should be

treated as normal progress with the current model.

• Definition of individual operations is not constant over time; a particular portion

of the work that must be performed on each unit often shifts from one operation

to another.

Some of these difficulties can be eliminated through the process of aggregation of the

parts manufacture operations and subassembly and final assembly operations. Conway

and Schultz state that "considering the sum of two operations the data are unaffected by

work transferred between these two operations (assuming the times for elements are

additive). Considering the product as a whole, redefinition of operations does not affect

the data. n••• in considering groups of operations we are in effect considering the sums

of a number of chance variables. In general such sums are much better behaved than the

individual change variable" [8, p, 43-44]. However, they indicate that the aggregation

process applies only to those operations that have basically the same cause-and-effect

pa tterns [8, p. 44]. Thus, each operation, part, subassembly, and assembly that has different cause-and-effect patterns is subject to its own learning curve.

Once the selection and aggregation process is complete, quantitative measures relating

to the cumulative production count and the direct labor input required to produce the xth

unit of production should be determined. The three quantitative measures that an analyst can develop, based on sufficient data, are:

• The sequential production units and corresponding actual direct labor input for

any particular unit.

• Production lot sizes released and corresponding costs accumulated on a production lot basis. The mean of a lot and corresponding average direct labor input is

calculated. An average direct labor input is weighed by the amount of production it represents.

• An annual cumulative production and corresponding cumulative average direct

labor input, averaged over all units of production.

13
TR-459 S=~I - "r.'-~, -------------------------------- '~~

After these measures are established, the learning curve can be developed either graphically or mathematically. In applying the former process, the log paper is used to plot a

direct labor input per unit against cumulative output. The line that connects these points

is the learning curve for the units produced. The slope of the line indicates the percentage of direct labor input reduction achieved as the number of units produced is doubled.

Alternatively, mathematical curve fitting procedures are applied to estimate which

equation best fits the data points collected. In applying this approach, the exponential

approximation function is assumed. On the basis of the empirical data, an analyst estimates the function's structural parameters by application of the ordinary least squares

method.

Although the exponential function is the most widely used functional form, it has not yet

been logically or mathematically rela ted to manufacturing procedures in terms of quantitative cause and effect. This form is intuitively appealing because it reflects the fact

that, in some industries, progress is rapid during the early stages of production and continues at a decreasing rate [15, p. 89]. Several authors proposed alternative approximation functions such as log-quadratic. Orsini summarizes this debate, the results of which

have been inconclusive [13, pp, 24-30]. Krawiec and Flaim state that "since the logic behind the learning concept does not suggest a particular functional form, analysts should

specify an equation which best summarizes the data" [9, p. 10].

Perhaps the most difficult problem associated with estimating learning curves is aggregation. The production of a given product is not a single process, but rather a summation

of fabrication, processing, subassembly, final assembly, installation, and testing, subject

to its own learning curve. The aggregated learning curve for a given product is the vertical sum of the individual unit learning curves. The process of aggregation of the individual unit learning curves rests on a strong assumption that the exponential approximation function (which is linear in logs) holds for each manufacture, subassembly, and final

assembly operation as well as for the completed product. It is difficult to justify assuming such linearity. Conway and Schultz indicate the implications involved by considering the sum of two unit learning curves for a series of two components of a given end

product:

(5)

a plot of (YL + Y2)x against x on log coordinates is a convex curve whose shape depends on

the values tal' a2' bl, and b2). The plot will be straight if and only if b1 = b2 = b

[8, p. 41]. This indicates that the rate of learning is the same for both components,

which will not generally be the case. Theoretically, the exponential learning curve can

at best apply to only one level [8, p. 41]. Krawiec and Flaim indicate that "from a

practical point of view, aggregating learning curves for processes involves complex calculations. If analysts decide to use the exponential function for each suboperation and

then the sum for convenience, they should realize that doing so will result in errors in

computation" [9, p. 11].

An alternative approach is to calculate the actual cost of the first unit "as a basis for

extrapolation and use of a uniform 80% characteristic to define slope" [8, p. 44]. The

14
TR-459

-~ ,~, --------------------------

first unit cost, the basic parameter to loea te the function, may be defined in a number of

ways depending on the perspective of the analyst. In a development firm, it may be

viewed as the cost of the first test article with or without a nonrecurring engineering

allocation or as a synthetic value extended from industrial engineering standards that

have been determined at, say, the lOath unit of production. Development of the first

unit cost can be accomplished employing the engineering build-up cost method "on the

basis of rome implicit conception of stabilized production volume and conditions peculiar

to the firm and its environment" [8, p, 44]. The applicability of this approach to estimate

numerical values of the structural parameters of the function is questionable. Although

the approach is widely used in the aircraft industry, Conway and Schultz do not recommend its application because "There is no such thing as a fundamental law of progress

such as the '80% learning curve'..•. No particular slope is universal, and probably there

is not even a common model" [8, p. 53].

Despite the various difficulties and unsolved problems mentioned, the structural parameters of the specified approximation function can be estimated either graphically or

mathematically.

3.5 EMPIRICAL APPLICATIONS OF LEARNING CURVES

The learning curve was developed as a managerial planning tool. It has been used as a

basis for facilities and manpower scheduling, pacing assembly operations, and cost estimation [8, p. 50]. Cost estimates obtained through learning curves have been used as the

basis for contract negotiations in the aircraft industry, and for a variety of pricing,

buying, and financing decisions [15, pp. 91-95].

Estimated learning curves can be useful tools for predicting manufacturing costs. At the

center of empirical studies is the concept of separating labor, materials, and overhead

costs, the factors that make possible both cost reductions and concomitant increases in

quantities produced. Prediction of labor costs will be discussed first. Next, the estimation of the parameters of the curve for materials and overhead costs will be considered.

Then, procedures for obtaining total product costs will be outlined. Finally, the limitations of learning curves will be addressed.

3.5.1 Labor

A learning curve indicates the relationship between labor input (measured in labor hours

per unit of output) and cumulative production. In order to determine labor costs, labor

hours must be multiplied by appropriate wage ra tes. Even though some authors have used

learning curves to predict labor costs directly [13,14], labor hours and wage rates should

be calculated separately for several reasons.

First, economic theory suggests that as the productivity of an input increases, its wage

rate should also increase [23, pp. 293-307]. Second, for some of the industries examined,

Conway and Schultz found that progress in labor hours was roughly offset by rising hourly

wages and that labor costs were less predictable than hours [8, p, 491. They state:

Use of labor hour or a machine hour base has been found to be much more

illuminating than dollar costs. Where dollar costs are desired they may be

used as a multipli er, but price changes serve to mask production progress in

terms of fundamental resource consumption. [8, p. 53]

15
TR-459 S=~I.'------------------------------- Finally, labor hours can be reduced by substituting more skilled and more expensive labor

for less skilled workers. However, reducing labor hours could cause higher labor costs

[15, p, 90]. For these reasons, labor hours and wage rates must be calculated separately

to avoid errors in labor cost predictions.

3.5.2 ~aterials

Material generally decreases in cost as the quantity of output increases. Although the

estimation of the parameters of the curve for materials costs has been of significant

concern, little has been written concerning reduction in the materials component of

product cost. Only a few authors have examined materials cost reductions due to volume

discounts, more efficient purchasing, or reduction of rejects [17,18,20,24]. Conway and

Schultz believe that "there is no reason why one could not quantify past experience and

use it to predict reduction in materials cost. Lacking such information, one would probably neglect progress in this area except for obvious economics associated with quantity"

[8, p. 51].

3.5.3 Overhead

Some empirical studies indicate that amount of overhead varies, within limits, with quantity of output. Overhead is usually expressed as a percentage of direct labor costs that

results in a log-linear relationship for overhead costs. Overhead costs fluctuate greatly

in different cases depending, for example, on structural components (i.e., taxes, depreciation, executive salaries) or when a particular factory is engaged in the manufacture of

one or many types of products [17,24].

3.5.4 Obtaining Total Produetion Costs

An aggregated cost curve for direct application to different quantities of the end product

can be developed by combining factors of labor, material, and overhead. Wright pointed

out that the shape of a total cost curve developed by this method in the aircraft industry

"will start out at 83%, then change to 87%, and finally reach 90%. This change in slope

is an indication of the relatively greater importance of material to labor as quantity increases" [17, p, 126]. This cost estimation procedure is distinctly different from using

experience curves to predict total unit costs directly.

3.6 UMITATIONS OF LEARNING CURVES

Learning curves can be useful tools for estimating changes in production costs. They

have been applied successfully in aerospace and related defense industries [8,15-17]. The

relevance of the learning curve in labor extensive manufacture-automobile assembly,

apparel manufacture, and the production of large musical instruments-was found by

Baloff', The studied products, modes of manufacture, and industries differed significantly

from each other and from the aerospace industry to suggest a greater, wider application

of the learning curve model in labor-intensive production activities [16, p. 339J. There is

need for a greater generalization for and application of the learning curve model in

capital-intensive industries. Labor learning as defined here may only be a minor source

of cost reduction in these types of production activities. However, the literature

[8,14-17,24] does not provide empirical evidence questioning the applicability of the

16
TR-459 5=~1'(·: -~ .-------------------

learning curve concept in capital-intensive industries. These learning curves are subject

to three important limitations.

First, the learning curve is a method of estimating changes in labor productivity which

occur after production operations begin. If substantial efforts are devoted to preproduction engineering and planning and if labor productivity is higher than it otherwise would

have been, the learning curve will not capture the progress that has been made.

Second, learning rates vary substantially among industries, firms, products, and types of

work [8,14,16]. Again, Conway and Schultz conclude:

There is no such thing as the fundamental law of progress such as the "80%

learning curve"* used in the aircraft industry. No particular slope is

universal, and probably there is not even a common model. The contention

that such exists is most difficult to defend either logically or empirically.

[8, p. 53]

Analysts must choose the appropriate model and estimate the rate of progress for that

product.

Third, the analyst must determine the range of production over which progress will

occur. Empirical data show that progress does not continue indefinitely [8,16,26]: The

application of learning curves is based on the assumption that progress in labor hours will

be achieved over the range of production specified.

*The "80% learning curve" is identical to the progress index.

17
18
S=~I 1

1*;1 -----------------------------=...::.:...~~ TR-459

SECTION 4.0

THE CONCEPT OF EXPERIENCE*

Like the learning concept, the experience concept was developed from the observation

that per-unit production costs declined in some industries as a direct, estimable proportion of cumulative production [26-28]. Unlike learning that refers to the productivity of

a single input, the experience concept has been used to describe changes in total cost as

a function of cumulative production.

According to the Boston Consulting Group, which claims to have originated the concept,

"The experience ••• effect .•• encompasses all costs (including capital, administrative,

research, and marketing) and traces them through technological displacement and product evolution" [27, p. 6]. They state from their observations of various industries that

"Costs appear to go down ••• 20% to 30% every time total product experience doubles

for the industry as a whole, as well as for individual producers" [27, p. 12]. The experience concept appli es to long-term improvements in production processes and includes

cost reduction from every conceivable source, including technical improvements, input

substitution, economies of scale, new product design, and changing input prices.

4.1 EXPERIENCE CURVES

Experience curves are similar to learning curves and are calculated as follows:

c = axb

x {6}

where

Cx=the cost of producing the xth unit of output;

x = cumulative production;

a = estimated cost of producing the 1st unit; and

b =parameter measuring the change in total cost (normally, -1 < b < 0 is

assumed).

In practice, the experience curve usually refers to the percentage reduction in cost that

occurs when cumulative production is doubled. It is calculated the same way that the

progress index is calculated for learning curves:

(7 )

-b

aX2 -b

-b =2

aXl

An 80% experience curve would indicate that if cumulative production doubles, per-unit

production cost would decline to 80% of its level prior to doubling [28, p, 53].

*This section originally appears, in essence, in Krawiec and Flaim [9].

19
S=~I TR-459 ,Ii,--------------------------"'-..........

4.2 APPUCATIONS OF EXPERIENCE CURVES

The Boston Consulting Group (BCG) examined historical data for 24 products and found

that price reductions* were strongly correlated with cumulative production [27, pp. 69-

101]. Their results were summarized by Costello et ale [10] and are presented in Table

4-1. Similar data were compiled by Hirschman [26] and cited by Bodde [28].

These experience curves were not estimated by the BCG to be used directly for cost estimation purposes. Rather, the BCG and other authors cite experience curves as evidence

that costs can decline as the result of many factors; they recommend that managers be

aware of cost reduction possibilities when designing product-market strategies [26-28].

4.3 EXPERIENCE CURVES AND COST ESTIMATION

Even though cost prediction was not the goal of the Boston Consulting Group, some analysts have attempted to use experience curves to predict the future costs of solar technologies [24, pp, xiv-xv, 159-161]. Despite the statistical evidence that costs have declined in some industries, there are serious problems with using experience curves to

predict cost reduction.

One problem is that there is no logical reason to believe that costs should decline as a

function of cumulative production per see In other words, there is no a priori reason to

believe that the parameter b in the experience curve will have a negative sign. It is

quite possible that costs may be increasing for a particular product. Long-run costs

could increase due to rising input prices, government regulations requiring higher cost

production methods, or firms adopting new production methods that result in higher production costs.**

A second problem is that there is no way to separate cost changes due to technical progress from those due to falling input prices, because the experience curve describes cost

changes as a function of cumulative production. Declining input prices clearly influenced

cost changes for at least 9 of the 24 products cited by the BCG: electricity and the 8 petroleum-based products.f Experience curves necessarily reflect the combined effects of

both progress and declining input prices, and cannot be interpreted as measures of progress alone.

*Cost data were unavailable; price data were used as proxies for costs. Price changes

were measured in constant dollars. In general, prices are inadequate measures of cost

because prices are determined by both supply and demand conditions.

**Firms might adopt higher-cost (less optimal) production methods because of imperfect

information or uncertainty. In addition, if markets are not price competitive, firms may

pursue goals other than cost minimization, which could result in higher production costs

[30]. However, these factors are beyond the scope of this report.

,rCrude oil prices in constant dollars generally declined from 1950 to 1972 (see Table

4-1). Crude oil is an input to petroleum products; fuels derived from crude oil are used in

electricity generation.

20
S=~I TR-459 ,[II ------------------------------:..:.::...~~

Separating the influence of technical progress from that of changing input prices is

further complicated by the fact that both affect improvements in production processes

[31, p. 23]. Thus, if a change in the production process is made in order to substitute a

cheaper input for a more expensive input, that improvement cannot be expected to apply

to a solar technology unless the solar firm faces the same relative input prices.

The third problem involves source identification and analysis. Even though the experience concept as defined by BeG [27] and Bodde [28] includes cost reductions from logical

sources such as technical improvements, sources of cost reduction must be identified

separately and their impacts on costs must be analyzed directly. There is no other way

to evaluate whether cost reduction sources and their impact on costs can be expected to

apply to the production of solar technologies. Without careful analysis of these separate

sources, experience curves that are estimated from other product histories and used to

predict the future costs of solar technologies will yield arbitrary and perhaps misleading

cost estimates.

Analysts using experience curves to predict future costs of solar technologies should heed

the advice of the Boston Consulting Group, who originated the concept:

As a practical matter experience curves should be considered only as a

means of understanding relationships, not as a measuring device. There are

inherently too many difficulties in definition and factor measurement. This

applies particularly to the definitions of product and cost as well as measuring accumulated experience. [27, p. 63. Emphasis addedJ

21
TR-459 S=~II_I ----------------------------

Table 4.-1. EXPERIENCE CURVES IDENTIFIED BY THE

BOSTON CONSULTING GROUpB-

(Cumulative Industry Production vs. Price)

Experience Curve

Process or Slope Data

Industry (Constant Dollars) Comments

Germanium Transistors 90% (1954-1960) 1954-1968

Industry 70% (1960-1968) 1954-1969

Silicon Transistors Industry 90% (1954-1959) 1954-1969

70% (1960-1965)

80% (1965-1969)

Germanium Diodes 90% (1955-1959) 1955-1968

70% (1960-1968)

Silicon Diodes 90% (1955-1959) 1955-1968

70% (1960-1968)

Integrated Circuits 70% 1964-1968 0965 &

1966 monthly data)

Crude Oil Increasing prices 1946-1968

(1946-1948) real prices

90% (1948-1958) increased from

70% (1958-1968) 1946-1948

Motor Gasoline Prices increased 1946-1968

(1946-1949) (average price/

90% 0949-1957) gal. excluding

75% (1957-1968) California)

U.S. data only

Ethylene 100% (1953-1963) 1953-1968

70% (1963-1968) U.S. tariff price

vs. U.S. data only

Benzene 70% (1953-1963) 1952-1968

90% 0963-1968) U.S. data only

Paraxylene 90% 0957-1961) 1957-1968

70% (1961-1968)

Low Density Polyethylene 90% (1952-1959) 1952-1968

70% (1960-1968)

Polypropylene 90% (1959-1961) 1959-1968

80% (1961-1968)

Polystyrene (General Purpose 90% 0943-1954) 1943-1968

Molding and Extrusion Resin) 70% 0954-1968)

Polyvinylchloride 90% (1946-1955) 1946-1968

7096 (1955-1961)

80% 0961-1968)

22
TR-459 S=~II.}I -----------------------------

Table 4.-1. EXPERIENCE CURVES IDENTIFIED BY THE

BOSTON CONSULTING GROUpa (concluded)

(Cumulative Industry Production vs. Price)

Process or

Industry

Experience Curve

Slope

(Constant Dollars)

Data

Comments

Primary Aluminum

Primary Magnesium

Titanium Sponge

Monochrome Television

Receivers

Total Freestanding

Gas Ranges

Total Freestanding

Electric Ranges

Facial Tissue

Japanese Beer

80% (1929-1939)

70% (1939-1948)

no trend (1961-1968)

80-90%

100% (1950-1954)

70% (1954-1968)

90% (1947-1954)

70% (1954-1968)

100% (1946-1950)

70% (1951-1967)

90% (1946-1957)

70% (I957-1967)

90% (1933-1945)

increases (1945-1948)

90% (1948-1966)

80-90%

1929-1968

1929-1968

significant

deviations in trend

1950-1968

strong deviation

between 1958-1968

1947-1968

1946-1967

average wholesale

price

1946-1967

1933-1966

1951-1968

retail price

minus indirect tax

Electric Power

Re fined Cane Sugar

70% (1939-1943)

80% (I943-1968)

70%

1930-1968

1935-1968

very wide fluctuations in data

aFrom Costello et ale [l 0, pp. 32-33]. Data were compiled from Boston Consulting Group

[27, pp. 69-110].

23
24
TR-459

-~ ;.~I ~' ------------------------------- SECTION 5.0

DEVELOPMENT OF LEARNING AND EXPERIENCE CURVES

FOR SOLAR ENERGY TECHNOLOGIES

5.1 BACKGROUND

The crucial issue in the commercial development of solar energy technologies is: "How

will the cost of solar technologies change over time?" Many current solar technologies

are too expensive to be economically feasible. Some of these technologies may achieve

substantial cost reductions from improvements in product design and production techniques. These cost reductions are usually estimated or predicted on the basis of rough

comparisons in dollars/lb or by comparing similar products. While such predictions may

indicate a general cost range, they are of little value in making investment decisions at

either the public or private levels in identifying specific areas needing improvement.

A brief and simple method for predicting the effect of future changes in design, materials, or production methods of solar technologies is essential to optimum development

and planning.

An analyst can estimate future normal costs of a given technology or product if he

understands their basic relationship to learning and experience. Such a relationship can

be quantita tively expressed by the application of learning and experience curves.

5.2 DATA LIMITATIONS

Empirical applications of learning and experience curves have been based upon products

for which production data are available [8,12-14,16,17,26,27]. However, solar energy

supply systems represent emerging technologies with both mature components and processes and untested prototype parts, assembly methods, and installation processes. Many

solar technologies require significant amounts of on-site construction. Most learning and

experience curves have been developed for the products that were manufactured, rather

than constructed on-site.

