The impact of quality on learning

Ž .Journal of Operations Management 15 1997 181–191

The impact of quality on learning

George Li ), S. RajagopalanSchool of Business Administration, UniÕersity of Southern California, Los Angeles, CA 90089, USA

Received 6 March 1996

Abstract

The strategic importance of learning curves has been recognized for a long time both in industry and in academia, butlittle is known about the huge difference in rates at which different firms learn. Recent theoretical studies and anecdotalevidence from Japanese manufacturing firms suggest that quality-related activities may be one major factor explaining the

Ž .difference in learning rates. When the impact of quality on learning is considered, three important questions arise: 1 HowŽ .well does cumulative output of defective or good units explain learning curve effects? 2 Do defective units explain learning

Ž .curve effects better than good units? 3 How should cumulative experience be represented in the learning curve model whenthe quality level may have an impact on learning effects? This paper presents, to our knowledge, the first empirical studyaddressing these questions. Using time series data from two manufacturing firms, we find that cumulative output of defectiveor good units is statistically significant in explaining learning curve benefits. However, defective and good units do notexplain learning curve effects equally as is implicitly assumed in traditional learning curve models. In particular, defectiveunits are statistically more significant than good units in explaining learning curve effects. q 1997 Elsevier Science B.V.

Keywords: Quality; Productivity; Learning; Statistical analysis

1. Introduction

The experience gained over time, as a firm pro-duces more units of a product, leads to reductions inthe unit cost of production, owing to reductions inlabor and machine hours, material costs, etc. Thisphenomenon is referred to as a learning curve, anexperience curve or a progress curve. Learning curves

Žhave been documented in several industries Yelle,.1979 and have many important applications in areas

such as pricing, anticipating behavior of rivals, plan-ning, forecasting, marketing and manufacturing strat-

Ž .egy Argote et al., 1990; Kantor and Zangwill, 1991 .However, little is known about why there is consid-

) Corresponding author.

erable variation in the rates at which different firmsŽ .learn Dutton and Thomas, 1984 .

The studies of Japanese manufacturing firms byŽ . Ž .Garvin 1988 and Abernathy et al. 1981 suggest

that quality-related managerial activities may be animportant factor in explaining the significant differ-ence in learning rates across firms. When defectlevels are high, a firm may devote additional effortto investigate the cause of defects and this leads toadditional knowledge of the process which, in turn,increases both quality and productivity. Thus, thedefective output may be seen as a trigger or surro-gate for the improvement efforts which result in theinduced learning effects discussed by Dutton and

Ž .Thomas 1984 .Ž .In a similar vein, Fine 1986 argues that ‘‘firms

choosing to produce high quality products will learn

0272-6963r97r$17.00 q 1997 Elsevier Science B.V. All rights reserved.Ž .PII S0272-6963 97 00003-X

( )G. Li, S. RajagopalanrJournal of Operations Management 15 1997 181–191182

faster or go down a steeper experience curve thanŽfirms producing lower quality products where qual-

ity is defined as degree of conformance to design. Ž .specification, not the quality of design ’’. Fine 1986

presents a quality-based learning curve model whereexperience gained over time is a function of thecumulative number of good units produced ratherthan cumulative total production. Extending this line

Ž .of research, Kini 1994 develops a quality-basedlearning curve model and shows that quality in-creases over time if defective units have greaterimpact than good units on learning and vice versa.

So, it is useful to obtain empirical evidence toanswer the following questions that arise from the

Ž .above discussion: 1 Is cumulative output of defec-tive or good units statistically significant in explain-

Ž .ing learning curve effects? 2 Do defective andgood units help explain learning effects equally and,

Ž .if not, which one has greater explanatory power? 3How can traditional learning curve models be modi-fied to take into account the impact of quality levelson learning?

While there is some interesting theoretical workŽ .Fine, 1986; Kini, 1994 and some anecdotal evi-dence about the impact of quality-related activitieson learning, there has been no formal empirical studyof the issues raised above. To our knowledge, thispaper presents the first formal test of the questionsposed above. We collected about three years of dataon quality levels, production, and labor hours fromtwo manufacturing firms. Results from the studyindicate that both defective and good units explainlearning curve benefits, represented here by reduc-tion in unit labor hours. Also, it may be better torepresent experience as a weighted linear combina-tion of cumulative defective output and cumulativegood output, unlike in conventional learning curve

Ž .models Yelle, 1979, Badiru, 1992 which use cumu-lative output as a proxy for experience.

The rest of the paper is organized as follows. InSection 2, the hypothesized linkages between qualityand learning are reviewed. Then there is a discussionof traditional and quality-based learning curve mod-els, where a quality-based model implies that cumu-lative defective or good output, instead of cumulativeoutput, is used as an independent variable in explain-ing learning curve benefits. Next, the hypotheses arestated and a search procedure is described that is

used to estimate the parameters of a non-linear learn-ing curve model. In Section 3, our data set is de-scribed. Section 4 reports the empirical results. Fi-nally, in Section 5, a number of important implica-tions of our empirical findings are discussed.

