THE LEARNING CURVE: HISTORICAL REVIEW AND ...s3.amazonaws.com/prealliance_oneclass_sample/qolgKJ4BBk.pdf1 10 100 1000 A basic introduction to learning curves is provided by Carlson

Education

THE LEARNING CURVE: HISTORICAL REVIEW AND COMPREHENSIVE SURVEY

Louis E. Yelle, University of Lowell

ABSTRACT

The use of the learning curve has been receiving increasing attention in recent years. Much of this increase has been due to learning curve applications other than in the traditional learning curve areas. A comprehensive survey of developments in the learning curve area has never been published. The closest thing to a survey was by Asher in 1956. His study focused exclusively on military applications during and immediately after World War 11. This paper summarizes the learning curve literature from World War 11 to the present, emphasizing developments since the study by Asher. Particular emphasis is given to identifying the new directions into which the learning curve has made recent inroads and identifying fruitful areas for future research.

INTRODUCTION

The learning curve phenomenon was first reported by Wright in the literature in 1936 [89]. The phenomenon which Wright observed was that as the quantity of units manufactured doubles, the number of direct labor hours' it takes to produce an individual unit decreases at a uniform rate. The uniform rate (i.e., 90 percent, 80 percent, 70 percent, etc.) of learning is peculiar to the manufacturing process being observed. Some typical learning curves having different learning rates are shown in Figure 1.

Learning curves follow the mathematical function

Y = KX"

where Y K

X =The cumulative unit number.

=The number of direct labor hours required to produce the Xth unit =The number of direct labor hours required to produce the first

unit.

n =-- log ' - The learning index. log 2 @J , =The learning rate. 1 -6 =The progress ratio.

'Some authors prefer to use cost as opposed to direct labor hours. This author subscribes to the school of thought that believes direct labor hours are a more useful measure. The primary reason is that hourly compensation usually changes over time. Also, there is the additional problem of inflation. In any event, direct labor hours can be easily converted into cost.

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FIGURE 1 Typical Learning Curves All Requiring One Direct Labor Hour

to Manufacture the First Unit (i.e., K = 1)

1.0 J-

\ 1 10 100 1000

A basic introduction to learning curves is provided by Carlson [28]. Various authors have sometimes referred to the learning curve and related concepts by other names such as the progress curve, the improvement curve, and the experience curve. In this survey, the term learning curve will be used throughout,

The learning curve began receiving attention during World War 11 as government contractors searched for ways which they could use to predict costs and time requirements for construction of ships and aircraft to be used to conduct the war. Crude attempts to use the data generated by government shipbuilding contractors during the early phases of the war were published by Montgomery [69] in 1943 and Searle [79] in 1945.

It wasn’t until after the war that aircraft production data were utilized by Alchian [3] [4] in an attempt to compress the aircraft production experience of World War I1 into a study relating the aforementioned experiences to the Wright phenomenon. In 1956, Asher [8] put most of the military World War 11 and immediate post-World War 11 experience into focus by publishing his classic study. This study is particularly important because it summarizes much of the un- published literature of that era.

The experiences of manufacturers with the learning curve phenomenon led to its gradual adoption by private enterprise after the war. I t is with that experience that this survey is concerned. Readers who are interested in the learning curve experience of the war years are referred to Asher [8].

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LEARNING CURVE GEOMETRY

Many geometric versions of the learning curve have been proposed since the initial discovery by Wright. The more well-known models are: 1. The log-linear model. 2. The plateau model. 3. The Stanford-B model. 4. The DeJong model. 5 . The various models are well described by Carlson (281 [29], and interested readers are referred to these two articles. The various models are depicted in Figure 2.

The S-model (i.e., cubic L-C).

FIGURE 2 Various Learning Curve Models All Having the Same Value of Y at I00 Units

s: t The reason for the search for something other than Wright’s log-linear

model stems from the fact that the linear model does not always provide the best fit in all situations. Garg and Milliman [41] describe the case where the Boeing Company found that the Stanford-B model was the best for the manufacturing of the Boeing 707 from the standpoint of describing actual experience. A modified version of the Stanford-B model was used to incorporate design changes on the Boeing 707.

The essential point is that although the log-linear model has been, and still is, by far the most widely used model, some manufacturers have found other models that better describe their experiences. Throughout the remainder of this paper we will be assuming the log-linear model unless explicitly stated otherwise.


PARAMETER ESTIMATION

Parameter estimation is particularly important because it allows manufacturers to more carefully plan their activities. The earliest study conducted in a search for factors affecting the parameters of the learning curve was by Alchian [3] [4] on wartime airframe data. Alchian found that fitting learning curves to the aggregate past performance of a single manufacturing facility in order to predict the future could result in a significant margin of error. This study was particularly significant because airframe manufacturers had, by far and in large, been operating on the assumption of an 80 percent learning curve. And, this assumption did not take into consideration the margin of error nor differences between airframe types.

Hirsch [50], in a comprehensive study of seven different machines built by a single manufacturer, concentrated on the slopes of the learning curves. He found that the individual progress ratios ranged from 16.5 to 20.8 percent. In a subsequent study on eight products made by the same manufacturer, Hirsch [51] found that the progress ratios varied between 16.5 and 24.8 percent.