Since the solar industry is young, few data are available on production economics and

hardly any basis exists for estimating a partloular learning or experience curve slope. In

such situations, learning and experience curves must be based on surrogate products or

systems resembling solar products in configuration, materials, and production methods.

The selection of an appropriate surrogate product or system with an available cost history allows inferences to be made regarding the cost of the new product. It is assumed

that because of these similarities, the new product will undergo the same cost reduction

pa ttern as the surrogate product. Procedures for developing learning and experience

curves for new products or systems using surrogate data follow.

5.3 ESTIMATING LEARNING AND EXPERIENCE CURVES FOR SURROGATE

PRODUCTS OR SYSTEMS

The procedure begins with the conceptual disaggregation of the new product or system

assembly into components, subassemblies, and processes. This disaggregation depends

upon the analyst's concept of availability of relevant surrogate components, subassemblies, and processes.

25
TR-459 S=~I - '.,-----------------------

~~

The surrogate part or subassembly should then be identified. A number of selected surrogates might later be eliminated because of special circumstances in their commercial

development, the fact that some entered the market fully developed, and the lack of reliable historical cost data.

The third step is to collect historical data on the costs of each of the surrogate parts or

subassemblies and to break down these costs according to labor, materials, and overhead. Labor input should be measured in person-hours per unit of output, multiplied by

appropriate wage rates, in order to determine labor costs. Economic theory suggests

that as the productivity of a labor input increases, its wage rate should also increase [23,

pp. 293-307]. For some of the industries examined, Conway and Schultz found that

progress in labor hours was roughly offset by rising hourly wages and that labor costs

were less predictable than labor hours [8, p. 49]. Labor hours can be reduced by substituting more skilled and more expensive labor for less skilled workers; however, by reducing labor hours, a firm could face higher labor costs [I5, p. 90]. "For these reasons,

labor hours and wage rates must be calculated separately to avoid errors in labor cost

predictions" [9, p, 13]. Material inputs should be measured in both physical units and

dollar-cost-per-unit. Unit overhead cost should be expressed in dollars, and individual

elements of the costs generated should be corrected to eliminate the effects of

inflation. Production volume for surrogate parts and subassembly should also be

determined.

Historical data collection for surrogates is the most difficult task, not because general

information does not exist, but because detailed data concerning labor, labor costs,

material costs, and investment on a historical basis may not exist in some instances, may

be extremely sensitive in others, or may be so interspersed with other unrelated data as

to be either unusable or uncollee table, Most of the data involved, except for regulated

industries, are usually unavailable to the public. But it is not always necessary to use

current or recent proprietary data sources to establish trends. An analyst can use published retail or wholesale manufacturing data to break down information into its costspecific components. He can then juxtapose specific technological improvements made

in particular industries with new data. However, this process requires tremendous

amounts of time and effort.

Once the data are obtained, an analyst has to identify the technological factors that

caused specific manufacturing changes or trends for the selected surrogates and determine how this development would or would not apply to the new product or system. This

step rationally projects how past cost history may be extended for the new proposed

product or system. In addition to carefully examining technological factors that caused

specific changes in manufacturing the surrogates, an analyst should assess production

volume effects. Surrogate product volumes probably will differ widely from one another

and from the projected volumes for the new product. These volume variations can have a

significant cost impact and have to be standardized to produce realistic predictions.

Based on the developed data, learning curves can be estimated using the procedures outlined in Sec. 3.6 of this repoct,

Finally, learning curves for surrogate products should be evaluated in order to determine

whether the same rate of progress can reasonably be expected to occur in the new

product or system. The surrogate learning curves can then be used to estimate labor requirements for each part, subassembly, assembly, and process involved in the product.

Significant materials cost reductions in the surrogate product's history should be estimated. In order to calculate total costs for each process, labor costs must be added to

materials, capital, and overhead costs [9, p. 25]. A composite product or system cost

26
TR-459 '1.;1 ------------------------------- -~ ~~

curve can be calculated by aggregating total cost estimates for each operation in the

production process.

5.4 ESTIMATING EXPERIENCE CURVES FOR SURROGATE PRODUCTS OR SYSTEMS

An experience curve can be estimated on the basis of production experience for related

products or systems. The selected surrogate should be similar in configuration, materials, and processes of manufacture and installation to those that are anticipated for

the proposed new product or system.

Once an appropriate surrogate product has been selected, historical data on costs

(including capital, administrative, research, and marketing), production rate, and volume

must be collected, examined, and traced through technological displacement and product

evolution. The procedure for estimating experience curves is outlined in Sec. 4.1.

If the experience curve that is developed for the surrogate product is judged applicable,

it can be used to predict an overall cost reduction for the new product. It should be

noted that such prediction can be used only to provide a rough estimate of long-term

changes in the cost of the proposed new product.

27
28
S=~I TR-459 i.1 ----------------------------....::..::=.:::........::~ - .

SEC110N 6.0

TECHNICAL ASPECTS OF HELIOSTAT DESIGNS

This section summarizes technical aspects of McDonnen Douglas and General Electric

heliostat designs. Included here are basic elements of a solar central receiver system, a

description of the DOE heliostat development program, some generic heliostat designs,

and key cost factors in heliostat designs.

6.1 BASIC ELEMENTS OF A SOLAR CENTRAL RECEIVER SYSTEM

The point focus central receiver system, commonly known as the "power tower," is a concept in which reflected sunlight is concentrated on a heat absorbing receiver mounted on

a tower. This absorbed energy is used to heat a fluid (such as steam, air, salt, or sodium)

which in tum can be used directly as a heat source for processes, to provide heat for operation of a turbine, or can be stored for later use. Figure 6-1 conceptually shows a typical central receiver for power generation using conventional steam technology [3].

As sunlight strikes the heliostat, it is reflected to the receiver/boiler that absorbs the

energy and turns water to steam. The steam is then directed to a conventional turbine

generator where electrical power is produced. When excess steam is produced, the heat

is extracted and stored for later use when sunlight is not available. Then, the steam is

cooled and condensed so that it can be pumped back to the receiver. Either a conventional wet cooling tower or a drying cooling tower can be used to cool the exhaust steam.

Several options exist in the selection of the thermodynamic cycle and coolant: closed or

open-cycle air, or closed helium Brayton cycles, as well as the conventional steam

Rankine cycle. The central receiver designs are characterized by high temperatures and

high pressures in comparison with other solar technologies. Turbine inlet temperatures in

excess of 800 K (980 0 F) and pressures of 7 MPa (I ,0 15 psia) are typical design values for

steam Rankine cycles. The closed-cycle helium systems reportedly will utilize inlet temperatures as high as 1,088 K (l,5000F); open-cycle Brayton will utilize inlet temperatures

of 1,250 K 0,8000 F) [32].

The heat to the elevated receiver is supplied by a field of tracking collectors called

heliostats. Various designs are being considered for the placement of the tower in relation to the heliostat field, depending upon plant capacity. A typical field layout for a

plant with a capacity greater than 10 MW is shown in Fig. 6-2 [34]. The tower is not

placed in the center of the field, but is sli~tly to the south of center to obtain the best

annual optical efficiency from the entire field.

The collector fi eld is usually the most expensive element in a central receiver system,

often accounting for more than 50% of the plant cost (Fig. 6-3) [33]. The collector field

consists of a large group of two-axis tracking heliostats (Fig. 6-4), each of which independently tracks the sun in a manner such that the sunlight is directed onto the towermounted receiver [36]. The field of heliostats is generally controlled by a centralized

computer that sends sun position data to each heliostat. Small microprocessors located

at each heliostat then translate those signals into azimuth and elevation angles for the

particular heliostat and direct it to the correct position. A centralized computer is also

used to supply common commands to the entire field during emergency or maintenance

operations.

29



S=~I '.!: --011~~ TR-459

In a large plant, several thousand heliostats may be used. For example, a recent design

of an advanced lOO-MW central receiver plant was sized for a capacity factor of about

0.7, which corresponds eto an annual average of approximately 8 hours of s~orage p~r day. To achieve this, 15,177 heliostats, each with reflective areas of 40 m (431 ft )

were assumed [35].

Since each heliostat in a large field is identical, significant cost reductions through mass

production and the use of commercially available, standard components are possible.

Moreover, further cost reductions can be expected through normal engineering ~eveloprnent. Current estimates by contractors and laboratories range as low as $42/rn ($3.90/

ft 2) reflectivity for quantities of 250,000 heliostats/yr [36], a potential reduction of 10-

20 times over the costs of early prototype units.

6.2 THE DOE HELIOSTAT DEVELOPMENT PROGRAM

The heliostat field represents the largest fraction of the cost of a solar central receiver

system. Accordingly, DOE has placed major emphasis on reducing heliostat cost through

several programs. The goal is to develop reliable cost-effective heliostat designs that

are amenable to mass production and meet strict performance s1fndards The current

cost goal, established in 1975, is to produce heliostats at $72/m ($7/ft~) reflectivity.

This goal is under review, however, and will probably change to reflect newer cost estirnates resulting from recent advances in heliostat design, as well as interest shown by

large manufacturing companies such as General Motors and Ford.

Various central receiver concepts have been studied during this century, notably by the

United States, Russia, Italy, and France. The modern power tower concept had its inception in the United States in 1969-70 when Hildebrant and others conceived a 500-MWt h

system to drive an MHD generator for a hydrogen production system. A proposal for a

similar system, to generate steam to drive a conventional turbine, was presented to NSF/

Rand in 1972, who funded a series of studies between 1973 and 1975 to develop systems

and components. One of these contracts, awarded to Martin Marietta, resulted in the

construction of a 1 MWtg cavity receiver and its subsequent successful test at the French

solar facility at Odiello.

The first major funding of this concept occurred in 1975, with the award of four contracts by the Energy Research and Development Administration (ERDA). Three of these

contracts (Martin Marietta, McDonnell Douglas Astronautics Company, and Honeywell

Inc.) included preliminary designs of a first-generation lO-MWe system, while the fourth

(Boeing Company) was for heliostat development only. All four contracts included the

design, construction, and testing of four prototype heliostats. Testing was completed in

1977 by all four contractors; design and test data were subsequently published.

Also in 1975, proposals were submitted to ERDA for the design and construction of heliostats for the Solar Thermal Test Facility (STTF). The contract, which was awarded to

Martin Marietta in 1976, resulted in the installation of 222 heliostats at the STTF site

near Albuquerque, New Mexico. The system was operational by 1978 and data on the

performance of the individual heliostats, as well as the entire field, have been gathered.

*An informative and detailed discussion of early work on the central receiver concept can

be found in Ref. 37.

33
S=~II.' ----....--------------~~ TR-459

Preliminary results also have been published [38]. Although the STTF was intended to be

a research and test facility and the heliostats were designed to meet stricter performance plans, considerable insight and experience into large-scale production was obtained.

The goals and approach of the heliostat development program are described in some detail in Ref. 39. Heliostat design has already progressed through several generations, as

shown in Fig. 6-5, [41, p, 7] starting with the original National Science Foundation hellostat and progressing to the prototype heliostat design. Additional paper/test hardware

generations are expected.

In 1977, a solicitation was released to design low-cost prototype heliostats. ERDA

awarded contracts to McDonnell Douglas, General Electric, Boeing, and Solaramics. The

McDonnell Douglas and Solaramics heliostat designs use glass mirrors and a steel structure. The General Electric and Boeing designs involve less rigid, cheaper heliostat mirrors encased in inflated plastic enclosures. The plastic enclosure designs result in both

lower mirror costs and lower reflectivity. One major cause of reflectivity loss is because

of transmission losses through the plastic enclosure.

The McDonnell Douglas low-cost prototype design was selected as most promising at the

beginning of FY78. To foster competition, contracts were awarded to five separate contractors to build prototypes. All of these prototypes will use the glass-mirror design, because the requisite plastics technology is not advanced enough to inspire confidence in

the plastic enclosure design. These low-cost prototypes are referred to as secondgeneration heliostats. While the glass-metal prototypes are constructed, research on development of suitable plastic materials for use in the enclosed heliostat design will be

continued.

While second-generation prototype heliostats are being built, work is proceeding on a

third-generation design. Twelve special studies are being performed on heliostat component design and maintenance. A new heliostat components solicitation was released in

1978. Resulting design improvements are planned to be incorporated into a thirdgeneration heliostat design in the early part of FY81.

Two second-generation designs discussed here are those proposed by McDonnell Douglas

Astronautics Company (MDAC) and General Electric (GE). They represent the two generic heliostat concepts already described.

6.3 DESCRIPTIONS OF GENERIC HELIOSTAT DESIGNS

Two generic heliostat concepts are being studied by several contractors: the glass-steel

and inflated plastic bubble concepts, now best represented by the designs of MDAC and

GE, respectively. The following describes the design, manufacturing, and installation of

these designs [3, 34].

6.3.1 The McDonnell Douglas Prototype Heliostat Design

The prototype heliostat design portrayed in Fig. 6-6 and Table 6-1 summarizes the technical characteristics of this design. The heliostat is divided into four subassemblies.

These subassemblies are the reflector panel (one-half of the reflective unit), the drive

unit (including the pedestal), the foundation, and the heliostat electronics (including controllers and control sensors).

34


TR-459

S=~I(.I -----------------------------

Table 6-1. TECHNICAL DESCRIPTION-COLLECTOR SUBSYSTEM

• Reflector-6 laminated reflector mirrors bonded

to the reflector support structure.

• Reflec tive Surface

Reflective Surface Area

Second Surface Mirror

Glass Back Lite

• Mirror Backing Structure

IMBD Cross Beam 14 Gage

OUTBD Cross Beam 18 Gage

Diagonal Beam 14 Gage

Hats 16 Gage

• Drive-Consists of a rotary azimuth drive,a double

jack elevation drive, center main beam, and pedestal.

Center Main Beam

Tube

Flange

Elevation Drive

Jacks

Motor (Two)

Drag Link-Weldment

Azimuth Drive

Housing-Weldment

Drive Unit (Retainer)

Helicon Input

Harmonic Output

Final Drive Ratio

Motor

Pedestal

Power Distribution Equipment and Wiring

Step Down Transformers

Position Indicators

Incremental Encoder-Motor Tum Feedback

• Control/Instrumentation Equipment

Data Distribution Interface

Digital Microprocessor

Communication Interface

Heliostat Controller

Digital Microprocessor

Drive Motor Controller

Cantrol Sensors

Communication Interface

787 kg (1734 Ib)

49.05 m2 (528 ft2)

1.5 mm (0.060n)

4.8 mm (0.1875")

469 kg (1034 Ib)

173"-27" deep

173fT- l I fT deep

112"-26" deep

130"-6"

578 kg (1273 Ib)

122 Kg (268 lb)

81' L 16" dia.

18fT square

102 kg (225 Ib)

5 ton ball screw (63

kg, 139 Ib)

1/4 hp; 480 VAC (9.5

kg., 21 Ib)

29.4 kg (65 Ib)

185 kg (407 Ib)

108 kg (238 Ib)

68 kg (150 lb)

162:1

242:1

39,204:1

1/2 hp; 480 VAC (8.6

kg, 19 lb)

169 kg {373 Ib

4160 VAC, 3- Phase

480 VAC, 3-Phase

Hall effect sensors

1985 readiness

78 per field of 25,000

1 per heliostat

37
TR-459 S=~I~_I-------------------------

Table 6-1. TECHNICAL DESCRIPTION-COLLECTOR SUBSYSTEM (concluded)

Signal Distribution Equipment

Fiber Optic Transmission Cable Included in power

cable

• Foundation

Concrete

Steel Reinforcement

Steel Form

5478 kg (12076 Ib)

194 kg (428 Ib)

34 kg (75 Ib)

38
TR-459 -~ ;.;;

~. ------------------------------.-;;~

Refleetoe, Each reflector panel is composed of six mirror modules and a support frame.

The mirror modules (Fig. 6-7) [3] are 1.22 by 3.35 m (48 by 132 In.) and made of a 1.5 mm

(0.060 in.) second surface mirror laminated to a 4.8 mm (0.1875 in.) glass back panel.

The clean reflectivity is estimated to be from 0.92 to 0.95, depending on the thickness

and chemical state of the glass,

The mirror assembly support structure is shown in Fig. 6-8 [3]. Each of the laminated·

mirror modules is stiffened with a pair of hat-section stringers which are part of the support structure assembly and are bonded to the glass when the reflector is assembled.

The 12 hat-section stiffeners are attached to the two cross beams that run the long distance of the reflector assembly, The deep, inboard cross beam is a 14-gage galvanized

steel sheet, 0.476 m (18-3/4 in.) deep and 7.62 ern (3.0 In.) wide. The shallow outboard

cross beam is IS-gage galvanized steel, 12.7 em (5.0 in.) deep and 5.72 em (2-1/4 In.)

wide. This cross beam is attached to the main beam by diagonal frames (beams) which

tie into this cross beam at two points that are 4.62 m (167.9 in.) apart. These diagonal

beams are 6.35 em (2.5 in.) wide with 1.91 ern (3/4 in.) return flanges.

Drive Unit. The drive unit is composed of a rotary azimuth drive, a double screw jack

elevation drive, and a pedestal. All drive motors are three-phase, 480-volt AC. The

function of the drive unit assembly is to rotate the heliostat mirror about the azimuth

and elevation axes. The drive unit will be operated for solar tracking, emergency

Slewing, stowage, and maintenance activities. Major performance requirements are described in Table 6-2.

The design life of the drive unit is 30 years. The drive unit is required to move the mirror from a stowed position to acquire the sun, track the sun during the day, and return

the mirror to its stowed position at the end of the day. This lifetime is planned to be

achieved without any scheduled maintenance activity.

The pedestal is a vertical tube 3.18 m (125 in.) high. At the top, the drive unit is welded

to the pedestal; at the bottom, the lower 1.12 m (44 in.) is expanded to give a slight taper

for slip-joint attachment to the rigid foundation.

The central torque tube main beam connects the two reflector panels (the reflective

unit) together and ties the reflector to the elevation hinge and the elevating jacks at the

top of the drive unit assembly. The main beam carries all the airloads and dead weight

loads from the reflector to the pedestal.

Heliostat Electronics. The heliostat controller is located in a housing on the top of the

drive unit. The controller receives and transmits commands from the collector controller and responds to requests for data. A microprocessor calculates the motor revolutions required to maintain tracking and activates the motor controllers. The motor

controllers switch the motor on and off to produce the required motion. The motor revolution sensors detect motor revolution and direction, and the controller maintains a count

of accumulated revolutions. A nonvolatile memory retains motor counts and alignment

data in the event of a loss of power. The field wiring terminates at a junction box located on the pedestal. Data are routed to the heliostat controller, decoded, and relayed

to the next heliostat in the link if not addressed to the receiving heliostat, Acknowledgment of receipt of a message and status are also transmitted.

39


TR-459 S=~I '.1 ---------------------------- Table 6-2. DRIVE UNIT REQUmEMENTS

Requirement Azimuth Elevation

Travel + 2700 oto -1800

Maximum Travel Time Under Load 1800 in 15 minutes

Maximum Static Load 9830 N-m -32,650 N-m

(87,000 in. Ib) -289,000 In.-Ib)

= 00

Maximum Starting Load 10,050 N-m + 13,890 N-m

(89,00 in.-Ib) + 122,900 inv-lb)

= -50 0

Maximum Running Load 10,050 N-m + 26,790 N-m

(89,000 inc-lb) + 237,100 inv-lb)

= -50 0

Maximum Overturning Moment 42,140 N-m

(373,000 inv-lb)

Backlash/Hysteresis 1 mrad 1.6 mrad

Back Drive None None

Life 30 years 30 years

Minimum Stiffeners 1.13 x 196 N-m/rad 1.516 x 106 N-m/rad

1.0 x 10 In.-lb/rad 1.342 x 107 in.-lb/ rad)

42
--------.....--------------------..:::.=:~~ TR-459 -~ .•

Foundation, The foundation is a drilled pier, 0.6 m (24 in.) in diameter. The pier extends

about 1.2 m (4 f t) above grade and 6 m (20 f't) below. A tapered steel shell secures the

mounting surface to the pedestal and serves as a form for the protruding end of the pier.