2. Quality and learning and hypotheses

2.1. Theoretical linkages between quality and learn-ing

The earliest work that captures the linkage be-tween quality and learning, albeit indirectly, is theframework of induced and autonomous learning de-

Ž .veloped by Levy 1965 and extended by Dutton andŽ .Thomas 1984 . Autonomous learning refers to direct

labor learning through a process of ‘‘learning bydoing’’ or ‘‘practice makes perfect’’. It involvesautomatic improvements that result from sustainedproduction and reaping its benefits requires little

Ž .conscious managerial effort Fine, 1986 . Most stud-ies in the learning curve literature which use cumula-tive output as a proxy for production knowledge or

Ž .experience see the survey by Yelle, 1979 implicitlyfocus on autonomous learning. In contrast to au-tonomous learning, induced learning represents theresult of deliberate actions by management, workers,and engineers to improve the quality and efficiencyof the production process. Examples of induced-learning activities are process improvement projects,defect prevention efforts and quality circles.

The first formal study of the interaction betweenquality-related activities and learning effects is found

Ž .in the pioneering work of Fine 1986 , where hepoints out that quality-related activities can lead todiscovery of ‘‘bugs’’ or inefficiencies in the produc-tion process, thus providing opportunity for learning.Fine models quality-based learning effects by usingcumulative good output as a proxy for knowledge.

Ž .Kini 1994 proposed a quality-based learning modelwherein knowledge or experience is represented by aweighted linear combination of cumulative output ofgood and defective units. He shows that the optimalpath over time of variables such as quality, price,and production depends on the relative weights ofcumulative good and defective output. Therefore, itis important to establish empirically the relative

( )G. Li, S. RajagopalanrJournal of Operations Management 15 1997 181–191 183

weight of good and defective units in explaininglearning curve effects.

Ž .Recent work by Adler and Clark 1991 uses theinduced and autonomous learning framework to in-vestigate the impact of labor training and engineer-ing changes on learning, although the quality vari-able is not considered. Rather than consider the

Ž .impact of quality on learning, Badiru 1995 exam-ined the effect of learning on product quality.

Ž .Schmenner and Cook 1985 , though not focusing onlearning effects, found that the most productive plantswere also those that paid the most attention to qual-ity in a study of productivity differences at 95 largeNorth Carolina plants.

The quality-based learning curve models proposedhere capture, to a certain extent, both autonomousand induced learning. Autonomous learning is repre-sented by cumulative production output, which is afunction of production levels over time. Inducedlearning is captured indirectly by the conformancequality level. When defect levels are high, a firmmay devote additional effort to understand the causeof the defects and this may lead to additional knowl-

Ž .edge of the process. Also, Garvin 1988 discusses,in detail, the common sources of improvement forquality and productivity. Lower quality implieshigher scrap and rework, which in turn means wastedmaterial, labor, equipment time, and other resources.

While it is possible to represent induced learningby carefully measuring the effort devoted to activi-ties such as quality improvement teams, quality train-ing, and employee empowerment, historical data onthese activities are not always carefully documentedor maintained. Also, there are differences in thenature of these activities across firms and a lack ofstandardization in measurement. Therefore, we de-cided to use defect levels as a surrogate for the effortdevoted to process improvement. This is also consis-tent with the analytical results in Rajagopalan and LiŽ .1995 , where it is shown that the optimal investmentin process improvement effort is high when defectlevels are high and vice versa.

2.2. Traditional and quality-based learning modelsand hypotheses

The learning curve has been formulated in anumber of ways. One of the representations of the

Ž .learning curve model, discussed in Berndt 1991 , isas follows:

ln H s ln H qb ln C qk ln P q« 1Ž .t 0 t t t

where ln is the natural logarithm, t is calendar timein months, H is the direct unit labor hours at time t,t

H is the direct unit labor hours at time zero, C s0 t

C qP , the cumulative output at time t, P ist y 1 ty1 t

production level at time t, b is the learning rate, k isthe parameter measuring the effect of scaleeconomies, and « is a stochastic disturbance termt

reflecting the inherent randomness in the labor hourreduction process. Typically, it is assumed that « ist

an independent, identically and normally distributedrandom variable with zero mean and constant vari-ance.

In this model, cumulative output is used as aproxy for experience and the production level term isused to estimate scale economies. Some of the em-pirical models used in practice do not contain a term

Ž .for scale economies, but Womer 1979 and BerndtŽ .1991 have provided a good rationale for why it isimportant to include this term. The learning curveeffect exists if b-0, that is, direct unit labor hoursdecrease with increasing cumulative output. Simi-larly, there exist scale economies if k-0, that is,direct labor hours decrease with increase in the

Ž .production level. Globerson and Levin 1995 haverecommended using ‘‘equivalent number of units’’,instead of cumulative output, as a proxy for experi-ence, where equivalent number of units are equal tofinished product units plus in-process inventory units.