Cole [35], in his 1958 California survey of non-aircraft companies, concluded that there was very little difference in progress ratios between different types of manufacturing studies (18 to 23 percent). Cole also concluded that there was no causal relationship between the slope and the first unit cost for different products. Cole’s conclusion that a K - n relationship did not exist was in disagree- ment with Asher [8]. And, to this writer’s knowledge, this inconsistency has never been resolved.

In the period 1966-1967, three separate studies were published whose central focus was on the predictability of the learning curve parameters. Baloff [lo), in his study of twenty-eight separate cases of new products and new process startups occurring in five separate companies in four different industries, found that the n parameter varied widely. Billon [19], in his study, searched for regularity in learning curves in order to improve forecasting. The study consisted of five distinct manufacturing programs and fifty-four products with three separate manufacturers. Billon concluded that the slope tends to vary among firms manufacturing similar products, among nonsimilar products manufactured by a single firm, and also among various models of a basic product type produced by a single firm.

Subsequently, in 1967 Baloff (111 described the results of an empirical approach to estimating the learning curve parameters using manufacturing experience and experimental studies in group learning. The primary focus of the paper was on estimating n, given a reliable measure of K. In other words, the approach assumed a K - n relationship existed. The study was conducted on steel and airframe industry data. The results were sufficiently interesting to suggest further work but could not be considered conclusive.

The parameter prediction dilemma still exists today. Two studies published by Yelle [91] [92] utilized the disaggregation-aggregation approach described by Conway and Schultz [37]. The results of the first study showed some promise, but since a single product was studied the conclusions that can be drawn from it are


limited. Then, in 1976, Yelle [92] developed a theoretical framework for cir- cumventing the power function addition problem described by Conway and Schultz [37]. Further work is needed in the general area of parameter prediction. Academicians as well as practitioners seem to have neglected this area for the last ten years.

LEARN I NG

At a macroscopic level the learning curve includes two categories of learning. They are labor learning and organizational learning. Hirschmann [52] claims that the two ways to improve learning lie in the inherent susceptibility of the labor in an operation to improve and the degree to which this susceptibility is exploited by the organization.

Labor Learning vs. Machine Learning

Operations that have a high degree of labor content or are, in other words, paced by labor can be expected to have much steeper slopes than operations that are machine paced. Hirschmann [53] and Jordan [59] reviewed airframe manufacturing data. Both authors estimated that the proportion of machine- paced labor had historically affected the slope of the learning curve in approximately the following way:

Machine-Paced Labor Learning Progress (as a percent of total labor) Rate Ratio

25 To 80% 20 Yo 50% 85% 15% 15% 90 Yo 10%

The only definitive empirical study done on this topic was conducted by Hirsch [51]. Hirsch found that machining progress ratios were much smaller than assembly progress ratios. Assembly progress ratios were approximately two times as large (25.6 percent vs. 14.1 percent). Thus Hirsch’s study established that the progress ratio decreases as the proportion of machine-paced labor to total labor increases.

The Learning Curve and Labor Slandards

The most widely used application of the learning curve has been as an aid in setting labor standards. When new or unskilled operators perform a task for the first time, they cannot be expected to do so in what would be considered to be an acceptable time span. A certain period of time or number of cycles must be allowed for the operator to gain familiarity with the necessary movements in order to build up speed. The relationship between the learning curve and the


standard time is shown in Figure 3. Some of the better articles which address the relationships between the learning curve and the standard time are by Corlett and Morcombe [38], Hancock [47], Kilbridge [62] [63], and Thomopoulos and Lehman [83].

FIGURE 3 Relationship Between the Learning Curve and the Standard Time Per Cycle

30 t U n s k i l l e d operator r

20

x M )I S k i l l e d oDeratar

W d c

10

I Standard T i m e

0 I

100 I

200 I

300

UNITS

One of the difficulties associated with setting standards using the learning curve is that, in many situations, operators possess a certain degree of skill gained on other jobs. This tends to make the standard-setting procedure quite difficult because operators who have achieved a degree of speed on other jobs will require fewer cycles to reach the standard time. This problem is addressed by DeJong (391. DeJong developed formulas to approximate the fall in cycle time when making time studies given that residual skill resulting from prior experience exists.


The Problem of Incentives

Operators accustomed to earning good wages on an incentive system natu- rally resist being transferred to another job within the plant. The source of this resistance is the possible drop in incentive pay during the learning period on the new job. Janzen [58] presents a quantitative method for handling an experienced operator’s wage decrease when he is given a new job of the same general type that he had been performing on an incentive basis. A complexity index is calculated and used in conjunction with the learning curve to determine how much time will be allowed for the operator to attain standard time and the incentive he will be paid during their period. Baloff and McKersie [I41 subsequently proposed that a reliable sliding norm with incentives tied to that norm is what is needed.