Heliostat Weight. Weights for the various heliostat components are given in Table 6-3.

6.3.2 The General Electric Prototype Heliostat Design

The enclosed heliostat (Fig. 6-9 and 6-10) [34] consists of an aluminized film reflector

deployed on a lightweight, eight-strut frame mounted on a single pipe pedestal. Azimuth

and elevation drives are also mounted on the pedestal. The entire assembly is housed in

an air-supported enclosure. An open-loop control system directs centrally computed sunangle steering commands to all heliostats in the field.

Reflector. The reflector is a 55-m2 octagonal surface of metalized polyester film held

in place and tensioned. by eight tubular compression struts and tension wires. These are

supported by a reinforced structural plastic hub with a spring-loaded tensioning device.

The reflector assembly is illustrated in Fig. 6-11. The principal parts of the assembly

are: reflector surface, reflector corner beams, struts, tensioning wires, hub assembly,

and tensioning device.

Support and Drive Assembly. The heliostat support and drive unit (Fig. 6-12) is a lightweight dual axis (azimuth and elevation) pedestal that provides the structural support and

interface for the reflector assembly. The support and drive structure consists of the following major components or assemblies: elevation axis, yoke/pedestal assembly, elevation and azimuth drives, and lower support and foundation. The foundation pipe is an 11

ern (4.5 in.) diameter pipe embedded to a depth of at least 1.5 m (60 in.).

Enclosure. The enclosure is an air-supported, transparent plastic structure that arrives

at the site in three zippered sections. These sections can easily be attached to each

other and to the plastic bag foundation ring.

The plastic bag foundation ring is a V-shaped bag that is buried in the ground and filled

with earth to form an anchorage for the inflated enclosure. It has an outside diameter of

9.23 m (27.0 ft),

A ground cloth is used to cover the earth within the enclosure. It acts as a vapor barrier

and also suppresses vegetation growth and dust movement. To form a complete seal, the

ground cloth will be zippered to the foundation bag.

The enclosure is kept inflated by a blower which draws 15 W of electric power.

Weight Breakdown. Estimated weight for the various components of the prototype heliostat are given in Table 6-3 [34].

43




S=~I TR-459 ,111 ------------------------"-=--:=

Table 6-3. GENERAL ELECTRIC HELIOSTAT WIDGHT BREAKDOWN

Assembly Weight, kg rn»

Reflector

Support Structure and Driver

Controls

Enclosure

Foundation

Installation (Packaging)

31.3 (69)

16.3 (36)

14.5 (32)

166.0 (366)

76.2 (168)

6.8 (15)

6.4 KEY COST FACTORS IN HELIOSTAT DESIGN

A significant factor in all heliostat designs is the total allowable error allocation. System performance is based upon a high percentage of reflected sunlight hitting the receiver, and accumulated error from all sources affecting a heliostat is considered part of

the total field efficiency. Errors occur either from wind- or gravity-induced deflections

of the structure, mechanical failures in the various heliostat subassemblies, foundation

misalignment, thermal deformation of components such as mirror modules, and positional

uncertainties in the control and instrumentation system.

Design of the various subassemblies or components depends on compromises between cost

factors and performance standards; the goal is to produce the lowest overall installed

field cost. A heliostat, therefore, has to meet requirements derived from total annual

system performance requirements.

Certain factors affect particular elements of a heliostat. Table 6-4 identifies those design parameters that significantly affect cost.

The highest costs in the "hard" (glass/steel) heliostat design involve the drive unit and the

reflective unit. The drive unit must be able to withstand the full torque induced on the

structure by the highest winds encountered during operations or emergency stow conditions. These loads may exceed 30,000 N-m (289,000 in.-1b) at certain times; the drive

must be able to withstand these loads without permanent deformation or breakage.

In addition, the drive unit (which includes position indicators such as encoders) must

track with great precision in order to assure that the reflected beam intercepts the receiver. The allowable backlash on a typical drive mechanism is 1 mrad (3.3 arc-min) for

azimuth and 1.6 mrad (5.3 arc-min) for elevation. This degree of backlash is more stringent than that usually encountered in commercial applications.

The glass mirror accounts for only a small fraction of the cost of the reflective unit.

The majority of the cost lies in the assembly of individual mirror modules.

The third major cost element for a hard heliostat is the foundation and site preparation,

which accounts for more than 20% of the total installed cost. The costs of the foundation are largely determined by the allowable error in heliostat alignment and deflections

48
TR-459 *, ------------------------------- -~

Table 6-4. DESIGN PARAMETERS AFFECTING HELIOSTAT ELEMENTAL COST

Component Significant Design Parameters

Structure

Drive Mechanisms

Elec tronic/Elee trical

Foundation

Installation and Assembly

Control

Reflective Surface

Reflective area, operation wind loads, error budget

Operational wind loads, slew speed, error budget

Operational modes, motor power, error budget

Site characteristics, error budget

Labor costs, adaptability to tooling

Operational and safety requirements, error budget

Spectral reflectivity, reflective area, wind loads,

thermal deformation

and by soil characteristics at the site. Many desert soils contain large quantities of

materials like (e.g., or bentonite) that are subject to long-term shifting. Accordingly,

they require more preparation and a larger foundation.

The foundation, in addition to supporting the weight of a heliostat, must be sufficiently

strong to withstand overturning by the wind without permanent deflection in either the

pedestal or the soil. For the MDAC design, this overturning point may reach as high as

42,140 N-m (373,000 in.-lb).

The elemental cost breakdown for the "soft" or plastic bubble enclosed heliostat is

slightly different. Because of the protection afforded by the plastic enclosure, wind

loads are less; as a consequence, costs of the drive unit and reflective unit are less. The

fraction of cost attributed to the structure, however, increases because of the cost of

the plastic enclosure. Foundation and site costs constitute a slightly higher proportion of

the total cost than for a hard heliostat.

Critical areas for cost reductions can be identified for each generic design. For the

glass/steel heliostats, major emphasis should be placed upon the drive units, the reflective unit (including structure), foundations, and site development. The more equal distribution of costs in the enclosed bubble design dictate that all elements receive closer attention. Major emphasis should be placed upon the foundation, site preparation, and the

heliostat structural support and protection.

49
50
TR-459

S=~I·I.;1 ------------------------ -~ ~.

SECTION 7.0

PROTOTYPE HELIOSTAT COST SCENARIOS*

7.1 INTRODUCTION

Establishing an adequately detailed costing scenario is the fundamental requirement for

estimating and predicting cost reductions. This section presents the costing scenarios

through description of the basic characteristics of market potential, production and installation volume, and the general configuration of the manufacturing/assembly facility

module. This section also indicates the points in cumulative production volume where an

effective, steady-state production process may be established along with the associated

first-unit cost for the steady-state scenario.

The scenario presented in this section represents only one of many possible scenarios.

Variations in market growth and location, competition, basic design, available production

technology and resources, and government and business policy may have a significant impact on the scenario and cost-reduction rate.

The term production here includes factory procurement, fabrication, assembly,and

checkout; transportation and handling required to ship the hardware to the installation

site; and field procurement, fabrication, assembly installation, and final checkout of the

hardware.

7.2 FACTORS INFLUENCING COST SCENARIOS

In developing cost scenarios for a prototype heliostat, the following factors have been

considered: (1) market projections, (2) the implied growth of volume, (3) design and development status, (4) plant and equipment impacts, and (5) management alternatives and

concerns.

7.2.1 Market Potentials

The market for heliostats is tied to the cost and availability of alternate energy products. The basic economic trade is the annual cost, including maintenance, per Btu of the

incremental capital employed in collecting solar energy versus the annual cost per Btu of

alternative fuels and other energy sources. For some potential solar energy users, this

issue has been marked by considerable uncertainty about the future price of fuels and the

capital needed for solar applications. Some analysts believe that the market is generally

unfavorable for centralized solar systems, but concern about the future availability and

cost of alternative sources of energy and the intrigue of a free and renewable energy

source keep interest high.

These circumstances have made it difficult to identify a broad market based on current

economic analysis, although the price trends of oil and gas and cost inefficiencies associated with using coal or nuclear power for intermediate-sized plants certainly suggest a

market potential, especially with incentives. As a result, market forecasts have been

*This section was written with the assistance of J. H. Nourse of McDonnell Douglas

Astronautics Company [41].

51
TR-459 S=~I - !;t.-~I ----------------------------- ~-

based on a penetration analysis given an absolute energy demand, expected growth in service by alternative energy sources, energy short-falls, and "target" solar energy costs.

Sandia Laboratories specified that Prototype Heliostat Study Contractors consider design

and production scenarios that would be appropriate at rate production heliostat outputs

of 25,000, 250,000, and 1 million units per year. In addition, a one-time pilot production

of 2,500 units was to be analyzed. Sandia also indicated that the design and production

scenario be conceived so that costs associated with the higher production rates are consistent with the DOE goal of $72/m2 reflectivity. Other than stating that the market

cover eight Southwestern states in the 1990s, no other market description was indicated. Sandia's main purpose was to determine the costs that could be expected under

certain and steady market conditions.

It is, however, necessary only to characterize and shape the growth of the market in general for purposes of cost reduction analysis and technology readiness considerations.

Figure 7-1 shows one possible market situation [3]. Although the details may be outdated, they are representative of the general aspects of a market plan considered at the

time of the study. Both the total market and an assumed share for one producer are indicated, as well as a possible gap in 1985 between overall demand for energy and this particular market scenario.

The illustration may be considered in phases. The first is a period of governmentsupported development projects requiring limited heliostat production capacity. At any

given time, demand for an individual supplier's product may range from the total market

to none. Then, a period of transition is expected where eventual users may begin to deploy the concept on a limited basis provided that any economic disparities with competitive energy sources are balanced by incentives. This is perhaps the most critical period

to the survival of the concept, and costs must not exceed goals. Of course, the final

phase is the most unpredictable; it depends on the relative success of the intermediate

phase, as well as on the cost and availability of alternative energy. It could be the period

in which both the technology and the relative cost of solar energy have received users'

acceptance. If expectations about energy shortages prove true, the potential demand for

and production of heliostats could be great.

7.2.2 Produetion Volume Progression

As indicated, the volume scenario established for the McDonnell Douglas Prototype

Heliostat Study [3] calls for 25,000 heliostats/yr at the lowest commercial rate. For the

study, it was assumed that the baseline plant (25,000 units/yr) will initiate operations at

the 100,000th unit and continue production for 500,000 units over the next 20 years. At

this period, production and installation capacity increases to 250,000 units/yr; an additional 5 million units will be produced at this rate. After 15-18 years of operation in

these facilities, capacity increases to 1 million heliostats/yr and continues for another 20

years.

This scenario provides three important variables-cumulative volume, annual rates of

production and installation, and rate of volume growth. While cumulative volume may be

the primary cost-reduction curve variable, quality and quantities of production and

growth rates had a much more significant impact on the production and installation scenarios devised for the Prototype Heliostat Study. The study assumes a steady production

rate where productive resources may be selected and geared in just the right balance to

economically achieve required output over the expected production period.

52

TR-459 S=~I.I ---------.,,.-----------------------

The expected rate and quality of growth further influences the timing and degree of capital employment. Capacity growth, although potentially rapid in the first 5-10 years, is

expected to be intermittent following the nature of demand. This could delay installation of the baseline rate production facilities, or cause it to be staggered until a steady

25,000-unit/yr output appears maintainable•. As indicated, growth in capacity from this

point has been portrayed in the study as a series of 20-yr steps extending approximately

50-60 years to about the year 2045. If this is true it could lead to a somewhat slackened

rate of innovation and the actual growth will probably occur in more frequent steps with

a modular approach to capacity expansion.

7.2.3 Design and Development Status

The status of design, development, test, and evaluation (DDT&E) is of interest from a

number of perspectives. The issue is not just whether a design is proven, but also

whether it is producible and cost effective, will have future technological impact, and

the extent to which the innovation may be implemented within current schedules or must

be programmed into the future. From a cost reduction viewpoint, very little improvement occurs in actual hardware cost while the design is still in a state of change. However, parallel design-to-cost studies may identify and project "paper" improvements

leading to rapid cost reductions as advance design-to-cost configurations and plans can

be incorporated.

The baseline 25,000/yr scenario represents plans for a well conceived, advanced design,

although the production plans for early supply assume that many specific design or production innovations will not be immediately available for technological or economic

reasons. Heliostat design has already progressed through several generations, as shown in

Fig. 6-5, starting with the original National Science Foundation heliostat and progressing

as far as the prototype heliostat design. Additional paper/test hardware generations are

expected.

A considerable amount of heliostat development effort will have been expended in research experiments such as the Barstow plant, and in second-generation, preproduction

hardware. Also, there will be some final production design involved in the first prototype

pilot production purchase. However, once the design has been established, further design

effort is self-supporting; Le., only design changes that will result in net design/hardware

cost reductions will be incorporated.

7.2.4 Plant and Equipment Impact

A fourth major consideration is the impact of prudent use of plants and equipment.

Figure 7-2 shows the importance of finding the right combination. The figure indicates

the approximate break-even investment associated with a savings, using an existing

method, of 1 person-minute on each heliostat produced, transported, and installed in a

20-yr period, at various annual production rates. In the example, any investment that

costs less than $264 thousand would result in a savings at a production rate of 30,000

heliostats/yr. Given this incentive, the objective is to identify cost-saving equipment!

processes and plant arrangements; influence the design to permit desirable alternatives;

and balance the use of plant and equipment resources with respect to desired output and

other management considerations.

54

TR-459

S=~I.! ----------------------- 7.2.5 Management Altematives and Concern

Management concerns generally relate to a specific firm's policy, traditions, practices,

and fiscal constraints. They affect decisions about market approach, resource sharing,

allocation and balance, make-or-buy resolutions, organizational structure, labor and

other industrial relations, and choice of technology. The firm must also consider availability of funds, labor, and other resources; the impact of outside influences such as government, unions, and special interest groups; the flexibility of their business approach

and the technical requirements/acceptability of their product in the face of market characteristics and competition; and finally, the acceptable risk exposure and return on investment. The result of these considerations sets the ultimate course of the production

scenario and the overall business approach in a manner intended to assure the firm's financial success and meet market requirements.

7.3 DESCRIPTIONS OF THE BASELINE PRODUCTION SCENARIO

The baseline production scenario for the MDAC and GE heliostat design describes production at a rate of 25,000 units/yr. Thus, the 25,000 units/yr rate has been studied in

greater depth than other production rates. This rate represents significant high-volume

production situations, yet may be easily translated into higher or lower production-rate

scenarios. As a result, this scenario effects a viable business approach that meets early

business constraints and simultaneously attempts to capture an important share of future

markets. A summary of the baseline production scenario and changes in the scenario for

higher production rates for each of the heliostat designs follow [3,34]. More detailed discussion of the production concepts is found in the cited references. Additional information on high-production heliostat manufacture can be found in Ref. 1.

7.3.1 The McDonnell DOuglas Prototype Heliostat Design Production Scenario

In developing the baseline production scenario for MDAC prototype heliostat design, the

overall approach has been to minimize hand labor and achieve quality through mechanization, with as much flexibility and as little risk as possible.

Equipment has been designed to reduce hand labor and to assure continuously acceptable

production performance. Wherever practical, automatic handling equipment has been

used to keep operator handling time to a minimum, especially where operation cycles involve manual loading or unloading. Reflective unit equipment used for fabrication,

transfer, and installation has been mechanized to address special problems in handling

glass and to minimize potential damage.

Assemblies are automatically checked out with special equipment in the factory to reduce field problems. Pre-assembled units are installed with equipment designed to correlate time, crew size, and proficiency with equipment capabilities rather than human

capabilities, and final alignment and checkout is largely automated. Thus tools and

equipment are designed mainly for automatic production, according to specifications and

to production rates.

56
TR-459

S=~I'*I: ---------.--------------------- - '.

7.3.1.2 Quality Assurance

The quality assurance approach calls for verification of hardware through control of incoming parts and materials and attention to the quality of tooling and equipment. The

quality of incoming materials and parts is controlled through careful supplier selection

and source inspections for critical items. Inspection at the Receiving phase is carried

out for identification, certification, and any damage. Tooling and equipment are designed within specified tolerances; an automatic "out of spec" trend warning triggers the

necessary tool adjustments. Automated end-item inspections and alignments are used for

the reflector panels and at checkout stations for the pedestal/drive/electronics unit and

power distribution modules. A special test van is used in conjunction with digital image

radiometers and master control support to automatically check and align an installed

heliostat.

7.3.1.3 Transportation to Installation Site

Transportation covers the transportation of final assemblies to sites, and delivery of installation equipment. In addition to the foundation and field wiring materials, only three

end-products must be delivered-reflector panels, drive/pedestal/electronics units, and

power distribution modules. Special reusable open containers are designed for ease of

access and handling. Placement and removal will be automatic for these containers,

using robot-like controls. Equipment is transported from factory to site by a manufacturer-owned truck fleet.

7.3.1.4 Installation Equipment Ownership

Another important provision in the overall approach is that the heliostat manufacturer

will control (through ownership or sale/leaseback) the special equipment used to transport, install, and check the heliostats. The manufacturer will provide the equipment to

each installation on a cost-sharing basis, and assure that an efficient, uniform plan is

available to any installation. This equipment is important to the installation scenario at

all volume levels and probably should be made available as early as the pilot production.

7.3.1.5 F1exib~ty

Flexibility has been considered in both macro- and micro-modularity characteristics of

the facility. The plant may be thought of as a production module that may be more than

tripled in capacity by adding shifts, extending the work week, or adding automation

equipment simultaneously in a few critical areas. When production capacity is reached,

or design technology and competition dictate, anew, upgraded module may be added in

the most desirable location. Assemblies such as the drive, electronics, and panels may

also be moved into their own facilities. Fairly low production rates, at a greater cost per

unit, may be accommodated by eliminating a shift, deferring purchase of certain equipment, or by increased use of the industrial base. In effect, the scenario permits a logical

extension of pilot facilities.

Such flexibility allows a manufacturer to take some financial risk in proportion to product demand and at the same time remain reasonably current in production and design

technology. In addition, use of the industrial base for production of tubes and beams,

glass, chips, drives, and other components reduces capital equipment requirements. This

57
TR-459 S=~I '.!----~------------

approach permits a firm to expand according to market conditions, available resources,

product acceptability, and profitability.

1.3.1.6 Make Or Buy Plan

The make-or-buy plan is shown in Table 7-1, and indicates whether finished parts and

materials are made in the heliostat production facility or are procured from outside

sources. Generally, if an item is commercially available from several sources, and acquisition of specialized capability is required, purchase of the items is likely. Make

items, typically, are specially designed and are critical to schedule, cost, or process. Obviously, this plan is a fundamental variable in sizing a facility.

7.3.1.7 The Heliostat Factory

Figure 7-3 provides a schematic of the prototype heliostat production plant. The plant is

62,500 ft2, located in the Southwest, and has access to rail lines and adequate roads. The

locality also provides a work force of less than 300 predominantly unskilled factory employees. The level of workers' skills permits low average labor wages, while the factory

burden and direct support typically reflect an operating stand-alone plant limited to one

product line. Annually, the factory produces 50,000 reflector panels, 25,000 drivel

pedestal/electronic assemblies, and 25,000 foundation caps. The plant operates on a twoshift basis, 5 days per week, 240 days a year.

Reflector Panel

Figure 7-4 provides a pictorial schematic of the reflector panel fabrication and assembly

line. A total of 52 assemblers, handlers, and operators are required to run this line as

follows:

Hardware Element

Mirror Modules

Support Structure

Assemble and Bond

Total

Workers

16

18

18

52

The two glass lites are stacked on a reusable A-frame, and automatically loaded on conveyors using vacuum cups. From that point, the mirror modules are fabricated, treated

with adhesive, and lined up on the bonding tables. Each of the facets on the bonding

tables is curved and canted individually so that each mirror module will rest in its appropriate position relative to the other five modules.

The beams and hat sections for the support structures are delivered from the rolling mill,

formed, and palletized for assembly. The details are purchased, formed, and palletized,

and then delivered to the fabrication area after inspection. The inboard, outboard, and

diagonal beams are loaded into separate punch presses that automatically punch the bolt

holes. The parts then proceed on an overboard monorail to a weld and drill station where

they are lowered into a floor-mounted fixture and secured. Spot welding of the inboard

and outboard areas is accomplished simultaneously. After welding, the bolt holes for attachment to the drive units are jig-bored.

58








TR-459 S=~I :;.1 ------~-------------------

The welded structure is removed from the weld fixture and proceeds on a monorail to

two stringer attach stations. The 12 stringers are loaded and clamped in position in the

assembly fixture, and the welded structure is lowered onto the stringers, clamped in

place, and bolted.

The structure is then removed from the tool and is moved by monorail to a dip-clean,

rinse, and air-dry station prior to bonding the structure to the mirror modules. At this

point, the structure is lifted from an adjacent conveyor by a fixture that places the

structure so that the hat sections rest on the two bond material lines that have been

appli ed to each mirror module. The fixture holds the drive unit interface in correct

alignment with respect to the mirror module surfaces. The mirror module curve and

cant, and the hat section interface imperfections, are taken up in the thickness of the

bond line.

The reflector panel is ambient-curved and vacuum-lifted from the assembly line and

placed on shipping racks for transfer to the site. Special exhaust systems will remove

vapors emitted by the acids, solvents, and adhesives. The exhaust systems may require

scrubbers before the exhaust is released to the outside environment.

Special attention has been given to glass handling and transfer through the production

lines. Glass handling equipment will be completely automatic and include unstaeking machines for removing large sheets of glass from vertical storage and placing them on a

horizontal production-line conveyor. Air float tables are used for transfer. Additional

handling equipment includes a 90° conveyaized transfer unit.

Drive and Pedestal Fabrication and 1\$embly. Drive and pedestal fabrication and assembly comprises a series of relatively standard equipment lines for metal forming, joining,

removal, and assembly that utilize automatic feed and clear features and are interconnected by conveyors, hoists, drive assembly carriers, and other handling devices. For example, shuttle-type loaders allow machining while hardware is being loaded and unloaded

on the numerical control machining centers and vertical turret lathes. This also allows

individual operators to service more than one machining activity. Automatic positioners

and gravity-fed conveyors allow the large, bulky items like the main beam and pedestal

to ron to their next station rather than be handled between stations. Where items required several positions for assembly, such as on the azimuth drive, specialized equipment permits multiple part orientation by individual operators. The type of handling

equipment and tooling used reduces the cost of major machine tool investment. Figure

7-5 shows the drive assembly flow and lists possible purchased materials and parts. The

basic equipment required is:

Major EqUipment

Number

Required

Flame cutter

G&L vertical turret lathe

Numerical control lathe

Automatic lathe

Hydrosize machine

Punch press line with coil straight

Hydraulic press (300-ton)

Deep draw pres;

Small press

Multi-drill station

Numerical control milling machine center K&T

2

2

2

1

1

1

2

I

1

67


TR-459 S=~II" ------------------------

Major Equipment

Number

Required

Conventional mill

Fusion welder

Inertia welder

Marvel saw

Broach

Automatic clean deburr station

Cam grinder

Total

1

6

2

2

1

1

1

36

Minor Equipment

Material handling (conveyors, hoists) 106.68 m

(350 ft)

Motorized drive carrier 5

In addition, an assembly and checkout station is required, as shown in Fig. 7-6, for integration of the drives, electronics and pedestal.

Ninety-eight "B" welders, machinists, handlers, assemblers, and operators are required as

follows:

-Nurnber

Numerical Control Machinists

General Machinists

"B" Welders

Process Machine Operators

Assemblers

Handlers

Total

8

37

17

4

16

16

98

Twelve of the above workers are allocated to the pedestal and foundation cap and 16

workers to the azimuth drive assembly and final integration of the drivers, pedestal and

electronics. The remaining 70 workers are involved in fabrication.

Elec1ronics and Elec1rical Components. The electronic and electrical components-the

heliostat controllers, data distribution interface (DDI), and pedestal junction box

(J-box)-are listed as buy items. However, for costing purposes, the harness assemblies

will be made in-plant. Figures 7-7, 7-8, and 7-9 indicate the purchased parts, the manufacturing flow, and processes involved in production of the heliostat controller, data distribution interface, and harnesses. These flows are representative of the flows involved

with the motor controller and encoder which are also buy items. The electronic assemblies will be procured from sources that have programmable automatic sequencer and insertion equipment. The flow solder technique is employed to complete the circuits, and a

functional test and burn-in is accomplished prior to installation in the housing. Commercial-grade components produced to military specifications will be employed.

70



TR-459 S=~II_ ---------------- _ A total of 32 assemblers, technicians, handlers, operators, inspectors, electricians and

mechanics are required, as follows:

Element Workers

Controller and DDI

Motor controller and encoder

Harness

Motor Test

Total

12

9

9

2

32

7.3.1.8 Installation

The heliostat is assembled in the field from basic component subassemblies that have

been checked in the factory. This simplifies field operations by reducing the number and

complexity of tasks performed in the field. The installation is mainly achieved by trade

labor supplemented with specially trained operators and technical personnel. The labor

force is supervised by the heliostat manufacturer. Figure 7-10 provides the sequence of

installation events; time and resource requirements for one crew are shown in Table 7-2.

Foundation. Standard construction techniques are employed for foundation installation,

and a number of methods could be employed. For the baseline method, rebar cages are

fabricated in a site-located shop with the foundation cap welded to the top. At the same

time, pier holes are drilled using a high-powered auger. The completed cages are trucked

to location and placed in the hole with light lifting equipment such that the cap extends

above grade. Concrete obtained from a local batch plant and trucked to the area is

pumped and vibrated through the top of the cap, filling both the hole and the cap extension.

Labor is required as follows:

Element

Workers

Crew Total

Cage Fabrication

Survey and Auger

Installation

Total

7

8

14

29

35

40

70

145

These 145 workers are capable of installing 25,000 foundations in 25 weeks, including lost

time for start-up/shutdown, personal time, fatigue shortages, inclement weather, and

lost efficiency. However, the numbers shown exclude concrete truck drivers and batch

plant laborers, who are covered in the concrete price.

Reflector and Drive/Control Unit. The reflector panels and drive/control units are installed using the equipment depicted in Fig. 7-11 and 7-12. The drive/control installation

equipment is used to lift the unit from the flatbed trailer, rotate it to a vertical position,

and place the pedestal section over the tapered foundation cap. The drive unit is then

oriented to true north, and downward pressure and vibration are applied to seat the unit

on the foundation. Once this is completed, the drive is filled with oil.

74

TR-459 S=~I~" -----------------------=.::=--==-

Table 7-2. INSTALLATION TASKS AND CREW SIZE

Task Number Time/Heliostat Crew and Equipment Allocations

1. Pedestal Excavation 30 min/heliostat 4 Trucks

Iron Work and Concrete 2 Hydraulic Cranes

1 Auger Rigs

3 Equipment Operators

4 Truck Drivers

2 Surveyors (part-time)

2 Oilers

2 Iron Workers

4 Rodmen

12 Laborers

2. Cable Installation 18 min/heliostat 1 Cable Plow

1 Cable Plow Operator

2 Laborers

3. Drive Unit Installation 18 min/heliostat 1 Pedestal/Drive Assembly

Installation Equipment

1 Installation Equipment

Operator

1 Millright

1 Laborer

4. Power Transformer/ 90 min/312 helio- 1 Milfwright

Distribution Panel stats 2 Laborers

Installation 1 Truck

1 Forklift

1 Truck Driver

5. Reflector Panel 21 min/heliostat 1 Reflector Panel Assembly

Installation Installation Equipment

1 Installation Equipment

Operator

2 Hi-Lift Forklifts

2 Forklift Operators

2 Millwrights

2 Laborers

6. Sensor/Calibration 8 hrs/3,000 heliostats 1 Field Engineer

Equipment I&C 1 Electrician

1 Volt-Ohm Meter

1 Oscilloscope

7. Connect, Check and 15 min/heliostat 1 Electrician

Close Out 1 Laborer

1 Test Set

8. Align Heliosta t 10 min/heliostat 1 Field Engineer

2 Technicians

1 Mobile Field Test

Station

76

TR-459 S=~I~'i ---------------------------- The reflector installation equipment is designed to simultaneously position two panels

next to the drive unit and provide convenient access for bolting the panels to the drive

unit. First, one pallet containing four reflector panels is loaded on each side of the installation equipment using a high-lift forklift. The equipment is then driven over the

drive, and a manipulator arm on each side (only one is shown in the drawing) engages a

panel. The arms move the panel to positions where they may be connected to the drive

using 8 bolts for each panel. The equipment is disengaged and moved over the next drive.

The work force employed is:

Element

Workers

Crew Total

Drive/Control

Reflector Panels

Total

3

6

9"

15

30

45

These crews are sufficient to install 25,000 reflector and drive/control units in 39 weeks,

with allowances for lost time.

Power Distribution and Sensor/Calibration Units. Installation of these elements includes

burying 88,350 m of branch circuit cable and placing and checking 79 power distribution

modules and 8 digital image radiometers. The cable is "plowed in" using a standard cable

plow that slices a V-groove in the soil to a .61-m depth and feeds cable into the bottom

of the groove at a rate of 76 m/hr, As the cable is placed, the plow guides the soil back

into the groove. At each point where power is required or distributed, the plow stops and

a large loop is allowed to form in the cable before the plow moves on.

Installation of the power distribution modules is basically a rnatter of pouring a small

foundation at each position and setting the equipment. A field engineer must further inspect the 8 digital image radiometers using a volt-ohm meter and an oscilloscope in the

final alignment of the heliostats.

Labor is employed for these tasks as follows:

Element

Workers

Crew Total

Lay Cable

Power Distribution Modules

Digital Image Radiometer

Connect, Check, and Closeout

Total

3

4

2

2

N/A

9

4

2

8

23

Plowing in the cable and the connections requires 39 weeks. Only 3 weeks are required

for the power distribution modules and a little over I week is required to install the digital image radiometers.

Alignment and Cheekout. A final tracking alignment and checkout are accomplished

using a control van, the digital image radiometer, and the plant master control. No

mechanical adjustments are required for the heliostat after installation. The alignment

78
TR-459

S=~I('! --------------------------

is done by establishing and adjusting position relationships in the heliostat controller to

reflect the differences between the programmed placement of the heliostat and the

actual position of the unit. New position information is required on the first alignment,

and a subsequent alignment compensates for vertical errors.

The control van is connected into the data distribution interface once for 24 heliostats as

the heliostats read positioning information from a common optical data bus. The group

of heliostats is then activated, moved to standby positions, and established on track. At

this point, the activities of the alignment branch into two categories: interacting manmachine alignment in the northern half of the field, and automatic search in the southern

half.

In the interacting alignment, a sighting mirror is placed on the reflector edge, and the installer views the position of the image with respect to an alignment target on the

tower. A verbal command is then given to the alignment operator in the control van that

brings the spot on the target. Once the spot is on target, the digital image radiometer is

used to establish the exact position and provide updated position information.

An automatic search technique will be used in the southern portion of the field because

the heliostats will be in a nearly horizontal position during much of the day. It is therefore inconvenient to attach a sighting mirror and observe the solar image. In the automatic search, the heliostat is moved in an expanding spiral search pattern until the target is intercepted. Afterward, the digital image radiometer is used to set the exact position and update it as in the interacting technique.

In conjunction with these activities, the heliostat will be checked for tracking, a .satisfactory image quality, correct data and power transmission, and lack of lubrication leaks

or installation damages. The labor required for this task is three 3-person crews over a

39-week period.

7.3.1.9 Changes in the Scenario for Higher Production Rates

Three main changes have been made in the 250,000 heliostats/yr costing scenario. First,

material weights have been reduced to their theoretical minimum for the mirror backing

structure and integrated drive unit structure. Second, parts have been eliminated in the

drive housing by assuming a welded flexspine/housing assembly. Third, automated assembly equipment has been deemed capable of eliminating 90% of hand labor and its additional use is assumed. However, production plant adjustments necessary to assimilate

au tomation assume that a lot of direct labor support is still required to maintain and provide basic direction to the automated equipment. Two types of industrial "robots" were

considered: one produced by Unimation, Inc., which is used in numerous automobile production situations, and a highly flexible one produced by Cincinnati Milacron. These machines are capable of performing almost any assembly, machine loading, welding, or handling operation. One other change assumed is that foundation installation will become

more mechanized. Also, overhead is assumed constant (i.e., reduced fringe costs will be

balanced by increased equipment cost and fringe rates), so that overhead rates increase,

dramatically. Additional changes, such as improved line flow, integration, and increased

supplier control, are considered through the operation of the cost reduction curve.

The 1 million units/yr scenario assumes further that production facilities will be more

functionally specialized; that designs, materials, and processes will continue to improve;

and that greater control over supply will be achieved. It also may become feasible to

79
TR-459 S=~II.'-----------------

vertically integrate the production of certain basic materials. All such changes have

been given effect through adjustments along the cost reduction curve to reflect costs at

the 15 millionth unit.

7.3.1.10 Produetion Volume Impact on Average Unit Cost

The MDAC heliostat cost comparison is presented in Table 7-3. Figures 7-13, 7-14 and

7-15 describe cost elements of the MDAC heliostat based on production rates.

The design/production, installation, and operating plans derived for the 25,000 units/yr

scenario support the belief that the DOE goal of $72/m2 reflectivity is obtainable. For

the rate of 25,000 units/yr. the average unit cost is projected to be about 20% lower than

that projected for the initial pilot production. Large cost reductions are projected for all

elements except foundation and site preparation. However, the costly foundation proposed for this design/production scenario permits important cost reductions in other

areas by (1) supporting a larger heliostat, and (2) contributing to a simplified field assembly and checkout procedure. Perhaps the largest cost reduction is projected in the drive

unit, which has been simplified in such areas as the housing, bearings, jackscrews, and

various azimuth components. In general, projected cost reductions are associated with

the introduction of automated assembly and material transfer; numerically controlled

machines; automatic electronic component insertion; and wave soldering.

The average unit cost for a 250,OOO/yr production rate shows a significant reduction

compared with its estimated value at the 25,000 heliostats/yr rate. This cost reduction

is supported by:

• Reduction of the material weights to their theoretical minimum for the mirror

backing structure and integrated drive unit structure.

• Elimination of the parts in the drive housing by assuming a welded flexspine/

housing assembly.

• Introduction of automation (industrial robots) capable of eliminating 90% of hand

labor positions.

• Mechanization of the foundation installation.

• Improved line flow, integration, and increased supplier control.

The average unit cost reduction for the 1 million/yr production rate is not as significant

as for the 250,000 units/yr scenario. It is assumed that further cost reductions will be

caused by continued improvement of the design, materials, and processes; further functional specialization of the production facilities; greater control over supply; and vertical

integration of the production of certain basic materials.

7.3.2 The General Eleetric Prototype Heliostat Design Production Scenario

This subsection briefly summarizes the facilities, equipment, tooling, manpower, new

materials, materials handling, quality control, and cost requirements needed to produce

2,500 heliostats as a one-time purchase, and steady-state production levels needed for

25,000, 250,000 and 1 million heliostats/yr. Detailed information on the manufacturing

design for this heliostat is in Ref. 34.

80




TR-459 S=~II.I -------------------------------

The 25,000 units/yr rate has been applied to develop a baseline production scenario for

the GE prototype heliostat design. The overall approach for developing a costing scenario is similar to that applied for the MDAC prototype design.

Tables 7-4, 7-5, and 7-6 illustrate the production volume progression on the average unit

cost while Fig. 7-16, 7-17, and 7-18 portray the relative changes in structural cost components of the GE heliostat based on production rates under consideration here.

7.3.2.1 Baseline Produetion Concept for 25,000 Heliostats/Yr

The facility concept f02the production of 25,000 heliostats/yr is shown in Fig. 7-19. The

plant occupies 3,700 m (39,812 ft2) and is operated on a 5-day, 2-shift basis using 43 direct labor personnel. The degree of automation is low.

The aver~ge unit cost for an annual production rate of 25,000 is estimated to be

$81.09/m reflectivity, a decrease in cost of nearly 55%. Cost improvements range from

40-65% over those of the pilot 2,500. Major improvements in the cost of the reflective

unit, and field assembly and checkout, resulted in decreases of 57% and 63%, respectively.

7.3.2.2 Production Concepts for 250,000 and 1,000,000 Heliostats/Yr

Tables 7-4 and 7-5 illustrate some effects of increased production rates, plant characteristics, and heliostat cost. As higher levels of production are reached, the degree of both

automation and vertlcal integration increases sharply. The manufacturing facility for

250,000 heliostats/yr is presented in Fig. 7-20.

Production of 250,000 heliostats/yr necessitates a highly automated facility, divided between two buildings to accommodate expansion. There are 151 direct labor personnel in

this plant, representing 1,656 heliostats per person, compared with 581 heliostats per

person at a 25,000/yr production output. At that level of production, an additional 279

people would be required if the automation level remained the same as for a 25,000

heliostat/yr production. However, automation could be greatly increased for 250,000

units/yr.

The 250,000/yr capacity of this facility would be limited only by the areas available for

die casting, final assembly, reflector fabrication, and ground cloth fabrication. Through

the addition of overtime and/or a limited third shift in these areas, the plant output

could be increased to 300,000 heliostats/yr without affecting other areas or operations.

For an increase in production from225,000 to 250,000 units/yr. the average unit cost decreased another 44%, to $45.39/m reflectivity, primarily because of automation. The

reflective unit and the heliostat support and protection subsystem showed the best cost

improvements, Le., decreases of 58% and 74%, respectively. The drive unit showed a

37% decrease. The remaining major elements showed cost improvement of 25% to 33%.

The GEl,000,000 units/yr production facility consists of multiples of the 250,000

heliostats/yr plant with an appropriate adjustment in film making capability. Two full

lines of film machinery would be required as well as additional materials handling

equipment.

85
TR-459 S=~I.' ------------------------ Vacuum metalizing of plastic film is now limited to 2.4-m (7.87-ft) widths based on

equipment size; the technology has no known limit. Therefore, vacuum metalizing

8.53-m (28-ft) widths is possible. Equipment based on the present technology (placing the

whole material roll and the take-up reel in the vacuum tank) is expensive. Technology is

now emerging that will eliminate the need for these reels to be in the tank, which will

greatly reduce the cost of such equipment. The manufacturing plan contains the vacuum

metalization of a one-piece reflector only at 1,000,000 units/yr, and contains vacuum

metalizing equipment to handle 2.l3-m (7-ft) material at 250,000/yr.

Cutting the reflector to shape can be readily accomplished by a numerically controlled,

multiheaded water knife or a tracer-controlled water jet which delivers a high pressure

stream of water .13 mm (.005 in.) wide. The cutting would be performed while the film

is held in place on a vacuum table and a chemically treated swab removes the aluminum

at the reflector center. The reflector would then be moved via belt conveyor to another

table where adhesive loadstraps would automatically be transported from their fabricating lines by overhead rubber suction devices and placed under the edges of the reflector. Pressure would be applied until the adhesive quickly cured; the reflector would be

rolled up on protected tynes and slipped into a compartment in the shipping container.