Turban [85] suggests handling the problem of incentive wage losses by setting temporary time standards in accordance with the learning curve. Purdue labor learning tables are introduced to construct the temporary standards on a week-by-week basis. The most important finding of this study was that an incentive during the learning period leads to operators learning faster, thus shortening the learning period. In yet another study, Broadston [25] proposed that using variable time standards would more nearly meet the needs of a wage incentive system.

The situation where a production team is used in manufacturing presents problems in the standard-setting process. Occasionally, a member of the team will have to be transferred or will leave the organization. This requires the infu- sion of a new inexperienced worker into the team. The regular members of the team will resent this because the new member, due to his inexperience, will reduce the output of the whole team while he learns. Barron [IS] describes this problem and suggests some methods for handling this type of situation during the new member’s learning phase in order that the regular members of the team do not have to take a cut in pay while the new member is learning.

Interruptions in the Learning Curve (i.e., Relearning)

Interruptions or discontinuities in the learning curve generally occur when new model changes are introduced, the design of the product is changed, or in the case of intermittent production on the same product. These interruptions lead to a learning loss on the part of operators who originally performed the task.

Hall’ [46] suggests that design changes lead to two costs: 1. The cost of added design less the quoted cost of the design removed. 2. Loss of learning-resulting in not being able to produce an assembly at the

full quantity contracted. Hall focused on a practical way of “factoring in” a new design change into the learning curve after the first unit is produced. Simple graphic techniques are proposed to determine the cost in hours of major design changes.

1979) THE LEARNING CURVE 309

Anderlorh [6] focused on “production breaks” between manufacturer lots and the resultant loss of learning which takes place. Five factors associated with loss of learning are identified. Baloff [12], Cochran [34], Carlson and Rowe [30], Hoffmann [55], and Towill [84] ‘also address this problem.

Organizational Learning vs. Labor Learning

The learning curve should be thought of as an aggregate model in the sense that it includes learning from all sources within the firm. On a macroscopic basis one may view the learning curve as a model which represents labor learning as well as managerial or organizational learning. Hirsch [50] in his study found that approximately 87 percent of the changes in direct labor requirements were associated with changes in technical knowledge (a form of organizational learning).

Wyer [90], using aircraft industry data, makes the point that the extent of the cost decrease that can be anticipated is associated with: 1. The complexity of the product. 2. The amount of planning done. The second point is particularly important as it is primarily related to organizational learning. The quality of managerial planning is reflected in the slope of the learning curve. Good planning results in a flatter learning curve during the early learning phase due to lower initial unit costs.

Lundberg [67] published an article which is closely related to Wyer’s work. Lundberg’s major point is that there is a direct relationship between the quantity of articles to be produced and the amount of effort management is willing to put into the pre-production planning phase. Stated simply, the larger the lot, the bigger the organizational effort and the flatter the learning curve during the initial learning period.

Hirschmann [52] uses petroleum refining as an example of where the learning curve might be thought to be inapplicable. He proves that the learning curve applies in the petroleum refining industry and that this is due to organizational learning (i.e., technological learning) and not labor learning. The essential point is that the learning observed in this industry can’t be due to direct labor learning as direct labor is practically nonexistent in the petroleum refining industry.

The “adaptation function” proposed by Levy [66] attempts to put organizational learning in perspective. Levy believes that the planning process can be improved through a better understanding of how the individual worker as well as the firm have historically adapted to past learning situations. The formal training and equipment replacement areas are used to illustrate how decision making was improved due to a better understanding of past behavior.

Bodde [20] summarizes some of the important issues concerned with organizational learning and labor learning. This article is recommended to readers who are interested in the use of the learning curve in manufacturing management.

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FORECASTING LABOR REQUIREMENTS

The learning curve literature has not formally addressed in any great detail the idea of forecasting labor requirements using the learning curve. This probably emanates,from the fact that much of the literature addresses the problem of setting time standards utilizing the learning curve. Then, overall labor force requirements are obtained indirectly by converting from time standards. With respect to forecasting the required work force, two points deserve special attention. The first point is that for a fixed labor force, capacity expands automatical- ly as learning takes place. There are significant implications in this from the aggregate planning point of view. The classical literature on aggregate planning did not consider the learning curve as one of its elements until Ebert’s work [40] was published in 1976. Authors who touch upon this are Andress [7], Cochran [34], and Hartley [48].

The second point which deserves special mention is the one made by Russell [78]. Russell elaborates on the ramifications of adding and subtracting parallel production lines. The theory as proposed states that doubling the number of production lines will double the quantity produced. But, the cumulative average number of units produced by each production line remains the same. The im- plication here is that expanding a single production line in order to increase output will accelerate the learning process. Therefore, in making a decision to ex- pand, the advantages and disadvantages of having two parallel production lines as opposed to a single larger production line should be weighed carefully. Both Baloff [12] and Cochran [32] discuss this point in a tangential way.

PLATEAUING

The phenomenon of plateauing was first observed by Conway and Schultz [37] and subsequently explored in detail by Baloff [lo] [13]. The two phases of plateauing are depicted in Figure 4. The first phase consists of the initial or startup phase. This is the early manufacturing history of a product. The second phase represents the steady-state phase of the learning curve or, in other words, the point at which learning ceases.

Machine-Intensive Manufacturing

Baloff [ 101 studied plateauing in machine-intensive manufacturing. The study consisted of twenty-eight separate cases of new product and new process startups that occurred in five separate companies in four different industries. Heavy emphasis was on the steel industry. Plateauing was observed in twenty out of twenty-eight cases.

Labor-Intensive Manufacturing

Baloff [ 131 explored the plateauing phenomenon in labor-intensive manufacturing. Three labor-intensive industries were studied; auto assembly, apparel

19791 THE LEARNING CURVE

FIGURE 4 The Two Phases of Plateauing in the Learning Curve

31 1

manufacturing, and the production of large musical instruments. In the musical instruments industry, only one-sixth of the products studied had a steady-state phase. No evidence of plateauing was observed in apparel manufacturing for the three cases examined. Auto assembly had plateauing in 75 percent of the situations studied.

The general conclusion which can be drawn from the two studies by Baloff is that plateauing is much more likely to occur in machine-intensive industries than it is in labor-intensive industries. One possible explanation for this is that plateauing could be strongly associated with labor ceasing to learn. And, in machine- intensive manufacturing, this is likely to happen much sooner because the proportion of machine-paced labor (to total labor) is much higher while the progress ratios are smaller. Also, in machine-intensive manufacturing, the steady state phase could be associated with management’s unwillingness to invest more capital in order to beget the technological improvements necessary for the learning process to continue. Hirschmann [52] offers still another possible explanation for plateauing. Hirschmann makes the point that skepticism on the part of management that improvement can continue may in itself be a barrier to its con- tinuance. This skepticism may lie in the fact that new goals are not set once previously defined goals have been achieved. And, lacking new goals, managers do not have incentives to motivate themselves. This position is somewhat sup- ported by Conway and Schultz [37] who found that two products that had plateaued in one firm continued down the learning curve when transferred to other firms. Two other articles related to plateauing to which readers are referred are Goel and Becknell [42] and Knecht [a].


TECHNICAL KNOWLEDGE AND INVESTMENT

Technical knowledge and investment are inseparable in the sense that investment begets technical knowledge which in turn results in lower costs as learning takes place. Hirsch [50], in his study of machine building, demonstrated the importance of the effect of changes in technical knowledge. He found that approximately 87 percent of the changes in direct labor hour requirements were associated with changes in technical knowledge.

Hollander [56] [57], in his pioneering study of DuPont rayon plants, found that 10 to 15 percent of the increased efficiency of the plants was due to the effect of plant expansion. The remaining 85 to 90 percent was accounted for by technical change. The author concluded that over 85 percent of the unit cost reductions (due to technical change) at each plant depended upon techniques that required investment. Unit cost reductions that required replacement investment were of special importance. Hollander’s recommendation was that less weight should be given to investment in plant and equipment similar to that already in existence and more weight placed on the insertion of novel technology into manufacturing.

1. 2. The main conclusion drawn by Sheshinski is that efficiency growth is correlated with the level of investment and that the large residual which is left can be ex- plained by technological learning.

In [53], Hirschmann suggested that the relationships of learning curves to depreciation and capital investment indicate that depreciation has been more than adequate to provide replacement capital. In other words, technological progress decreased costs more than inflation increased them.

Sheshinski [SO] postulated that: Cumulative experience depends on cumulative gross investment. Cumulated experience depends on cumulated output.

MANAGEMENT CONTROL

A management control system may be thought of as that system of inter- woven checks and balances that monitors the activity of a firm. The fundamental purpose of a management control system is to help management assess performance at various levels and indicate where remedial action is needed. In other words, the management control system is the primary mechanism used to assess managerial effectiveness. And, the learning curve is related to the management control process because it is used by management for planning and goal-setting purposes.

Young [93] states that there are five problems that complicate the isolation of the reasons for the learning curve slope decline. They are: I . Overestimation of initial costs in order to protect oneself. 2. Shifting workers from direct to indirect status and vice-versa. 3. Changes in manufacturing methods and tooling. 4. The manufacturing lot size and material availability. 5 . Continual engineering changes.


The Learning Curve and Budgeting

Summers and Welsch in [82] make the point that the standard cost concept implies no learning (i.e., that the learning curve is horizontal). Therefore, if learning is taking place over time, budget standards based upon the standard cost concept are apt to be excessively liberal or tight depending on where various operators are positioned on the learning curve. The resultant variance reports are likely to be misleading. They conclude that inclusion of the learning curve in budgeting should improve managerial planning and control of operating costs. As implied in Figure 3, this requires a knowledge of the operator’s previous experience as well as his current position on the learning curve with respect to the standard. The authors suggest that it is also possible to incorporate a “learning effect variance” to measure performance. Bump [27] also suggests that the learning phenomenon will wreak havoc with variance reports and that the inclusion of the learning phenomenon in setting the standard cost would lead to more mean- ingful reports for management control. Ordinarily standards are set once a year. A significant amount of learning can take place during one year. I f a firm uses the approach of estimating the expected average standard cost over the year for budgeting purposes, the learning phenomenon will accentuate unfavorable variances over the latter part of the year. Readers are referred to Harvey [49] for additional reading in this area.