Cost improvements for increases in annual production, from 250,000 to 1,000,000 units,

were not as dramatic as in earlier increases. Average estimated unit cost dropped only

6%, to $42.88/m2 reflectivity. The most noteworthy cost improvements were in the control and instrumentation and the support and protection subsystems, with decreases of

12-15%. Cost decreases for other subsystems ranged between 2% and 6%. The small decrease indicates that automated manufacturing techniques may reach a maximum use

level, and demonstrates that heliostat costs have here nearly reached a plateau on the

learning curve, unless further technical advances are made.

Installation. The typical installation sequence is illustrated in Fig. 7-21. Several of the

initial steps in this process are performed by a device called the Automatic Foundation

Machine (AFM). The AFM performs the following functions:

• Accurately surveys and locates the center hole for mounting the heliostat;

• Trenches and buries the power distribution cable and lightning grid wire between

heliostat stations;

• Drills the heliostat support pipe foundation into the ground; and

• Trenches, inserts plastic, fills with excavated soil, and closes the ring bag enclosure foundation.

After these steps are completed, workmen place a cloth over the ground within the ring

bag, zip together the enclosure and foundation bag, inflate the enclosure, and assemble

the heliostat within it.

Weight Breakdown. Estimated weight for the various components of the prototype heliostat are the same as those shown in Table 6-3.

Cost Breakdown. Estimated costs for the prototype heliostat at production levels are

given in Table 7-5 [34].

86

TR-459 S=~I;_'-----------------

Table 1-5. PROJECTED COSTS FOR GE HELIOSTAT DESIGN (1978$)

Installed Equipment Operation and Heliostat Heliostat

Production Investment Costs Maintenanc;e Installed Co~t

Level (in thousands of dollars) Costs $/m'" Cost $/m -R

1,000,000 171,619 26.77 $1,363 42.88

250,000 64,254 27.24 $1,443 45.39

25,000 13,351 50.48 $2,597 81.09

2,500 1,264 79.73 $5,686 178.83

1.4 STEADY-sTATE PRODUCTION VOLUME AND COST VARIATION

Although the information presented in previous sections provides an accurate summary of

the design and baseline production scenario for each of the heliostat designs, it should be

recognized that it represents only one of many possible scenarios. This subsection demonstrates how changing market growth and location, competition, basic design, available

production technology and resources, and government and business policy affect the establishment of an effective, steady-state production process in cumulative production

along with the associated first-unit cost for the steady-state scenario. The manufacturing and installation of the MDAC prototype heliostat design is an example.

1.4.1 The MDAC Prototype Heliostat Design

The prototype heliostat study implied that the 125 thousandth unit is the cumulative volume point at which an effective, steady-state production process is established for the

baseline scenario. The rationale for this assumption is that certain electronic components will not be available until 1985, and a market will not develop that will support a

rate of 25,000/yr until 100,000 units have been produced. An additional plant startup of

one to two years or 25,000 units will stabilize production in the new plant. On the whole,

this is a conservative projection. It is possible that because of market or fiscal incentives or production alternatives, the facility could be installed well before 100,000 units

are reached. Also, many elements (especially buy items procured from the industrial

base) may reach a steady state well before the first facility goes into operation. On the

other hand, designs and production methods never fully stabilize and breakthroughs will

continue long after the first 100,000 units of production. These aspects may be illustrated through a brief overview of expected developments in heliostat design, production

methods, and cost from early test programs to the installation of the baseline 25,000

units/yr facility.

1.4.1.1 Production Scenario Progression

Changes in the production scenario are viewed in three parts: (1) test hardware to pilot

plant, (2) pilot production to demonstration and early commercialization, and (3) continuation to Nth unit of production.

88
TR-459 S=~I·;.;I -~ ~~~, ---------------------- _

Table 7-6. 10,000 HELIOSTATS/YR STEADY-STATE

PRELIMINARY COST BREAKDOWN

(1978 $)

Labor Material Dollars

Cost Element Dollars Parts Raw Material Total

Reflective Unit $ 129 $ 856 $ 103 $1,088

Drive Unit 233 1,088 284 1,605

Control 74 160 234

Foundations/Site Prep 532 187 112 831

Heliostat Support

Structure 32 1 132 165

Field Assembly and C/O 300 1 301

Total $1,300 $2,293 $ 631 $4,224

Adjustments:

Reflective Units (856) 856

Foundation (187) 187

Adjusted Total $1,300 $1,250 $1,674 $4,224

89






TR-459 S=~I.' ----------------------- Test Hardware to Pilot Plant. 'The National Science Foundation heliostat, the Subsystem

Research Experiment (SRE) hardware, and other future preproduction articles (Le., hardware constructed in small quantities in order to verify design/production feasibility) have

been or will be produced in a typical way for test hardware using soft tools (nonpermanent) and experiencing all the usual delays and first-time production problems. The nature of test hardware construction, if not trial and error, is at least one of a close, informal, and exacting association between engineers and manufacturing personnel.

Further, materials may not be delivered on time, they may be overprocured, or they may

come in totally "out of spec," and setup times and vendor tools must be allocated over

only a few units. Often, items must be reworked, causing additional setup or changes in

tooling. Finally, a great deal of time may be spent in working out specific methods for

actually accomplishing particular manufacturing operations. As a result, labor and overhead are typically 5-15 times the cost of material, and materials are 4 or 5 times what

they might cost for a pilot procurement.

The first major breakthrough in cost reduction will occur in the Barstow pilot plant heliostat production. A production design is available and the quantities allow hard tooling,

procurement discounts, scale economies, and standardized processes and balanced production flows typical of a working operation. It is difficult to project a true first-unit

cost for such a scenario for comparison with test-unit cost because material costs are

based on vendor quotes for entire quantities, while labor hours are based on industrial engineering standards for a working production line. However, a synthetic first-unit cost,

developed by extending unit pilot-plant costs on typical cost reduction curves, would be

only a quarter to a third the actual test article unit cost. Overall, labor and overhead

costs for pilot production will nearly equal materials costs and the average unit hours of

labor will be about 10% of the unit hours for the test articles.

Further cost reductions may occur in follow-on pilot production as a result of design-tocost analysis. However, since ever-changing designs tend to inhibit cost reduction, much

of the potential will be latent, with changes apparent only where costs are immediately

designed out (e.g., elimination of inversion or of the site assembly facilities). Otherwise,

the basic production facilities will change very little and each design variation will require essentially a restart of production for the affected assemblies.

Pilot Produetion to Demonstration and Early Commercialization. As a result of preproduction and pilot production programs, a relatively firm design that will be suitable for

commercial production is expected to evolve. The prototype heliostat is representative

of such a design, although several changes have already been examined since completion

of the study. This design allows considerable flexibility as to where and how the major

assemblies are fabrica ted and assembled.

The potential for strong but intermittent and uncertain growth in demand for any manufacturer's product may give rise to a conservative attitude toward expansion. A rational

expansion scenario suggests that modifying and expanding existing pilot facilities, in conjunction with added shifts and overboarding, and taking advantage of available capacity

in other facilities, are necessary. Continued extensive procurement from other industries will limit expansion pressures on resources, allowing much of the expansion to be industrially financed. As a result, many of the purchased parts will have reached an established production status at this point.

96
- ~ ~_.. =~' TR-459 -II II --------------------- _

- '~-~

Cost-effective changes in the plant will reflect labor learning and management

learning-about organizational requirements, production line flows, and so forth. As volume expands, the manufacturer will be in a better bargaining position with suppliers,

scale benefits will become apparent, and it may become necessary or cost-effective to

add or upgrade some equipment. Also, cost-effective design improvements that develop

or were planned as part of the prototype scenario but deferred until available, proven, or

volume justified may be incorporated on a part-by-part basis. In sum, although this is a

cautious, "make-do" period of expansion, a considerable cost reduction potential exists.

Early Commercialization to Nth Unit of Production. The primary issue during this period

is when and how the change-over to a large-scale, independent production facility should

occur. At least two possibilities exist. The first is to continually expand an existing facility with added and upgraded equipment until a steady 25,000/yr production rate is

achieved. This could cause some market share to be lost if cost reduction opportunities

are delayed or capacity 5 reached before the new facility is completed.

The other possibility is to make the move when less than half of the 25,000/yr rate is

reached. This could occur as early as the demonstration/early commercial period. As already indicated, the facility may be operated on a one-shift basis at reduced capacity

with deferred purchase of some equipment. The most likely candidates would be certain

au tomatic transfer equipment, or similar equipment (such as a bonding table).

AIthough the per-heliostat cost of the facility may double in the short run, the increment

may be small compared with the savings potential of the new facility. It is likely that

the increase would be more' than compensated by a reduction in average labor rate, reduced hand labor headcount (Le., "hands-on" workers producing hardware either directly

or through operation of equipment), reduced overhead, and increased productivity caused

by increased output per shift. As volume increases, the deferred equipment may be

added along with the second shift, contributing further to productivity and scale benefits. In addition, further procurement advantages will become apparent as suppliers recognize both increasing volume and the growing stature of the program.

7.4.2 The Point of Steady-State Production

The point of steady-state production may be defined as that point in cumulative production volume where the only design changes permitted are those that may modify a part

for more eff ective production or performance, are for the primary purpose of cost reduction, and will maintain a stable interface with other part concepts and designs. Similarly, the production process is a working, "debugged" operation representative of the

eventual rate production plan. Generally, changes will be logical extentions of or improvements upon existing processes, made only if the change permits a favorable rate of

return and involves an acceptable risk.

This definition does not exclude changes, but it is clear that research experiments, preproduction, and even pilot heliostat production facilities do not qualify because of their

uncertainties. It is equally clear that the prototype heliostat baseline production scenario would qualify because it is a steady-state scenario by definition. As conceived, this

scenario provides for the production and installation of 25,000 heliostats/yr, but by operating on a one-shift basis, somewhat reducing line speed, and cutting the number of field

crews in half, identical facilities and equipment may be operated with a minimal, acceptable economic penalty at a rate of 10,000 heliostats/yr, or less. Thus, the prototype

97
TR-459 S=~I_' ------------------------------- baseline scenario, with minor modification, also may be used to describe a steady-state

production situation that could occur at a fairly early stage of cumulative production.

Accepting the modified scenario, the issue becomes a matter of determining how early

an aggressive and dominant producer would plan initial operations in a new facility

capable of producing approximately 10,000 heliostats/yr. In other words, at what point

may the new production plant's initial operating capability (IOC) be aligned with the producer's cumulative sales volume expectations? Prior to this point in a producer's cumulative volume, it may be assumed that the production scenario is in an unstable state

where designs and production plans are still subject to important changes. It follows that

the determination of this point should provide the earliest stage in cumulative production

volume, where it would be feasible to initiate operations under the defined steady-state

production scenario.

Relative to a market projection, this point of interest may be further defined as the earliest point in cumulative demand for a dominant producer's product, after which further

demand is able to absorb all the producer's future output at or greater than the steadystate production rate. A review of demand growth projections reveals the following production requirements:

• By the end of 1982, all suppliers will have produced 10,000 heliostats.

• During 1983 and 1984, an additional 10,000 heliostats will be supplied each year.

• In 1985, all suppliers must produce an additional 20-25,000 units in order to meet

demand projections.

• During 1986, 1987, and 1988, demand will approach 100,000, 150,000, and 300,000

heliostats, respectively.

• Demand will continue to expand from 1988 through the turn of the century.

Accepting this market projection, an aggressive supplier desiring 40-50% of the market

could plan its new facility IOC in 1985, in time to produce its 15,001st heliostat in

accordance with the modified baseline scenario. This corresponds to the electronics

technology readiness date, and all other part configuration and production processes described as part of the prototype heliostat baseline production scenario. Assuming a 12-

month startup, during which 5,000 heliostats are produced, the actual steady-state status

will be achieved when the supplier has produced a cumulative total of 20,000 heliostats15,000 of other designs in other facilities and 5,000 of the prototype heliostat design.

Whether or not this occurs in the manner described depends on how the individual firm

actually views the strength of the market and associated risks. Of course, the strength

of the market depends on the quoted price of heliostats relative to alternative energy

costs, and the extent of incentives for both suppliers and users. These variables are still

unknown. However, if successful cost reduction results in preproduction, pilot, and demonstration programs, by 1983 a manufacturer should be able to demonstrate to its own

satisfaction that a move into baseline facilities will permit heliostat price quotes that

are quite competitive with alternate energy costs. As a result, an effective, steadystate production status for an aggressive, dominant supplier at the 20,000th unit is not an

unreasonable projection under the circumstances.

98
TR-459 S=~I i'.1 ----------------------------

7.4.2.1 First-1Jnit ()osts

First-unit cost may be defined in a number of ways, depending on the perspective of the

analyst. In a development firm, it may be the cost of the first test article with or without a nonrecurr-ing engineering allocation, or a synthetic value extended from industrial

engineering standards that have been determined at, say, the 100th unit of production.

A materials buyer for a new product may see the first-unit cost of a procured item as the

average cost of the first lot in the door. The seller of the same item may see the price

charged as that which the market will bear in order to recover a margin plus the average

cost to produce the item during the current year. The true first-unit cost for the item

may be some unknown amount that occurred many years earlier. In a steady-state case

such as this, the analyst is likely to consider the first unit cost as the sum of the cost per

unit at the steady-state production rate plus an allocation of the incremental startup

costs that may be associated with introduction of a new model, introduction of new production equipment, or the initiation of a new production facility.

A marketer may be interested in developing a synthetic first-unit cost for use as a reference in formulating downline pricing strategy. For this purpose, a product's current cost

experience must be aligned with a unit of cumulative sales volume, as suggested in the

previous subsection, and the cost extended back on a historically representative cost reduction slope to arrive at the synthetic first-unit cost. The resulting cost is synthetic

because it represents a situation that usually is only hypothetically implied by current

production characteristics and the slope of the applied cost reduction curve. The relationships between these definitions for the modified scenario will_become more apparent

when the steady-state cost is quantified.

7.4.2.2 Development of Steady-State Unit Costs

The steady-state unit cost for the modified prototype heliostat scenario is developed

using two different methods. Both methods adjust the detail costing results presented in

the prototype heliostat study final repcrt for the 25,000-unit production rate. The first

method uses an aggregate resource impact approach, while the second employs cost reduction curves applied to each detail line item. Both approaches arrive essentially at the

same total price at unit 20,000. Labor and overhead, however, are somewhat higher in

cost for the resource impact approach.

The results of the first method are shown in the steady-state cost column of Table 7-7.

They address the four basic cost categories: factory labor, raw materials, purchased

parts, and field labor. The concept is to develop a logical adjustment reflecting the

changes in the scenario that permit reduced annual output. Since facilities and equipment remain the same, factory and field labor per unit stay essentially the same since

total labor would be cut in half in going from two shifts to one. However, to produce

10,000 units/yr, line speed must be reduced 20%, which is assumed to increase hand labor

25%. As a result of reduced use of the plant, the scale effect must be considered so that

overhead is assumed to be 50% higher. Capital cost per unit will be somewhat more than

double, while fringe benefits and a large share of other overhead items will vary more directly with labor hours per unit. Direct support-including items such as sustaining

tooling, industrial engineering, quality assurance, and manufacturing engineering-are

also assumed to increase 50% for reasons of scale.

99
S-~I. TR-459 - :11:

- ~~:."

Table 7-7. STEADY-sTATE COSTS

Costs (1978$)

Steady-State Synthetic Effective

Element CRC Costs at 20,000 1st Unit 1st at 500

Factory Labor 89% $ 468 $2,973 $ 870

Ra w Material 98% 1,674 2,301 1,864

Purchased

Parts 92% 1,250 4,667 1,948

Field Labor 98% 832 1,152 933

Total 94% $4,224 $11,103 $5,615

100
TR-459 S=~I 'II!I-------------------------------

These adjustm ents have been applied to the labor and overhead costs at the prototype

heliostat's resource loaded peg point-the 125,000th unit. Costs were adjusted down a

learning curve from this point to the 350,000th unit, and are thus slightly lower than

costs used for the adjustments described previously. Use of the costs at the peg point

provides a more direct connection to the actual manloads developed for the prototype

heliostat report.

Materials costs were adjusted to include discounts. These costs were quoted by suppliers

at rates of 25,000 units/yr over several years and reflect suppliers' discounts for fairly

large quantities. At 4096 of these quantities, the discounts will be less. The changes in

discount are assumed as follows:

% Discount

10,000 25,000

Price

Impact

Raw Materials

Installati on

15%

26%

25%

40%

1.13

1.23

The discount factors are based on cost analysis judgment, and the price impact shows the

increase applied to materials prices. For this analysis, all reflector unit and foundation

rnaterials costs were treated as raw rna terials, although the prototype heliostat final report classified some of these elements (such as the sheet steel beams) as purchased parts.

The cost-reduction curve method extends the costs shown in the prototype heliostat report from the 350,000th unit (i.e., the midpoint unit in the 25,OOOth unit/yr scenario)

back to the 20,OOOth unit. The same curves have been used that were employed to extend the midpoint cost of the 25,OOO/yr baseline out to the midpoint cost projected for

the 250,000 unit/yr scenario. If labor increases approximately 10% a cost breakout is

provided that ties in with results determined using the resource impact approach.

Table 7-7 also shows the results of adjusting the steady-sta te cost column, using the cost

reduction curves shown, to reflect a synthetic first-unit cost and the effective cost at

plant startup. The latter is calculated as the synthetic cost of the five hundredth unit,

and is based on the determination that 500 units of equivalent experience are applicable

to a calculation of plant startup costs.

Figure 7-22 shows these costs plotted in a log-log relationship. Translating a steadystate cost to an actual point on a cost-reduction curve can cause controversy. It is clear

that the cost should not be plotted as a first-unit cost, if only because the second unit

would be expected to be significantly less costly, while the average change in cost between units in steady-state production will be very small. Similarly, it is probably not

preferable to plot the cost at even several thousand units further out in cumulative volume. A point is finally reached, however, where such empirical arguments fade, so that

the alignm ent of the steady-state cost and a point on a cost-reduction curve finally must

be based on a feel for circumstances, some logic, and no small amount of judgment.

The steady-state cost is plotted at the 20,000th unit reflecting the attitude that the

steady-state cost actually represents costs associated with one heliostat's worth of resources and production time where the employment of resources is exactly as described,

and the 20,000th unit is the earliest probable point where this situation exists exactly as

defined. Prior to this point, the producer will not have deployed and utilized this set of

resources as defined because of market conditions, technological conditions, and startup

problems. After the steady-state is reached, as defined, the resources and the manner of

101

TR-459 =~-~ '[W;,

~, ------------------------------~

their employment will immediately start to change in response to market growth,

innovation in product, process, equipment and manufacturing flows, influence on

suppliers, and similar cost reduction issues. Obviously, there is a margin of expectation

in this analysis, so that the projection must be carefully considered.

The synthetic first-unit cost, as defined earlier, is also plotted along with the effective

first cost after plant startup using the data shown in Table 7-7. The plant startup cost is

plotted at unit 15,000 and shows a rapid drop to the steady-state cost as prior heliostat

experience is used and production schedules and plans are brought to rate production

definition. These points are presented more to illustrate the concepts than as basic parameters since they will vary around the steady-state projection, depending on the way a

firm conducts its business and responds to market conditions.

The cost reported in the prototype heliostat study for the first 2,500 units is also plotted

for purposes of comparison. This cost reflects the prototype heliostat design but not the

steady-state production scenario. These heliostats are assumed produced in a pilotplant-like production facility geared to a lower rate of production. The projected cost is

approximately 75% greater than the unit cost curve at 2,500 units, which is about what is

expected for this scenario since labor and overhead rates nearly double, production

equipment is much less sophisticated, procurement is apparent for only 2,500 units rather

than 10,000 units/yr, and many production uncertainties still exist.