The Learning Curve and Breakeven Analysis

Brenneck [23) examines the effect of the learning curve on variable cost per unit. As shown in Figure 3, the cycle time per unit will decrease as long as learning takes place. As the cycle time decreases, both the per unit direct labor cost and variable cost will drop. The conclusion that Brenneck draws is that learning curves should be used in conjunction with traditional breakeven analysis in order to better approximate the variable cost per unit. This is because the variable cost per unit will change with volume produced as long as learning exists, and traditional breakeven analysis assumes that variable cost per unit is a constant. Mcln- tyre [68] and Pegels [75] provide additional insight on this topic.

Manpower Scheduling

Whenever the learning curve phenomenon exists, it must be considered when making out manpower schedules. A knowledge and understanding of the learning curve makes it possible to project more accurate manpower requirements in advance. Authors who address this issue are Brenneck (241, Katz [60], Shroad [81], and Wertmann [87].

When one department depends upon another for its input, serious inventory problems can occur if the departments are on different learning curves. The use of learning curves to schedule manpower in the two departments in such a way that imbalances do not occur is discussed by Cochran [33] [34].


Behavioral Implications of the Learning Cwve

One of the fundamental ingredients of any management control system is the behavioral effect it has on key employees. White [88] suggests it is desirable for management to predict expected improvement in advance. Goal-setting is a proven ingredient for success in business. Yet Hirschmann 1521 warns against what he calls a “ceiling psychology.” The ceiling in this case is set using the learning curve and then management works towards achieving the target. As Hirschmann sees it, once the goal is reached, in many instances management does not forge ahead to drive its costs down further. This is undoubtedly due to the fact that management’s incentive is the target that it set for itself. And, once that goal is attained, the motivation to improve disappears. In other words, the target set with the use of the learning curve becomes a self-fulfilling prophesy.

COST REDUCTION PROGRAMS

Cost reduction programs are very important for obvious reasons. Two of the more fruitful areas for cost reduction programs are the pre-production planning and the product-redesign areas.

FIGURE 5 The Effect of Organizational Planning Effort

on the Initial Phase of the Learning Curve

1

m 6

7 Poor planning

100

10

1

1 10 100 1000

CuPpILbTIVX mER OF UUITS


Pre-Production Planning

The effect of significant organizational planning in the pre-production stage is to flatten out the initial phase of the learning curve. Stated another way, the more planning a firm does prior to manufacturing a product, the lower the value of the K parameter in the learning curve model. Becker and Baloff [I61 and Bhada [I71 make this point. The ramifications of organizational planning become quite obvious when one views Figure 5. Even though the product cost will follow the learning curve, effective planning in the early stages will significantly reduce the initial cost. The area between the two functions may be thought of as repre- senting the potential cost reduction due to planning. If it is more costly to plan properly, then the potential cost reduction must be decreased by this additional cost.

Supply-demand imbalances during the production phase are addressed by Abernathy and Baloff [ I ] . The authors suggest that careful planning at the production-marketing interface would help prevent costly imbalances due to the rate of increase in productivity being different from the rate of increase in sales.

Redesigning

Prior to redesigning a product for lower cost, it will be necessary to estimate the engineering effort required to do the job. Both the product technology and the organization’s engineering skills will have to be appraised. Bruns [26] suggests that the learning curve can help. In his article he presents a unique two- dimensional model relating the engineering learning curve to the production learning curve. The focus of the model is on helping management estimate how many engineering design passes are needed to drive the product cost down to some predetermined target, The model is unique and is highly recommended for readers interested in redesigning products to reduce costs.

PURCHASING DECISIONS

Purchasing and bidding decisions are made daily by business firms. The learning curve can be a valuable aid in the decision-making process. The potential contribution of the learning curve lies in the fact that a firm can evaluate learning on its own products (bidding) as well as on products of its suppliers (purchasing).

Bidding

When bidding on an order, it is important to know what the costs are now as well as what they will be in the future. The value of the role of the learning curve in the bidding process lies in the fact that it allows a firm to project its costs out over an entire order and quote or bid accordingly. If the quote is for delivery sometime in the future, then the current costs of the firm may not be relevant at all for bidding purposes. If the quote is for immediate delivery, then the learning


which takes place during the time required to manufacture the lot is all that is relevant. Readers are referred to Boren [21] for more information in this area.

Evaluation of Supplier Quotes

The learning curve can be helpful in evaluating quotes from suppliers. Most quotes include quantity discounts for purchasing larger volumes of materials. Rice [77] proposes evaluating a vendor’s quotes using the learning curve. The method suggested requires that the per-item price be plotted against volume on log-log paper. Rice concludes that this process should yield a straight line. If the process does not yield a straight line, then the vendor is probably not quoting ac- cording to learning in all cases. Those quotes which appear to be out of line (peaks) should be renegotiated with the supplier prior to making a decision on how much to buy. Readers are referred to [ 5 ] and (75) for other reading in this area.

The Learning Curve and Economic Order Quantity

The only work of any significance done in this area is by Keachie and Fon- tana [61]. The article deals with the learning curve effect on the calculation of economic order quantities using the classical formula. The study assumed intermittent production with large enough lots so that the learning phenomenon would occur within a given lot. It was demonstrated that the traditional lot-size formula yielded answers that were smaller than optimum. The underlying reason for this is that the traditional formula assumes a constant manufacturing unit cost.

NON-MANUFACTURING APPLICATIONS OF THE LEARNING CURVE

Until recently, the learning curve was primarily thought synonymous with manufacturing activity of some sort and/or cost control. In the last few years, however, the learning curve has made inroads into other areas. Those areas are discussed below.

The Learning Curve and Accident Experience

Greenberg [43] (441 applied the learning curve concept to the industrial accident experience in the petroleum industry. The study encompassed forty-seven firms and a total of 163 departments.

The focus of the study was on USA standard 216.1. One of the statistics defined by this standard is listed below:

No. of Disabling Injuries x l,OOO,OOO No. of Manhours Worked

Disabling injury frequency rate =


Greenberg found that this method of reporting accidents was very misleading. The fundamental reason for this is that the denominator is in units of time rather than units of production. Thus, learning is not taken into consideration over time. The crux of the matter is that 216.1 does not include a productivity measure. By using hours (time units), and given that productivity per hour has increased over time, accidents per unit decrease over time.

Greenberg found that the incidence of accidents was found to be highly correlated negatively with productivity. In other words, accidents per unit produced decreased as management gained productive experience. When accident experience is computed on a per-item basis and is plotted against cumulative production, a learning curve accident model emerged of the form Y = KX".

In yet another study, Greenberg [45], using mining industry data, demonstrated that mining accidents also conformed to the learning curve model. One important observation which should be made about Greenberg's studies is that he used publicly available data. For years, the nonavailability of data has been the bane of the existence of researchers interested in the learning curve phenomenon.

'

The Learning Curve and Warranty Maintenance

Kneip [65] selected two products and proceeded to accumulate field service data on the two products. The objective was to ascertain if the learning curve could adequately describe the relationship of a product's maintenance requirements during the warranty period to cumulative production experience. The situation is depicted in Figure 6. The results of regression analyses on the two products proved that there was a strong relationship between the amount of service required to perform warranty maintenance and cumulative production experience because the quality of the product improved as more units were manufactured. The relationship was of the classical form Y = KX", and the results were significant at the .001 level.

In a different kind of study, Clark [31] used learning curves to predict the required size of a plant's maintenance force. The assumption was made that maintenance workers learn and that this learning is adequately described by the classical model Y = KX". Clark was able to successfully demonstrate that the learning curve was useful in predicting the size of a maintenance force over time.

The Learning Curve, Cost Allocation, and Income Reporting

The accounting profession has for years followed the practice of reporting actual costs in the period in which those costs are incurred. From the product-life- cycle point of view, this practice leaves something to be desired. It inevitably results in heavy losses during the initial phases of the product life cycle due to required initial investments and the usual high cost on the*first units manufactured. In the latter stages of the product's life cycle the opposite occurs. The firm reaps the benefits of low per-unit costs due to learning and also the benefits derived

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FIGURE 6 The Relationship of a Product's %nice Requirements

During Its Warranty Period

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from having made investments in the early phases. Morse [70] states that the practice of reporting costs in the period incurred is inconsistent with the accounting profession’s definitions of the concepts of “matching” and “materiality.” Morse takes the position that the allocation of costs over the entire production life cycle of the product with the help of the learning curve would more closely meet the “matching” and “materiality” criteria.

The procedure suggested by Morse is to use the learning curve to arrive at an average unit cost over the entire product life cycle. Then costs are charged on a per-period basis using the average unit cost projected by the learning curve. The net effect is that income is raised in the early phases and lowered in the later phases of the entire production life cycle. In other words, an income smoQthing effect takes place. Morse concludes that this approach more closely meets the criteria suggested by Bierman and Davidson [18]. Readers are referred to Morse [70] (711 for further reading on this topic.

Recently, Harvey [49] built upon the works of Bump [27] and Summers and Welsch [82]. He developed modifications to the learning curve in order to better observe the financial effects of errors in parameter estimates. The financial areas studied were cash flow, profitability, and the internal rate of return.

MANAGEMENT STRATEGY

The term management strategy is used here synonymously with policy making at the top management level. Throughout this paper, topics of a policy nature have been discussed without explicitly so stating. In this section, however, the view is policy making at the top level. The policy-making area is particularl-y important because it has been difficult to find suitable constructs with which to describe this organizational activity adequately. As far as the learning curve is concerned, Abernathy and Wayne [2] and Conley [36] have done most of the work in this area.’

Conley [36] describes the use of a modified version of the learning curve called the experience curve. The experience curve is utilized to make major decisions in the marketing area which ultimately affect manufacturing operations significantly. The major assumption made is that the manufacturer who has produced the most units probably has the lowest unit cost due to learning. The im- plication from a strategy viewpoint is that successful firms are those that follow the strategy of achieving market dominance by committing themselves to becoming the largest manufacturer of a product. As manufacturing experience is gained, the lowest unit cost is achieved via the learning curve phenomenon. Then, market dominance becomes a reality by using price as a competitive weapon.