These projections have been developed using relatively general cost analysis methods

that could be accomplished within the scope of the study. A more complete analysis

would obtain vendor quotes for each item of material at the production rates involved.

Also, the impact on overhead and direct support labor would be examined in greater

depth, and hand labor would be reviewed in detail in order to carefully screen the impact

of line speed and double- to single-shift adjustments. Although a fair amount of revision

of the detail costs might be necessary if a more comprehensive analysis were needed, aggregate costs are probably within a reasonable range of accuracy. Even so, the main intention of this analysis has been to provide sufficient credible data so that the expectations presented in this report may be adjusted to fit any variation in production circumstances that might be considered. Above all, it should be recognized that the market and

production scenarios, logic, and costs that have been presented here represent only one

of many ever-changing alternatives to the design, production, and marketing of

heliostats.

103
104
TR-459 S=~I _~ ![.~i ~~ ------------------------------

SEC110N 8.0

DEVELOPMENT OF LEARNING AND COMPOSITE CURVES FOR

COSTS ASSOCIATED WITH HELIOSTAT PRODUC110N*

The development of learning and composite curves, outlined in Sec. 3.0, can help predict

overall cost reduction in manufacturing the MDAC and GE prototype heliostat designs.

The objective here is to project future costs for these designs using surrogate cost history and the learning curve conceptual and analytical framework. First, selection of surroga tes will be discussed, followed by estimations of the learning and cost curves for surrogate products, and applications to heliostat assembly.

8.1 SELECTION OF SURROGATES

Each of the two heliostat designs has been broken down into its components for purposes

of cost analysis by both McDonnell Douglas Company and General Electric. The components are designated by WBS name and number as shown in Table 8-1.

Not all of the components and subassemblies are applicable to both contractors' designs.

Surrogates for cost analysis have been chosen for selected components on the basis of the

following criteria:

• The relative importance of the component to the contractor's design; - .

• The relative contribution of the component to the total cost of the design; and

• The likelihood of availability of a meaningful surrogate having a body of historical data.

The initial choice of components and surrogates to be analyzed and the design to which

they are applicable is given in Table 8-2. As the study proceeded through the data collection phase, it became evident that other surrogate selections should be considered in

addition to or instead of those originally specified. An expanded listing of surrogates

used for this study is shown in Table 8-3.

The surrogates that were finally used for this study were chosen because manufacturing

operations between the surrogate and the heliostat components were close and reliable

cost data were available. In several instances, the first surrogate chosen had to be

changed to another because of a lack of historical cost data. In other instances, as the

study progressed, more appropriate surrogate parts with cost data became available. The

changes made to the list of surrogates can be seen by comparing the surrogate selections

in Tables 8-2 and 8-3.

*This section summarizes material presented in the report, "Development of Surrogate

Experience Curves for Costs Associated with the Production of Heliostats," prepared by

Arthur D. Little, Inc., for SERI.

105
TR-459 S=~II*'-----------------

Table 8-1. HELIOSTAT DESIGN COMPONENTS

WBS Number Part or Subassembly Name

4410

4411

4412

4413

4420

4421

4422

4423

4424

4425

4426

4430

4431

4432

4433

4440

4441

4442

4450

4451

4452

4453

4460

4461

4462

4463

4464

4465

4466

Reflective Unit

Reflective Surface

Mirror Back Structure

Assembly and Bonding

Drive Unit

Azimuth

Elevation

Motors

Position Indicators

Power Distribution

Assembly of Drive and Pedestal

Control and Instrumentation

Sensor

Field Control Electronics

Control Signal Equipment

Foundation and Site Preparation

Foundation

Site Preparation _

Heliostat Support and Protection

Heliostat Support Structure

Heliostat Protective Enclosure

Lightning Protection

Field Assembly and Checkout

Heliostat

Sensor Calibration Equipment

Electrical Power Distribution

Alignment of Heliostats

Field Support

Packaging and Transportation

106
TR-459

S=~I.I----------------------

Table 8-2. CHOICE OF SURROGATES FOR HELIOSTAT COMPONENTS

Design

Heliostat Component Surrogates Applicability

l. Reflectors Laminated Mirrors McDonnell Douglas and

Plywood Production General Electric

2. Elevation Actuator Machine Ball Screws McDonnell Douglas and

General Electric

3. Drive Controls Solid State Power McDonnell Douglas and

Controls and Condition- General Electric

ing

4. Pedestal Corrugated Tube McDonnell Douglas and

Welded TUbing General Electric

5. Reflector Supports Conduit Raceway McDonnell Douglas

Door Mullions

6. Azimuth Gear Box Automative Gear Boxes McDonnell Douglas

Variable Pitch Prop.

Boxes

7. Azimuth Drive Hub Surveyors Wheels General Electric

Bus/Truck Steering

Wheels

Complex Precision

Plastic Mold

8. Reflector Yoke Automobile Exhaust Tube McDonnell Douglas

and General Electric

9. Reflector Enclosure Swimrning Pool Liners General Electric

Weather Balloons

Blow Molded Bottles

107
TR-459 S=~I.I ------------------------ Table 8-3. SURROGATES USED IN THE STUDY

Design

Statusa Component Part Surrogate Applicability

1. Reflectors A. Plywood McDonnell +

1. Total Douglas and

2. Southern (New General

Industry) Electric

3. Western (Old

Industry)

4. Plywood and

Veneer

5. Plywood

Manufacturer

B. Aircraft Windshield +

C. Laminated Mirrors +

2. Elevation

Actuator A. Aircraft Landing Gear McDonnell +

Douglas and

General

Electric

B. Machine Ball Screws

3. Drive Controls (Single Chip Microprocessor)

A. Personal Electronic McDonnell +

Calculators Douglas and

B. Solid State Caleula- General

tors Electric

4. Pedestal A. Steel Pipes and Tubes McDonnell +

B. Corrugated Tube Douglas and

C. Welded Tubing General

Electric

5. Reflector

Supports A. Steel Wall Studs McDonnell +

B. Metal Door Sash and Douglas +

Trim

C. Metal Household +

Furniture

D. Conduit Raceway

E. Door Mullions

6. Azimuth Gear

Box A. Drive Wheel Trans- McDonnell +

mission Douglas

B. Automotive Gear Boxes

C. Variable Pitch Prop.

a+Data have been obtained; -Data cannot be obtained.

108
TR-459 S=~II,.1 -------....,-----------------------

Table 8-3. SURROGATES USED (concluded)

Design

Component Part Surrogate Applicability Statusa

7. Azimuth Drive

Hub A. Steering Wheel Bus, General Electric

Truck

B. Harmonic Drive Wheel

C. Surveyors Wheels

D. Complex Precision

Plastic Mold

8. Reflector Yoke A. Automobile Exhaust McDonnell

Tubing Douglas

B. Metal Household and

Furniture General

C. Steel Pipes and Tubes Electric +

D. Fabricated Pipes and +

Fittings

9. Reflector

Enclosure A. Swimming Pool Enclo- General

sure Electric

B. Aircraft Canopy +

c. Blow Molded Bottles

D. Weather Balloons

E. Helicopter Blade +

Liners

F. Inflatable Shelters +

10. Assembly A. Truck/Bus Bodies McDonnell +

B. Wing Structure Douglas +

and General

Electric

11. Overall Product A. Motor Vehicles McDonnell +

B. Radio Telescope Douglas

and General

Electric

a+Da ta have been obtained; -Data cannot be obtained.

109
TR-459 S=~II.! -------------------

All automotive-related surrogates (e.g., exhaust tubes, differentials, steering wheels) had

to be discarded because of the automotive industry did not release enough cost information. Despite several efforts, including those of a leading automotive industry consultant, no more definitive data than selling prices of automotive parts could be obtained.

During the course of the study, an exceptionally appropriate surrogate for the entire

heliostat assembly was discovered in work sponsored by the U. S. Air Force at Hanscom

Field, Mass, in 1962 [40]. The project involved the construction of a radio telescope design to function in the same manner as the heliostats and tower. A multiplicity of individually oriented flat screen antennae were used to reflect radio waves emanating from a

distant source. to a central receiver mounted in the tower. The plan was to use several

thousand antennae in an engineering installation. Unfortunately, only about 500 were

built and no mass production techniques were used. However, there were several design

cycles for cost improvement that were completed within the course of the program.

The specific surrogates that were investigated for this study (see Table 8-3) have varying

degrees of validity for heliostat component counterparts and varying levels of cost data

quality. Surrogate validity and data fidelity cannot be measured quantitatively. Some

inferences regarding the quality of the data, however can be drawn statistically. A

qualitative judgment can be made regarding the validity of the surrogate as an analogue.

The use of aircraft windshields and laminated mirrors for surrogates of heliostat reflectors represents a very close match of materials and manufacturing processes. The use of

plywood as an analogue of the reflector is less directly related in terms of material but

similar in terms of manufacturing processes. Plywood manufacturing requires the

bonding of friable, delicate laminates into a continuous, multilayered structure. Although plywood manufacturing is an old, well established industry, there are segments

(such as the southern plywood industry) that are relatively new. Learning effects would

probably be more prevalent in such new industries.

The elevation actuator is an electromechanical positioning system. While the machine

ball-screw mechanism is a very close surrogate, no cost data were available for it. The

aircraft landing gear is a similar electrohydraulic mechanical system which requires fine

mechanical design, careful machining and assembly, and a high level of reliability. Although the landing gear surrogate is probably more complex than the elevation actuator,

the configuration, materials, and manufacturing operations are probably quite similar to

the elevation actuator component.

The drive controls and their costs are dominated by the microprocessor or control component. The other components of the control system are necessary to the design but are

generally purchased elsewhere and assembled. The electronic calculator is a reasonable

surrogate in that its cost is also dominated by a single chip microprocessor, despite a

need for other electronic and structural components.

The pedestal is a difficult component for which to find surrogates with meaningful historical cost data. Steel pipes and tubes (welded or seamless) are manufactured using an

old technology. Early cost data are virtually lost, and recent data would exhibit little (if

any) learning effect. We have nevertheless included these because of the dearth of information related to any other potential surrogates. Furthermore, the fact that manufacturing of tubular components is a well developed technology indicates that if manufacturers skilled in the art are used, the learning effect in manufacturing these components

would be minimal.

110
TR-459 5=~1 - 'I.~i .;------------------------ Several surrogates are available for the reflector supports. The least realistic are the

fabricated pipes and fittings. However, the rolled steel sheet shapes used in steel wall

studs and metal door sashes are completely analogous to the steel channel configuration

of the reflector supports. These surrogate industries are well developed and mature.

Learning effects, however, are probably not evident. Of course, there may also be little

learning in fabricating as simple a structure as the reflector supports.

The surrogates for the azimuth gear box were chosen because they represent medium

precision gear trains that were produced in medium volumes of tens of thousands per

year. The only choice including manufacturing cost data involved the transaxle transmission of an off-the-road vehicle such as a tractor. The data is given for a completely new

design, from its inception four years ago to the present. The results indicate that, while

learning occurs, the design and fabrication of transmissions by people skilled in the art

will avoid most of the early costs associated with new designs.

Data for the azimuth drive hub was not available for any of the selected surrogates.

Manufacturers of automotive steering wheels and harmonic drives would not release cost

data. Other sources such as industry associations and plastic parts suppliers also proved

to be of no help. For this study, it was conservatively assumed that the current manufactured estimated cost by General Electric would not change much.

The surrogates for the reflector yoke resulted in much the same data as the pedestal surrogates. No data were directly available from manufacturers or manufacturers' associations. The data available at this time are weak. However, because of the nature of

these parts, significant learning should not be expected.

Several surrogates yielded good quality data for the reflector enclosure. The aircraft

canopy, helicopter blade liners, and inflatable shelter provided valid cost data. The helicopter blade liners and inflatable shelter are closer in manufacturing process and rnaterial to the processes used for the reflector enclosure.

Originally, no surrogate was selected to represent the assembly operation. Essentially,

the heliostat is a mechanical configuration requiring assembly of structural components. The aircraft wing structure assembly operation is an excellent analogue for which

valid cost data are available from the manufacturer. The learning effect is typical of

what may be anticipated for the assembly of the heliostat units.

For the overall product no reliable surrogate data were available. As previously discussed, the radio telescope antenna assembly represents the penultimate surrogate for a

heliostat, Unfortunately, none were ever mass-produced.

8.2 DERIVATION OF LEARNING AND EXPERIENCE CURVES FOR SURROGATES

8.2.1 Data Acquisition

As noted previously, surrogate products were identified and selected under the assumption that historical (relative) cost pa ttems exhibited by these products could apply to

corresponding heliostat components as well. Furthermore, in order to be applicable to

the heliostat assembly, the relative cost behavior for each surrogate must be independent

of ancillary conditions such as:

III
TR-459 S=~I.i- --------- • years of production,

• annual production rates, and changes in ra tes,

• absolute unit costs of surrogates,

• economic factors,

• production volume, lot sizes, and sequence numbers used, and

• extraneous factors that might distort the constant rate of reduction in unit cost.

Thus, it is assumed that cost reduction (or increase) is constant over time, and can be estimated from the observed slope parameter (b) for the surrogate products selected. If

the raw cost/quantity data have been influenced in any way to distort the "true" slope in

a substantive manner, these effects must be estimated and data adjustments made, or

assumed negligible. An excellent example of this type of problem is illustrated at the

conclusion of this section.

After an acceptable list of surrogates was constructed, the next step was to acquire

meaningful production cost data. The original intent was to gather historical cost data

for each surrogate product, broken down according to labor, materials, and overhead.

Furthermore, sources of cost reduction and extraneous factors were to be determined in

the manufacturing of surrogate products.

The data acquisition task proved to be a formidable one, so cost data presented in this

section are of limited quality. For the surrogate approach to be credible, it is imperative

that accurate and reliable cost data be used, and that the manufacturing history of each

component be well understood. In view of unexpected and unavoidable difficulties encountered in acquiring reliable cost data for many surrogate products, it is recommended

that the derived learning percentages be accepted with caution, and that absolute values

and ranges be considered illustrative rather than definitive.

The data acquired for this study were provided by Arthur D. Little, Inc., and are summarized in the tables presented in Appendix A. Data sources are identified except when

confidentiality was requested. Some comments concerning the data contained in those

20 tables are listed below.

• All dollar amounts given were normalized within each table, to account for inflation, and are internally consistent for each surrogate.

• Since cumulative production quantities are required, only sequential production

volumes are included; for example, plot points for the data in the first table are

calculated from the true cumulative production figures since 1960.

• In cases where production quantities were not available, plot points were calculated from normalized material costs. This technique assumes that observed

differences in deflated materials costs reflect production level changes only and

that materials unit costs remain constant.

• Prior cost/quantity history is ignored. Data are assumed to reflect "normal"

learning even though mature industries such as motor vehicles might have behaved quite differently at the outset.

• Tabulated data given in this report have been summarized; more detailed raw

data were extracted from most sources indicated.

• In all cases, total cost includes direct labor, materials, and overhead.

112
TR-459 S=~I :111 -------------------

8.2.2 Surrogate Learning Curve Estimation

After all relevant data had been accumulated, unit costs and corresponding cumulative

production quantities were calculated according to the scheme presented in Appendix B.

In most cases, the average cost of producing the nth unit was considered; however, a few

cumulative cost curves were also generated primarily for comparative purposes. Cost/

quantity data pairs were subsequently coded for computer processing. A least-squares

linear fit (on log-log scale) was then performed using the BMDP Biomedical Computer

Program subroutine BMDPIR, Multiple Linear Regression. The complete output of this

program is illustrated in Appendix B.

The output presents several summary statistics of interest, including the followingr

• The estimated least-squares line; i.e., the intercept parameters (log a) and the

slope (b);

• The standard error of the regression coefficient estimate (b); under certain

assumptions, this term can be used to ealoula te confidence interval estimates for

the slope;

• R (the sample correlation coefficient) and R2 (which measures the proportion of

variation in log cost explained by the linear model);

• The standard error of estimate, which measures the dispersion of the observations about the estimated regression line; and

• The probability that a test statistic calculated from the data (the F-ratio) would

be as large as observed if, in fact, the two variables were-not linearly related; a

small probability implies rejection of the hypothesis of no linear relationship.

A plot of observed log cost (0) and predicted log cost from the least-squares equation (P)

is also given in log-log units. The plot of residuals (observed minus predicted log costs) is

also given for purposes of validating an important underlying assumption of regression

theory. This assumption refers to the independence of observed C values; that is, in regression theory it is assumed that successive observations are independent of one another. This assumption is clearly violated in cumulative cost curves, and is difficult to

justify in unit cost data as well. Nevertheless, probability statements in the form of 95%

confidence interval estimates are presented in Appendix A, Table A-21, which summarizes all regression runs performed. Therefore, if regression assumptions are accepted,

the probability is 0.95 that the learning rate interval given includes the "true" (unknown)

learning rate that we are attempting to estimate from these data.

The effect of variability in slope estimates, as well as overall precision in the estimation

techniques, is demonstrated in Fig. B-3 (see Appendix B) using the Aircraft Canopy surrogate data. Three lines are drawn to indicate upper (80%) and lower (58%) bounds, and

the regression estimate itself (68%), since these estimators are subsequently applied in

this study to actual heliostat components.

In the usual application of learning curve theory, this regression line would be used to

predict the unit cost of producing additional canopies. Two different, and often misunderstood, interval estimates are also illustrated in Fig. B-3 for this situation. The narrower band depicts a 95% confidence interval estimate about a conditional mean cost

value; the wider band represents the more commonly required prediction interval concern

with the estimation of only one individual unit cost. For example, it can be stated with

95% confidence that, on the average, the cost of producing the 200th canopy would be

113
TR-459 S=~II.' --------------------

between 4.7 and 13 hours; however, the data also indicate that the predicted cost of the

200th canopy produced in this sequence can be expected to be between 2.9 and 22 hours,

a much broader, less precise estimate. In addition to displaying this distinction between

confidence and prediction intervals, this figure is intended to illustrate the following:

• Variation in slope can have dramatic effects on unit cost estimates;

• An apparent "reasonable good" linear fit (R2 =0.88) does not necessarily yield estimates or predictions of acceptable (i.e., useful) precision; and

• Regression theory, if appropriate at all, works best near the mean of the data;

extrapolating beyond the observed data is risky and subject to large estimation

errors.

There are additional limitations to applying historical learning trends to different, but

related, components. For example, the following concerns are worth noting:

• Learning curve slope estimates are highly dependent on the particular surrogate

selected; different surrogates intended to characterize the same heliostat component can yield vastly different estimates of learning effects.

• Identical surrogates, such as plywood, lead to widely varying estimates depending

on the data source, age of industry, and other variables, such as geographical 10-

cation.

• There is evidence that learning dampens out for mature industries and, therefore,

may not represent expected patterns for similar components with different system configurations and/or requirements. -

• Estimated slopes appear to depend on units of measure, even in situations that

should be invariant; e.g., see Appendix A data on number of workers (b = -0.59),

hours worked (b = -0.33), and standardized labor cost (b = -0.65).

• Many of the surrogates used in this study indicated little or no measurable

learning. This condition manifested itself in two ways: 0) the data did not

appear to be linearly related as measured by standard goodness-of-fit test statistics, and/or (2) unit costs were constant, and apparently unrelated to quantity

produced over the range of production units considered.

• Estimates for heliostat components are highly subjective, even if the surrogate

cost/quantity data were reliable in all respects.

• Historical "raw" cost data can be, and frequently are, contaminated with hidden

effects that only extraordinary and detailed investigation will uncover. Examples of this type of unusual cost/quantity behavior are given in Appendix A, in

two sets of cost data. In Tables A-16 and A-19, one column, denoted as "actual"

unit cost, represents costs incurred to produce the corresponding lot sizes. The

other cost column, entitled "adjusted" unit cost, reflects the results of a comprehensive study conducted by Arthur D. Little, Inc., which yielded substantive evidence that "actual" costs were inflated and unusually high for early production

units because of circumstances unique to a particular production program. Such

uniqueness is not uncommon in the literature.