The experience curve ideas as put forth by Conley were tested in another study by Nathanson [73]. Nathanson’s work focused upon the use of price

’Patrick Conley was a vice president of the Boston Consulting Group in 1970. His article [36] reflects many of the ideas found in this group’s publication [22]. Because [22] is not readily available, interested readers are referred to [36].


forecasting as a competitive weapon in the petrochemical industry; he coupled statistical price forecasting with technological forecasting. This article is strongly recommended as further reading since it is the only article known to this writer that expands upon and validates the ideas expounded upon by Conley.

Abernathy and Wayne [2] used the Ford Motor Company’s experience with the Model T and Model A as an example of how the experience curve can be used to explain policy-making behavior. The essential point made in this article is that Ford pursued the learning phenomenon with a passion and succeeded in pricing competition out of the market. The authors point out that this strategy leads to organizational inflexibility and that this led to General Motors becoming the leader in the long run. The tastes of consumers for automobiles changed, and Ford’s policy was such that the organization could not react quickly to this change as they were committed to a single policy of mass-producing a low-priced, no-frills automobile.

Readers are urged to relate the Ford experience (essentially one model) to the point made by Conley [36]. Conley strongly recommends that a balanced port- folio of products greatly reduces the risks of following a mass-production policy. The essential point is that if Ford had had three or four models to sell instead of just the Model T, then they might have lost out on the Model T but would have had other models to support the company’s viability.

SUMMARY OF RECENT TRENDS IN LEARNING CURVE APPLICATIONS

The period 1935-1969 saw the learning curve literature describing applications within a restricted area. Almost all of the literature of this period focused upon military applications followed by industrial applications of a cost-control nature. The industrial applications focused upon the following topics: 1. 2. Parameter estimation. 3.

4. Classical cost control. 5 . Purchasing and bidding functions. One significant departure from this scenario was Hollander’s study of DuPont rayon plants [56] conducted in 1963. Hollander addressed the relationship between technical change, capital investment, and increased productivity due to learning. This significant study has not generated the attention it deserves, as evidenced by the lack of subsequently published studies by other authors.

Then Greenberg [43] in 1969 applied the learning curve concept to occupa- tional accident reporting in the petroleum industry. This is the first study of which this writer is aware that applied the learning curve concept outside the manufacturing environment and that also utilized publicly available data.

Various shapes of the learning curves.

Industrial engineering applications such as setting time standards and incentives.

19791 THE LEARNING CURVE 32 1

Subsequent studies using the learning curve construct as an aid in better understanding items of a public policy nature such as Greenberg’s have yet to be published.

In 1970, the Boston Consulting Group [22] and Conley [36] proposed using a version of the learning curve (experience curve) to assess competition’s manufacturing costs. This data in turn would be compared with internal data and then decisions of a marketing strategy nature would result. The whole point of this work was to make marketing and manufacturing policy for the firm based upon informational forecasts generated with the learning curve.

Morse [70] in 1971 was the next individual to do work of a non-classical learning curve nature. Although Morse’s work is in the accounting area, its, focus is really on accounting philosophy and how some of those concepts can be better achieved by the use of the learning curve to benefit society. Readers will recall that Morse proposed a cost allocation philosophy which would ultimately affect a firm’s earnings per share significantly. This in turn would affect the perceptions of the investing public as to just what the firm’s stock was really worth.

Then Abernathy and Wayne [2], in 1974, described the top management policies of the Ford Motor Company (i.e., Model A and Model T) using the learning curve model. This article is the best published work of which this author is aware that sets the top management policies of a large corporation into perspective using the learning curve as a structural aid for explanatory purposes. Readers are also urged to read Utterback and Abernathy [86].

Clearly, the learning curve construct has been found useful in new areas during the past ten years. The general direction of these new applications focuses to a great degree on the policy-making level of the business firm and on public- or service-related issues. This is in sharp contrast to the classical industrial engineering types of applications that were the focus of attention during the previous three decades. In the following section of this paper, areas for future research will be suggested. Much needs to be done, particularly at the macroscopic level as opposed to the microscopic level of the period 1935-1965.

FURTHER RESEARCH

The potential areas for future research are indeed abundant in the learning curve area. Both the so-called classical areas of learning curve application as well as the newer areas delineated in the previous section need much more attention from academicians as well as practitioners.

Parameter Estimation

Many issues in this general area still require further clarification. The aggregation-disaggregation concept approach of estimating learning curves needs to be validated.

Identification of factors favoring an accelerated rate of learning is another promising area for future research. Within this area focus should be brought to


bear upon the relationship between organizational effort in the planning stage and its effect on the initial K parameter as well as the slope of the learning curve.

Also, it would be useful to know when a process has gone from the startup to a steady-state phase. The relationship between the two phases, the cycle time, and the production run time needs to be clarified. Managerial interest in the product needs to be assessed from the standpoint of its effect on plateauing. In other words, does a lack of interest on the part of management create plateauing?

The research areas cited above should be directed as much as is possible to estimating learning curves in advance of producing the first unit. Management needs to know prior to making significant commitments of capital and talent whether or not these commitments are going to be worthwhile. In addition, the areas of product costing, delivery commitments, scheduling, and raw materials acquisitions would all benefit from more accurate learning curve estimates.

The Learning Curve and Aggregate Planning

With the sole exception of Ebert’s recent work [MI, the aggregate planning models that proliferate the literature do not include the learning curve as a com- ponent. Given a steady work force, as learning takes place output increases due to increases in productivity per worker. An aggregate planning model that incor- porates the learning phenomenon would yield more realistic information for managerial planning purposes. The significance of new aggregate planning models becomes increasingly apparent when one starts to consider the effect of better information on areas such as inventory policy.

Learning

Labor learning has received the most attention in the literature for obvious reasons. There are elaborate techniques designed to set labor standards. Organizational learning and one of its major subsets, technological learning, have frequently been mentioned in the literature. Yet, studies to “factor out” these various types of le,arning from a single learning curve have not been pursued. A better understanding of the contributions of each kind of learning is needed. As one example for further study in this area, consider that it has already been established that different departments within the same firm have different learning curves. How much of this is due to different organizational ar- rangements between departments?

Within this area falls the phenomenon of workers and an organization forgetting, to a degree, how to manufacture a product due to production interruptions. The phenomenon of relearning needs more attention in the context of the various components of learning. Are the relearning phenomenon and the idea of “organizational adaptation” mentioned in [9] related?

Financial Areas and the Learning Curve

In [52] the question is raised as to whether or not it is best to modernize ex- isting facilities or build new ones. Additionally, the comment is made that the


relationship of the learning curve to depreciation and capital investment suggests that depreciation has been more than adequate to provide replacement capital. The role of the learning curve as a managerial decision-making aid in these two areas needs t6 be explored and clarified.

Morse [70] proposed a cost allocation learning curve model. The model, if implemented, would have the tendency to change the reported earnings of a firm. A fruitful area for future research would be to establish to what degree allocating cost on this basis would change the value investors place on a firm.

The area of human resource accounting as proposed in [72] falls into the financial category. Studies could be done to examine the effects of reporting the learning potential of human resources as an asset. The value of this learning potential would be projected by the use of learning curves.

Another question that needs answering is to what degree can the learning curve be useful in providing information that would prevent over-building of plant capacity.

The Learning Curve and Marketing Strategy

Every product has a life cycle. One unanswered question is how is the learning curve related to the product life cycle? For example, is the phenomenon of plateauing in the learning curve related to the mature phase of the product life cycle? If there is a relationship here, the implications for strategic planning are enormous. The work of Utterback and Abernathy [86] is certainly a beginning here.

The Boston Consulting Group’s studies [22] deserve further attention. Nathanson’s article [73] touches upon the use of various indices (deflators) used by this group. Further validation of Nathanson’s work is needed.

The use of the learning curve as a technological forecasting construct was cited by the 1967 Organization for Economic Cooperation and Development as the most neglected area. Since then, a new journal, Technological Forecasting and Social Change, has been born. This journal has served as a useful vehicle for the dissemination of embryonic knowledge in this area.

Non-Manufacturing Applications of the Learning Curve

Greenberg [43] moved the learning curve into the accident-reporting area of the federal government. This work should be extended further into the formation of public policy on industrial accident reporting, particularly with respect to the way accidents are reported. The end result would hopefully lead to better accident-prevention policies. The impingement effects of policy changes on workmen’s compensation plans, civil suits, private insurance, and legal fees are all areas for further investigation.

1. Two other areas mentioned by Greenberg for future research are: The use of learning curves to set accident control limits (i.e., like quality control limits) within a firm.


2. The possibility that the automotive accident experience can be described by the learning curve. Extension of the work reported by Kneip [65] on warranty maintenance ser-

vice seems in order. Kneip used the learning curve to relate service calls to production experience. The implications of extending this work so that warranty policies are changed based on manufacturing experience has not been investigated. Also, relating this work to an overall marketing policy has yet to be done.

The Management-Economics Interface

The quality of the interface between the management profession and the applied economist could be improved. The professional economist can undoubtedly help managers to better understand the learning curve phenomenon. On the other hand, managers need to be receptive to help from economists. Two very good articles written by Hirshleiffer [54] and Preston and Keachie (761 help to bridge the gap between how an economist looks at a firm’s cost functions and the learning curve. Pegels [74] is also recommended.

The Learning Curve and Management Strategy

Conley [36] and Abernathy and Wayne [2] have done the pioneering work in applying the learning curve to top management policy making. Conley’s approach focuses on marketing policy whereas Abernathy and Wayne focus primarily on manufacturing policy. In effect, readers have only these two studies available to themselves in this crucial top management area. The surface has hardly been scratched. And, in this writer’s view, this area represents one of the more promising areas for future research for the learning curve.

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THE LEARNING CURVE: HISTORICAL REVIEW AND ...s3.amazonaws.com/prealliance_oneclass_sample/qolgKJ4BBk.pdf1 10 100 1000 A basic introduction to learning curves is provided by Carlson