114
TR-459 S=~II.I --------------------------:-------

8.3 APPIlCATION TO HEUOSTAT ASSEMBLY

8.3.1 Unit Cost Estimates for Relevant Components

The prototype heliostat investment cost estimates for various annual production levels

for MDAC and GE design configurations are presented in Subsection 7.3. Tables 8-4 (GE)

and 8-5 (MDAC), provide the average investment cost per heliostat estimates for the

baseline plant (25,000 units/yr), For the MDAC Prototype Heliostat Study (3) it was

assumed that plants will initiate operations after 100,000 heliostats have been produced

for pilot plants, demonstration plants, and the first commercial plants. However, the average investment cost per heliostat has been projected as that required at the start of

the second year of rate production in the factory, or at unit 125,000 [3, pp, 9-21].

The 25,000 units/yr scenario, representing the plans associated with a well conceived, advanced design, has been developed as the baseline scenario. This rate represents a significant high-volume production situation and reflects conditions required to establish a

steady-state production process. Also, it may be easily translated into both higher or

lower productive capacity. Thus, this scenario lends visibility to the development of viable business constraints and at the same time supports astrategy for capturing an

important share of eventual markets. As indicated before, the 25,000 heliostats/yr production rate represents only one of many possible scenarios.

8.3.2 Development of Heliostat Component Cost Curves

The procedures for using learning and cost curves to predict cost -reduction in manufacturing solar technologies outlined previously show that learning and cost curves were to

have been derived and aggregated over labor, materials, and overhead cost elements for

each heliostat part. Since the data collected are not amenable to treatment at this level

of detail, the conceptual framework will be illustrated with total cost data only.

Learning curve slope estimates observed for the surrogates were superimposed on the

single point estimate assigned to the 25,000th unit. Again, the criterion for selecting a

representative slope estimation from among two or more alternatives was essentially

subjective. In each case, upper and lower bounds were also prescribed to emphasize the

importance of using interval estimation; these bounds, however, are not purported to be

confidence limits, and probability statements do not apply. The technique is illustrated

for the GE design and the MDAC design in Appendix C.

8.3.3 Aggregation of Costs

A composite cost curve was then constructed for each heliostat design configuration by

aggregating costs at similar production quantity levels over the 11 individual component

parts. To illustrate the aggregation process, boundary value cost curves are generated by

summing high and low costs, respectively, in addition to the "expected" value determined

from the "most likely" learning slope. As before, these boundary values have no statistical (i.e., probabilistic) meaning, and are included for illustrative purposes only. The

aggregate cost curves are given in Fig. 8-1 and 8-2 for the GE and MDAC designs, respectively. As these curves indicate, the conceptual estimation technique examined in

this study would almost certainly yield a nonlinear cost/quantity relationship for the

overall assembly since slopes are not additive over components in a log-log scale.

115
TR-459 S=~I.J -------------------------

Table 8-4. COST ESTIMATE-25,OOOth UNIT, GENERAL ELECTRIC DESIGN

Corresponding Unit Cost

Surrogate WBS Number Component Estimate

4400 Collector Equipment $2,174.57

1 4410 Reflective Unit 270.74

4420 Drive Unit 262.62

7 4421 Azimuth Drive 29.00

2 4422 Elevation Drive 9.49

4423 Motors 154.13

4424 Position Indicators

4425 Power Distribution 70.00

3 4430 Control and Instrumentation 194.94

4431 Sensor 68.75

4432 Field Control Electronics 110.56

4433 Control Signal Dist, Equip. 15.63

4440 Foundation and Site 398.91

4450 Heliostat Support and Protection 826.16

4 4451 Heliostat Support Structure 109.42

9 4452 Heliostat Protective Encl. 706.10

4453 Lightning Protection 10.64

10 4460 Field Assembly and Checkout 149.12

4470 Design and Engineering 0

Other (1) Manufacturing Facility 23.83

(2) Packaging and Transportation 48.23

4800 Distributables and Indirect

Costs 422.22

4810 Construction Facilities,

Services G&A, and Fee 152.86

4820 Spare Parts 14.86

4830 Architectural Engineering

Service 12.92

4849 Construction Management 4.07

4850 Plant Startup and Checkout 1.44

4860 Contingency 239.07

$2,596.79

116
Table 8-5. COST ESTIMATE-25,OOOth UNIT, McDONNELL DOUGLAS DESIGN

Capital Investment III

Mati. Dollars III

--U Corresponding

Surrogate WBS Number Component Hours

Labor

$ $ $

Pur. Pt. Raw MH. Unit

Estimate

Cost -,fill

'.: ~~'

Grand Total-Heliostat 39.3 748. 1923. 570. 3,241.

4410 Reflective Unit 4.3 60. 735. 88. 883.

I 4411 Reflective Surface 1.3 18. 407. 46. 471.

5 4412 Mirror Back Struct 1.5 22. 328. 13. 363.

4413 Assy and Bond 1.5 21. o. 28. 49.

4420 Drive Unit 7.8 110. 937. 260. 1306.

6 4421 Azimuth 4.0 56. 141. 156. 354.

2 4422 Elevation 2.5 36. 482. 103. 621.

4423 Motor Total 0.0 o. 164. o. 164.

4424 Pos/Limit Indicate •8 II. 17. o. 29•

4425 PWR SPLY Dist. 0.0 o. 131. o. 131.

4426 Assy Dr./Ped./Elect •5 7. I. o. 8•

3 4430 Control/Instrum. Eq. 2.4 36. 78. o. 114.

4431 Sensor/Calib.Bquip, 0.0 o. I. o. 1. ..... 4432 Field Control 0.0 o. I. o. 1. -a 4433 Cntrl./Sig. Eq. 2.4 33. 68. o. 102.

44320101 Collector Control .1 2. 8. o. 9.

4440 Found/Site Prep 14.5 336. 172. 102. 609.

4441 Foundation 10.5 243. 172. 102. 517.

4442 Site Preparation 4.0 93. o. o. 93.

4450 Helio SPT ST/PR EN 1.1 IS. 1. 121. 136.

4 4451 Helio Supp. Struct 1.1 15. 1. 121. 136.

4452 Protection Encl. 0.0 o. o. o. o.

4453 Lightning Prot. 0.0 o. o. o. o.

10 4460 Field Assy and C/O 9.2 192. 1. o. 193.

4461 Heliostat 3.2 74. o. o. 74.

4462 Sensor/Calib. Eq, 0.0 o. o. o. o.

4463 Elec trical/Distrib. 1.8 42. o. o. 42.

4464 Align Heliostats .8 18. o. o. 18.

4465 Field Support 1.1 25. o. o. 25.

4466 Pack and Transp. 2.3 33. 1. o. 33.

4100 Site, Stru.,Misc. Eq•. .2 3. 34. 1. 38. I

4130 Misc. Equip. .2 3. 34. 1. 38. en

4800 Dist. and Indir, 0.0 o. 2. o. 2. (0

4840 Initial Spares 0.0 o. 2. o. 2.


TR-459 S=~I ,_,-------------------

The MDAC heliostat assembly composite unit cost curve starts out at approximately

94%, changes to 96%, and finally reaches 97%.

Table 8-6. ESTIMATED COSTS AND SLOPES FOR THE MDAC HELIOSTAT DESIGN

Quantity Heliostat Investment Cost Composite Curve

Produced $ Per Heliostat Slope (%)

1,000 4,128

93.8

2,000 3,873

94.1

4,000 3,645

95.1

8,000 3,468

95.7

16,000 3,319

96.1

32,000 3,189

96.7

64,000 3,085

100,000 3,017 97.3

While, for the same production scenario, the GE heliostat assembly composite unit cost

curve starts out at approximately 89% changes to 92% and 94%, and finally reaches 99%.

Table 8-7. ESTIMATED COSTS AND SLOPES FOR THE GE HELIOSTAT DESIGN

Quantity

Produced

1,000

2,000

4,000

8,000

16,000

32,000

64,000

100,000

Heliostat Investment Cost Composite Curve

$ Per Heliostat Slope (%)

4,106

88.7

3,640

89.5

3.258

90.9

2,962

92.0

2,723

93.0

2,533

94.0

2,382

2,287 99.0

120
TR-459 S=~I !I.I-------.,.-------------------------

SECTION 9.0

REFERENCES

1. Drumheller, K. Heliostat Manufacturing Analysis. EY-76-C-O 6-18 30. Prepared for

the United States Department of Energy. Richland, WA: Pacific Northwest Laber

ratory; Oct. 1978.

2. Electric Power Research Institute Heliostat Cost Study. Cambridge, MA: Arthur

D. Little, Inc., et al.

3. McDonnell Douglas Astronautics Company, West. Solar Central Receiver Prototype Heliostat CDRL Item B.d Final Technical Report. Vol. I and II. MDCG7399.

Huntington Beach, CA: McDonnell Douglas Astronautics Company; Aug. 1978.

4. General Electric. Solar Central Receiver Prototype Heliostat Phase I. Final Technical Report. SAN-1468-I. Schenectady, N.Y.: General Electric; Oct. 1978.

5. Petruschel, R. L. The Derivation and Use of Estimating Relationship, Memorandum

#RM-3215-PPR. Santa Monica, CA: The Rand Corporation; Feb. 1962.

6. Fisher, G. H. Derivation of Estimating Relationships: An illustrative Example.

Prepared for the United States Air Force Project Rand, Memorandum RM-3366-

PRo Santa Monica, CA: The Rand Corporation; Nov. 1962.

7. Walters, A. A. "Production and Cost Functions: An Econometric Survey." Econometrica. Vol. 31: Jan. 1963; pp. 1-66.

8. Conway, R. W.; Schultz, Andrew Jr. "The Manufacturing Progress Function," The

Journal of Industrial Engineering. Vol. 10: Jane-Feb, 1959; pp, 39-54.

9. Krawiec, F.; Flaim, T. Solar Cost Reduction Through Technical Improvements:

The Concepts of Learning and Experience. SERI/RR-52-173D. Prepared for the

U.S. Department of Energy. Golden, CO: Solar Energy Research Institute; Feb.

1979.

10. Costello, Dennis; et al. Photovoltaic Venture Analysis; Progress Report. SERI23. Golden, CO: Solar Energy Research Institute; Apr. 1978.

11. Young, Samuel L. "Misapplications of the Learning Curve Concept". The Journal

of Industrial Engineering. Vol. XVII (No.8): pp, 410-415.

121
TR-459 S=~I_I ------------------------ 12. Colasuonno, Vincent. An Analysis of Progreg; Curve Conceptual Advances and

Progreg; Curve Uses Since 1956. M.S. thesis. Air University; 1970.

13. Orsini, Joseph A. An Analysis of Theoretical and Empirical Advances in Learning

Curve Concepts Since 1966. M.S. thesis. Air University; 1970.

14. Hirsch, Werner F. "Manufacturing Progress Functions." Review of Economics and

Statistics. Vol. 34: May 1952; pp. 143-155.

15. Andress, Frank J. "The Learning Curve As a Production Tool." Harvard Business

Review. Vol. 32: Jan.-Feb. 1954; pp, 87-97.

16. Baloff, Nicholas. "Extensions of the Learning Curve: Some Empirical Results."

Operational Research Quarterly. Vol. 22: Dec. 1971; pp. 329-340.

17. Wright, T. P. "Factors Affecting the Cost of Airplanes." Journal of Aeronautical

Science. Vol. 1: Feb. 1936; pp. 122-128.

18. Pegels, C. Carls. "Start Up or Learning Curves-Some New Approaches." Decision

Sciences. Vol. 7: 1976; pp. 39-54.

19. Asher, Harold. Cost Quantity Relationships in the Airframe Industry. R-291.

Santa Monica, CA: The Rand Corporation; July 1956.

20. Reguero, Miguel A. An Economic Study of the Military Airframe Industry. Ann

Arbor, MI: University Microfilms International; 1957.

21. Billion, S. A. "Industrial Learning Curves and Forecasting." Management International Review. July 1965; pp. 65-96.

22. Harting, W. G. Cost Estimate Errors Resulting from the Use of Learning Curve

Methods. L. G. Hanseem Field, MA: Escer, Electronic Systems Division; Aug.

1969.

23. Ferguson, C. E. The Neoclag;ical Theory of Production and Distribution. London:

Carnbrldge University Press; 1971.

24. Brewer, Glenn M. The Learning Curve in the Airframe Industry. M.S. thesis. Air

University; 1965.

122
TR-459 S=~I ,i.i--------------------------- - .

25. Morse, Wayne J. "Learning Curve Cost Projections with Constant Unit Costs."

Managerial Planning. Marc-Apr, 1974; pp. 15-21.

26. Hirschmann, Winfred B. "Profit from the Learning Curve." Harvard Business

Review. Vol. 42: Jan.-Feb. 1964; pp. 125-139.

27. Boston Consulting Group. Prospectives on Experience. Boston, MA: 1972.

28. Bodde, David L. "Riding the Experience Curve." Technology Review. Mar.-Apr.

1976; pp, 53-59.

29. Yelle, Louis E. "Estimating Learning Curves for Potential Products." Industrial

Marketing Management. Vol. 5: 1976; pp, 147-154.

30. Seherer, F. M. Industrial Market Structure and Economic Performance. Chicago,

IL: Rand McNally Publishing Co.; 1970.

31. Salter, W.E.G. Productivity and Technical Change. 2nd ed, London: Cambridge

University Press; 1966.

32. Small Power Systems Study. Quarterly Report No.1. SERI-36. Golden, CO: Solar

Energy Research Institute; July 1978.

33. Recommendations for the Conceptual Designs of the Barstow, California, Solar

Central Receiver Pilot Plant. Executive Summary. SAND-77-8035. Sandia Laboratories; Oct. 1977.

34. Solar Central Receiver Prototype Heliostat. Phase 1, Final Technical Report.

SAND-1468-I. Schenectady, N.Y.: General Electric; Oct. 1978.

35. Conceptual Design of Advanced Central Receiver Power System. Phase 1, Executive Summary. Martin Marietta Corp.; Sept. 1978.

36. Prototype/Second-Generation Heliostat Evaluation and Recommendations. Executive Summary. SAND-78-8265. Sandia Laboratories; Oct. 1978.

37. Vant-Mull, L. L. "Development of Solar Tower Program in the United States."

Optical Engineering. Vol. 16 (No.6): Nov.-Dec. 1977.

123
TR-459 S=~I '.1 ---------------------------__ 38. Marshall, B. W. "Operation of the Solar Thermal Test Facility." Department of

Energy Large Solar Central Power Systems Semiannual Review. SAND-78-85ll.

Sandia Laboratories; Nov. 1978.

39. Wilson, W. G. "An Overview of the Heliostat Development Program." Proceedings

of the 1978 DOE Workshop on System Studies for Central Solar Thermal Electric;

Mar. 27-30, 1978. University of Houston Solar Energy Laboratory; Oct. 1978.

40. Schell, Allen C. Preliminarv Design Study of a Flat Plate Reflector and Support

Unit. Hughes Aircraft Co.; May 1964.

41. Nourse, J.H. Prototype Heliostat Costing Scenario. Final Report. Huntington

Beach, CA: McDonnell Douglas Astronautics Company; July 1979.

42. Development of Surrogate Experience Curves for Costs Associated with the Production of Heliostats. Final Report. Cambridge, MA: Arthur D. Little, Inc.; Sept.

1979.

124
TR-459 S=~I.i --------------------------

APPENDIX A

SUMMARY OF DATA ACQUIRED FOR SELECTED SURROGATE PRODUCTS

125
126
TR-459 S=~I~.I ---------------------------- Table A-I. SURROGATE lA-PLYWOOD (TotaI)R

Labor

Production No. of Hours Cost

(feet x 109) Workers Worked (x 103)

Year Annual Cumulative (per million feet of production)

1960 7.8 75.3 b 4.9 b 1965 12.6 127.7 3.5b 1970 14.1 195.4 2.8

1972 17.8 229.6 2.2 5.04 $12.64

1973 17.9 247.6 2.3 4.97 10.94

1974 15.3 262.9 2.4 5.12 12.58

1975 15.7 278.6 2.2 4.54 11.41

1976 17.8 296.4 2.2 4.78 10.96

1977 19.7 316.1 2.1 4.55 9.58

aSource: American Plywood Association.

bEstimates derived from employment figures for softwood plus hardwood plywood.

Table A-2. SURROGATE lA-PLYWOOD (West)R

Production Direct Material Total

(feet x 109) Labor Cost Cost

Year Annual Cumulative (per 103 feet)

1965 11.3 125.0 $15.95 $30.58 $58.51

1970 10.1 177.4 17.12 35.06 65.90

1975 9.3 231.3 15.27 33.27 60.80

1977 10.7 263.3 12.97 29.39 51. 74

aAmerican Plywood Association.

127
TR-459 5=~1'.'------------------

Table A-3. SURROGATE lA-PLYWOOD (South)a

Production Direct Material Total

(feet x 109) Labor Cost Cost

Year Annual Cumulative (per 103 feet)

1965 1.14 1.62 $10.23 $25.12 $54.21

1970 3.32 11.96 6.69 27.17 55.00

1975 5.68 38.06 6.66 24.25 48.58

1977 7.45 52.32 6.02 20.95 40.30

aAmerican Plywood Association.

Table A-4. SURROGATE lA-PLYWOOD CORPORATIONa

Year Production Manhours

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

8,299

34,393

46,945

46,679

48,208

59,063

55,345

48,729

47,247

50,678

55,146

57,230

59,559

34,914

55,019

290,439

.262,377

239,186

248,176

291,685

301,930

271,029

276,036

293,496

309,799

317,840

324,830

168,044

aConfidential source (plywood Corporation).

128
TR-459 S=~I ';.~l ----------------------

-~ '.

Table A-5. SURROGATE lA-PLYWOOD AND VENEERa

Direct Material Total

Production Labor Cost Cost

Year (tons x 106) (x 106) (x 106) (x 106)

1967 7.7 $335.7 $931.4 $1,384.4

1968 8.6 322.1 946.4 1,377.9

1969 8.0 300.1 925.9 1,334.7

1970 8.3 332.7 990.1 1,446.4

1971 9.7 346.8 1,103.6 1,579.4

1972 10.4 357.8 1,218.7 1,702.9

1973 10.4 326.4 1,187.3 1,628.7

1974 9.0 NA NA NA

1975 8.7 386.8 1,132.9 1,534.5

1976 9.9 295.4 1,230.4 1,638.0

a Predicasts.

Table A-6. SURROGATE I B-AIRCRAFT WINDSmELD8

No. of Units Geometric _ Unit Cost

Produced in Lot Meanb (Direct Labor Hours)

4 2.2 97

4 6.4 79

22 20.6 149

4 31.3 181

3 32.4 468

9 52.7 190

35 59.3 153

42 137.0 94

35 146.8 73

15 203.0 182

29 238.5 92

18 241.4 141

aConfidential source (aircraft manufacturer).

bUnits were not produced sequentially within lots; plot points were

calculated according to geometric mean of actual production sequence.

129
TR-459 S=~I il.' _ Table A-7. SURROGATE IC-LAMINATED MmRORSa

Direct Material Total

Labor Cost Cost Cost

Year (x 106) (x 106) (x 106)

1967 $27.12 $81.97 $125.1

1968 28.09 86.72 131.3

1969 30.54 97.54 146.9

1970 29.08 84.26 132.7

1971 30.70 97.55 148.1

1972 42.67 135.69 205.6

1973 44.59 138.69 211.7

1974 46.47 135.08 211.7

1975 49.94 141.82 241.6

1976 52.80 162.32 253.9

a Predicasts.

Table A-8. SURROGATE 2A-AIRCRAFT LANDING GEARa

No. of Units

Produced in Lot

Geometric

Meanb

Unit Cost

(Direct Labor Hours)

4

4

22

4

3

9

35

42

35

15

29

18

3.9

5.4

18.8

29.5

31.2

45.7

60.5

114.6

121.5

161.9

196.0

197.4

238

218

124

140

100

111

97

135

107

155

157

79

aConfidential source (aircraft manufacturer).

bUnits were not produced sequentially within lots; plot points were

calculated according to geometric mean of actual production sequence.

130
TR-459 S=~I - i'*'1 .~ ------------------------

Table A-9. SURROGATE 3A-PERSONAL ELECTRONIC CALCULATORSa

Direct Material Total

Production Labor Cost Cost

Year (tons x 106) (x 106) (x 106) (x 106)

1974 12.0 $130 $415 $603

1975 16.0 84 320 513

1976 17.5 43 196 316

1977 18.7 65 244 393

aPredicasts,

Table A-IO. SURROGATES 4A AND 8C--5TEEL PIPES AND TUBESa

Year Prod. Labor Materials Overland Total

1967 .206 1.000 .099 1.305

1968 .229 1.126 .113 1.468

1969 .267 1.236 .138 _ 1.641

1970 .238 1.014 .133 1.385

1971 .257 1.188 .152 1.597

1972 .331 1.469 .178 1.978

1973 .423 1.908 .217 2.548

1974 .613 3.416 .326 4.355

1975 .735 4.037 .421 5.193

1976 .714 3.687 .431 4.832

a Predicasts.

131
TR-459 S=~II*i_---__-----------

Table A-H. SURROGATE 5A-sTEEL STUDSa

Year

1975

1976

1977

1978

1979

Production Materials Other Cost/Ft

(feet x 103) Cost/Ftb (incl. Profit Margin)b

150.1 $.116 $.063

506.0 .126 .059

1196.0 .137 .059

1176.0 . .149 .058

1067.0 .169 .044

aConfidential source (building component manufacturer).

bEstimate from price data and wholesale price index for galvanized carbon steel

sheets.

Table A-12. SURROGATES 5B-METAL DOOR SASH AND TRIMa

Year Prod. Labor Materials Overland Total

1967 .308 1.000 .241 1.549

1968 .329 1.059 .253 1.641

1969 .363 1.140 .294 1.797

1970 .383 1.186 .306 1.875

1971 .424 1.424 .344 2.192

1972 .520 1.709 .415 2.644

1973 .556 1.769 .434 2.759

1974 .672 2.297 .505 3.474

1975 .620 2.299 .509 3.428

1976 .748 2.879 .621 4.239

a Predicasts.

132
TR-459 S=~I·;·il ----------------------- - .

Table A-13. SURROGATES 5C AND 8B-METAL HOUSEHOLD FURNITUREa

Year Prod. Labor Materials Overland Total

1967 .351 1.000 .202 1.553

1968 .397 1.099 .223 1.719

1969 .455 1.209 .241 1.905

1970 .456 1.253 .276 1.985

1971 .477 1.356 .306 2.139

1972 .574 1.649 .35 2.573

1973 .675 1.921 .422 3.018

1974 .759 2.270 .500 3.529

1975 .709 2.193 .506 3.408

1976 .865 2.694 .614 4.173

a Predicasts.

Table A-14. SURROGATE 6A-DRlVE WHEEL TRANSMISSIONa

Production

Year Volume Manufacturer

Percent Change

Labor Total

1975-76

1976-77

1977-78

1978-79

6,325

11,431

14,303

12,448

+2.3%

+6.2

+0.4

+0.6

-5.6%

-3.7

-7.1

-1.7

-2.1%

+2.1

-2.9

-0.3

aConfidential source (vehicle manufacturer).

bpercentages are based on dollar change in standard cost exclusive of economics or

accounting changes over the time period indicated; 1975 was the first full year of

production.

133
S=~I'.' TR-459

Table A-IS. SURROGATES 8D-FABRICATED PIPES AND FITI'INGSa

Year Prod. Labor Materials Overland Total

1967 .316 1.000 .207 1.523

1968 .371 1.245 .216 1.832

1969 .332 1.417 .223 1.972

1970 .419 1.198 .265 1.882

1971 .475 1.464 .323 2.262

1972 .562 1. 759 .377 2.698

1973 .661 2.324 .443 3.428

1974 1.131 4.010 .580 5.721

1975 1.388 5.796 .951 8.135

1976 2.251 7.754 1.241 11 .246

a Predicasts.

Table A-I6. SURROGATE 9B-AIRCRAFT CANOpya

No. of Units Geometric Actual Unit Cost Adjusted

Produced in Lot Means b (Direct Labor Hours) Unit CostC

4 2.6 1,134 646

4 14.1 234 391

35 25.1 239 331

42 82.3 94 233

35 89.9 192 226

15 130.9 115 203

29 156.8 89 192

aConfidential source (aircraft manufacturer).

bUnits were not produced sequentially within lots; plot points were calculated

according to geometric mean of actual production sequence; e.g.; first lot

contained first, second, fourth, and sixth items produced.

eActual costs were demonstrated to be excessive relative to experience of previous

manufacturer; cost estimates were subsequently derived to ADL study to account

for differences.

134
TR-459 S=~I '1111 ----------------------------

Table A-17. SURROGATE 9E-HELICOPTER BLADE LINERS

No. of Direct

Year Units Labor Materials Total

1969 10 $1,132 $686 $4,422

1970 509 34,788 24,008 129,241 1971

1972 705 36,508 33,379 133,776

1973 1161 59,908 57,901 189,698

1974 909b (da ta missing)

1975 657 22,299 29,304 74,437

1976

1977 1200 35,252 49,840 114,986

1978

1979 1450 35,706 55,585 114,500

~confidential source (plastic components manufacturer).

Estimated production used to determine plot point.

Table A-18. SURROGATES IDA-TRUCKS AND BUSBODIESR

Year Prod. Labor Materials Overland Total

1967 .368 1.000 .199 1.567

1968 .427 1.181 .223 1.831

1969 .468 1.274 .259 2.001

1970 .509 1.440 .319 2.268

1971 .541 1.757 .357 2.655

1972 .769 2.950 .495 4.214

1973 .844 2.910 .520 4.274

1974 .836 2.940 .508 4.284

a Predicasts.

135
TR-459 S=~II*I- -----------------=-

Table A-19. SURROGATE lOB-WING STRUCTURER

No. of Units Geometric Actual Unit Cost Adjusted

Produced in Lot Meansb (Direct Labor Hours) Unit CostC

4 4.4 6,538 4,707

4 4.7 6,170 4,603

22 20.3 2,895 2,984

3 21.5 3,139 2,962

4 28.8 3,178 3,213

9 43.8 1,347 2,400

35 60.4 1,671 2,165

42 114.6 1,362 2,128

35 121.4 1,185 1,759

15 163.3 1,024 1,611

29 194.8 1,091 1,818

18 198.3 1,072 1,534

aConfidential source (aircraft manufacturer).

bUnits were not produced sequentially within lots; plot points were calculated

according to geometric mean of actual production sequence.

eActual costs were demonstrated to be excessive relative to experience of previous

manufacturer; cost estimates were subsequently derived to ADL study to account

for differences.

Table A-20. SURROGATES llA-MOTOR VEIDCLESa

Year Prod. Labor Materials Overland Total

1967 .105 1.000 .045 1.150

1968 .136 1.249 .055 1.440

1969 .139 1.309 .059 1.507

1970 .126 1.134 .061 1. 321

1971 .178 1.651 .075 1.904

1972 .202 1.853 .086 2.141

1973 .246 2.203 .106 2.555

1974 .232 2.159 .109 2.500

a Predicasts.

136
III

Table A-21. SUMMARY STATISTICS III

;41 I_II - 95% ~".-

No. of Confidence

Data Estimated

R2 b

Data Interval

Component Table Surrogate Cost Element Learning %a Slope Points For Slope

Reflectors 4 Plywood (Total) No. of Workers 67% -0.59 0.97 9 62.0 - 71.0 rr Production Labor $ 64% -0.57 0.57 6 37.0 - 109.0 11 Labor Hours 80% -0.33 0.58 6 61.0 - 104.0

5 n (West) Total $ 92% -0.12 0.16 4 52.1 - 162.8 n Labor $ 85% -0.23 0.46 4 50.0 - 146.0

" Material $ 99% -0.02 0.01 4 60.3-161.3

6 n (South) Total $ 96% -0.06 0.53 4 85.2 - 108.2 n Labor $ 92% -0.13 0.92 4 87.0 - 96.0 1t Material $ 98% -0.03 0.29 4 87.5 - 109.1

I-' 7 Plywood Corp. Labor Hours 76% -0.39 0.99 4 70.0 - 82.8 -J

8 Plywood/Veneer Total $ (CUM) 98% -0.03 0.79 7 96.6 - 99.0

n t1 Labor $ (CUM) 95% -0.07 0.95 7 94.0 - 96.0

rr rr Material $ (CUM) 99% -0.02 0.47 7 97.7 - 100.1

rr " Total $ 99% -0.02 0.13 9 96.3 - 101.5

11 " Labor $ 93% -0.11 0.79 9 90.0 - 96.0

9 Aircraft Windshield Labor Hours 100% 0.005 0 12 82.2 - 120.9

10 Laminated Mirrors Total $ (CUM) 100% 0.0 I 0.41 10 100.0 - 101.0 It It Labor $ (CUM) 100% -0.002 0.04 10 99.2 - 100.5

" " Labor $ 100% -0.002 0.06 10 99.3 - 100.3

apercent reduction in cost for each doubling of quantity.

bMeasure of proportion of total variation in log cost that is explained by line of best fit.

I

c:.n

(0
Table A-21. SUMMARY STAl1S'I1CS (continued)

95%

No. of Confidence

Data Estimated

R2 b

Data Interval

Component Table Surrogate Cost Element Learning %a Slope Points For Slope

Elevation 11 Aircraft Landing Gear Labors Hours 90% -0.15 0.38 12 81.9 - 98.9

- c Sleeve and Plunger, Man Hours 88% -0.19 NA 9 NA

Aileron Power

Control Cylinder

(F=4 A/C)

c...:> Drive 12 Personnel Electronic Total $ (CUM) 76% -0.39 0.99 4 70.6 - 82.4

00 Controls Calculators

" " Labor $ (cUM) 72% -0.47 0.99 4 66.0 - 79.0

" " Material $ (CU M) 76% -0.39 0.99 4 70.7 - 82.6

" " Total $ 72% -0.48 0.91 4 52.9 - 97.8

" " Labor $ 67% -0.58 0.86 4 41.0 - 111.0

" If Material $ 72% -0.47 0.91 4 52.8 - 98.2

- e Memory Unit-Saturn Mfg. Hours 78% -0.36 NA 10 NA

Computer

apercent reduction in cost for each doubling of quantity.

bMeasure of proportion of total variation in log cost that is explained by line of best fit.

cLearning curve estimate summarized in IfReport on Improvement Curve Experience," DCAAP 7641.14, April, 1970.

III

III

N

-
Table A-21. SUMMARY STATISTICS (continued)

95%

No. of Confidence

Data Estimated Data Interval

Component Table Surrogate Cost Element Learning %8 Slope R2 b Points For Slope

Pedestal 13 Steel Pipes and Tubes Labor $ (CUM) 100% -0.005 0.02 10 98.0 - 101.6 n II II n Total $ (CUM) 100% -0.003 0.09 10 99.7 - 100.7

" II n

" Labor $ 98% -0.027 0.11 10 94.0 - 102.5 II n II 11 fI Total $ 100% -0.002 0.01 10 98.7- 101.0

....... Reflector 14 Steel Studs Total $ 106% 0.086 0.86 5 101.6 - 110.9 e:,." Supports n C1:l " Other $ 96% -0.060 0.48 5 88.6 - 103.8

15 Metal Door sash and Labor $ 97% -0.039 0.36 10 94.0 - 100.3

and Trim

" 11

" " Total $ 99% -0.012 0.33 10 98.2 - 100.0

16 Metal Household Labor $ 9896 -0.029 0.38 10 96.0 - 100.1

Furniture fI

" Total $ 100% -0.00 I 0.01 10 99.6 - 100.2

apercent reduction in cost for each doubling of quantity.

bMeasure of proportion of total variation in log cost that is explained by line of best fit.

III

III

N

'if.;;:;~ -

I~_II
Table A-21. SUMMARY STATISTICS (continued)

95%

No. of Confidence

Data Estimated

R2 b

Data Interval

Component Table Surrogate Cost Element Learning %a Slope Points For Slope

Azimuth 17 Drive Wheel Transmission Total (% Change) 100% -0.005 0.16 4 97.3 - 102.1

Gear " " " Labor (% Change) 96% -0.052 0.92 4 93.0 - 99.7

Box " " " Material (% Change) 102% -0.027 0.90 4 99.8 - 104.0

- c Constant Speed Drive Direct Labor Hours 92% -0.12 NA 26 NA

Transmission

c SM 705/737 Truck Production Dir. 91% -0.14 NA 5 N/A .....

,;::.. Transmission Labor Hrs, 0

- c M551 Tank Transmission Direct Labor Hours 96% -0.053 NA 16 NA

- c TX- 100 Transmission Direct Labor Hours 87% -0.20 NA 28 NA

_c XTG-250 Transmission Direct Labor Hours 87% -0.20 NA 12 NA

- c XTGYl12A Transmission Direct Labor Hours 83% -0.26 NA 27 NA

Azimuth No Data Available

Drive

Hub

a Percent reduction in cost for each doubling of quantity.

bMeasure of proportion of total variation in log cost that is explained by line of best fit.

cLearning curve estimate summarized in "Report on Improvement Curve Experience," DCAAP 7641.14, April, 1970.

III

III

N

-
Table A-21. SUMMARY STATIS11CS (continued)

In

III

-

Data Estimated

Component Table Surrogate Cost Element Learning %a

Reflector 18 Fabricated Piping and Fittings Labor $ (CUM) 98%

Yoke 11 " n n Total $ (CUM) 99%

" 11 11 " Labor $ 98% fI 11 11 " Total $ 99%

95%

No. of Confidence

Data Interval

Slope R2 b Points For Slope

-0.025 0.37 10 96.0 - 100.1

-0.009 0.38 10 98.7-100.0

-0.030 0.08 10 92.0 - 103.9

-0.012 0.12 10 97.3 - 101.1

13 Steel Pipes and Tubes

Data also given for Reflector Supports ...... 16 Metal Household Furniture ;l::l.

......

Reflector 19 Airc raft Canopy Labor Hours 68% -0.56 0.88 7 58.0 - 79.9

Enclosure (Actual) n

" Labor Hours 81% -0.30 1.00 7 81.3 - 81.6

(Adjusted)

20 Helicopter Blade Liners Labor $ 91% -0.14 0.99 4 88.1 - 94.1

" II n Total $ 86% -0.22 0.90 7 80.8 - 90.9

_c Inflatable Shelter Labor Hours 85% -0.24 NA 10 NA

8Percent reduction in cost for each doubling of quantity.

bMeasure of proportion of total variation in log cost that is explained by line of best fit.

cLearning curve estimate summarized in "Report on Improvement Curve Experience," DCAAP 7641.14, April, 1970.
Table A-2l. SUMMARY STATISTICS (concluded)

95%

No. of Confidence

Data Estimated Data Interval

Component Table Surrogate Cost Element Learning %a Slope R2 b Points For Slope

Assembly 21 Trucks and Bus Bodies Labor $ 94% -0.097 0.64 8 89.0 - 98.0

" " " " Total $ 98% -0.025 0.55 8 96.8 - 99.8

22 Wing Structure Labor Hours 71% -0.49 0.95 12 67.2 - 75.4

(Actual)

" " Labor Hours 82% -0.28 0.96 12 80.0 - 85.0

(Adjusted)

c ..... F-4 Wing-Slotted Total Mfg. 87% -.020 NA 27 NA ,.j:>.

I:,:) c - Fuel Tank and Direct Mfg. 86% -0.22 NA 7 NA

Afterbody Assy. for Hours

MK46 Torpedo

_c AN/MPQ-4A Trailer Direc t Labor 93% -0.10 NA 10 NA

Assemblies Hours

Overall 23 Motor Vehicles Labor $ 101% 0.01l 0.29 8 99.6 - 102.0

Product " " Total $ 100% 0.002 0.33 8 100.0 - 100.3

. apercent reduction in cost for each doubling of quantity.

bMeasure of proportion of total variation in log cost that is explained by line of best fit.

cLearning curve estimate summarized in "Report on Improvement Curve Experience," DCAAP 7641.14, April, 1970.

III

III

AI

I.;;;~I -

'-:: ~,/
TR-459 S=~I ,(I}I-------------------------

APPENDIX B

ESTIMATIONS OF THE SURROGATE LEARNING CURVE

143
144

TR-459 S=~II.I -------------------------

Figure B-1. LEARNING CURVES ILLUSTRATIVE EXAMPLE (continued)

(i) CUMULATIVE AVERAGE CURVE

Plot

Point Average Cost

12.0 673/12 = $56.08

28.0 (673 + 513)/28 = $42.36

45.5 (673 + 513 + 316)/45.5 = $33.01

64.24 (673 + 513 + 316 + 393)/64.24 = $29.50

CD UN IT COST CURVE

Plot

Point* Unit Cost

5.3 673/12 = $56.08

20.0 513/16 = $32.06

36.7 316/17.5 = $18.06

54.7 393/18.74 = $20.97

*Plot Point =(. ±: log i) /N

1 = F

where F = first unit in lot

L = last unit in lot

N= number of units in lot

146




TR-459 S=~I !.'----------------------

APPENDIX C

COST CURVES FOR THE MDAC AND GE HEIJOSTAT DESIGNS

151



















Document Control I" SERI Report No.

Page TR-353-459

4. Title and Subtitle

12. NTIS Accession No. 3. Recipient's Accession No.

5. Publication Date

An Investigation of Learning and Experience Curves

7. Autnorts)

Kr;,n.ri Pro F' Th r-on r ori _ J.: Edesess .. M.

9. Performing Organization Name and Address

Solar Energy Research Institute

1617 Cole Boulevard

Golden, Colorado 80401

12. Sponsoring Organization Name and Address

15. Supplementary Notes

Aori1 1980

6.

8. Performing Organization Rept. No.

10. Project/Task/Work Unit No.

5227

11. Contract (C) or Grant (G) No.

(C)

(G)

13. Type of Report & Perioa Covered

Technical Reoort

14.

16. Abstract (Limit: 200 words)

This study assesses the applicability of learning and experience curves for predicting future costs of solar technologies, and the major test case is the production economics of heliostats. Alternative methods for estimating cost

reductions in systems manufacture are discussed, and procedures for using learning and experience curves to predict costs are outlined. Because adequate

production data often do not exist, production histories of analogous products/

processes are analyzed and learning and aggregated cost curves for these

surrogates estimated. If the surrogate learning curves apply, they can be used

to estimate solar technology costs. The steps involved in generating these cost

estimates are given. Second-generation glass-steel and inflated-bubble heliostat

design concepts, developed by MDAC and GE, respectively, are described; a costing

scenario for 25,000 units/yr is detailed; surrogates for cost analysis are chosen;

learning and aggregate cost curves are estimated; and aggregate cost curves for

the GE and MDAC designs are estimated. However, an approach that combines a

neoclassical production function with a learning-by-doing hypothesis is needed

to yield a cost relation compatible with the historical learning curve and the

traditional cost function of economic theory.

17. Document Analvsis

a. Descriptors Learning Curves ; Exper ience Curves ; Comrnercializa tion ; Design ;

Production ; Cost ; Learning ; Heliostats ; Installation

b. Identifiers/Open-Ended Terms

c. UC Cateqories

18. Avallaollity Statement

National Technical Information Service

u. S. Department of Commerce

5285 Port Royal Road

Springfield, Virginia 22161

Form No. 8200-13 \6-79)

19. No. of Pages

190

20. Price

$9.00