Ž .Fine 1986 proposed the following quality-basedlearning model:

ln H sf qf ln N qf ln P q« 2Ž .t 0 1 t 2 t t

where f s ln H , H is direct unit labor hours at0 0 0

time 0, N sN qP q , the cumulative goodt ty1 ty1 ty1

output at time t, q is the conformance quality levelt

at time t, i.e. the proportion of the products that arenon-defective. The other variables and parameters

Ž .are similar to those in Eq. 1 .This model is different from the traditional learn-

ing curve model in that the quality variable is incor-porated by replacing cumulative production volumewith cumulative good production volume. Using this

Ž .model. Fine 1986 derived many interesting theoret-ical results concerning optimal quality, pricing and


production policies. This model considers the impactof good units on learning, but there have been noempirical studies to determine whether good unitsexplain learning curve effects. Therefore, we posit

Ž .our first hypothesis H1 that cumulative good outputis significant in explaining learning curve benefits,

Ž .i.e. in the learning model in Eq. 2 , f -0.1

Next, we consider the power of defective units inexplaining learning curve effects. Anecdotal evi-dence suggests that high levels of defects are often atrigger for investigating the cause of defects. Thisinvestigation leads to a better understanding of theproduction process and may result in process im-provements which reduce costs and improve quality.Thus, as discussed earlier, defective units may be agood surrogate for defect prevention efforts, qualitytraining, quality circles, etc. used to achieve betterunderstanding of processes resulting in both higherquality and productivity. We propose the following

Ž .model, complementary to the one in Fine 1986 :

ln H sg qg ln D qg ln P q« 3Ž .t 0 1 t 2 t t

Ž .where D sD qP 1yq , is the cumulativet ty1 ty1 ty1

output of defective units at time t. The other vari-Ž .ables and parameters are similar to those in Eqs. 1

Ž . Ž .and 2 . The learning model in Eq. 3 is differentŽ .from the model in Eq. 2 because cumulative defec-

tive output is used as a proxy for experience. How-ever, there have been no empirical studies to deter-mine whether defective units can adequately explainlearning curve effects. This leads to our second

Ž .hypothesis H2 that cumulative defective output issignificant in explaining learning curve benefits, i.e.

Ž .in the learning model Eq. 3 , g -0.1

In the traditional learning curve model, defectiveunits and good units are assumed to be equivalent inexplaining learning curve effects. Therefore, cumula-tive production volume is used as a proxy for knowl-edge or experience. If defective and good units arenot equivalent in explaining learning curve effects,then it is inappropriate to use cumulative productionvolume as a proxy for knowledge. So, we proposethe following non-linear quality-based learningmodel:

ln H su qu ln s D qp N qu ln P q« 4Ž . Ž .t 0 1 t t 2 t t

where s and p represent, respectively, the relativeweight of defective and good units in explaining

learning effects. Other variables and parameters areŽ . Ž .similar to those in Eqs. 2 and 3 .

Ž .The learning curve model in Eq. 4 generalizesthe traditional learning curve model by consideringthe possibility that defective and good units may nothave the same impact on learning. For example, ifssps1, this model becomes the traditional learn-

Ž .ing curve model in Eq. 1 , with cumulative produc-tion output as a proxy for knowledge. There is noempirical evidence investigating the relative impactof good and defective units on learning.

It is also important to find out which of these twoweights, s and p , is greater for the following

Ž .reason. Fine 1986 implicitly assumes that ss0Ž .and p)0 in his model. Kini 1994 , generalizing

Ž .the model in Fine 1986 , shows that the optimalbehavior over time of quality, production and pricedepends on the relative values of s and p . Inparticular, he shows that quality improves over timeif s)p and quality decreases over time if s-p .

Ž .So, our last hypothesis H3 is that cumulative outputof defective and good units do not explain learningcurve benefits equally and, in particular, cumulativedefective output explains learning curve benefits bet-ter than cumulative good output, that is, in the

Ž .learning model in Eq. 4 , s)p .

2.3. A search procedure

Most studies in the learning curve literature use aŽ .linear model such as in Eq. 1 . For the linear

learning curve model, parameters can be estimatedeasily using the least squares procedure. However, in

Ž .this paper, the model described in Eq. 4 is non-lin-ear and thus the least squares procedure cannot beapplied directly to estimate each parameter. There-fore, we use the following search procedure to esti-

Ž .mate each parameter in Eq. 4 .The procedure is a maximum likelihood estima-

tion procedure that searches each pair of possibleŽ .values of s and p in the domain 0, ` to find the

estimates of s and p that maximize the likelihoodfunction. The procedure is based on the simple ob-servation that, given any specific values of s and p ,

Ž .the model in Eq. 4 is a linear learning curve modelwhich can be easily estimated using the least squaresprocedure, and for which the value of the likelihood


function can also be calculated easily. In particular,the procedure begins with a search over values of s

and p using increments of 0.05 to identify thesub-area in which the likelihood function may reacha maximum. Once a sub-area is identified, the proce-dure searches the sub-area using increments of 0.001to find the maximum value of the likelihood func-tion.

Theoretically speaking, this procedure is equiva-lent to nonlinear search procedures that vary all theparameters simultaneously, but it is computationallyeasier to implement. This procedure is also similar tothe scanning procedure for maximum likelihood esti-

Ž .mation in works by Goldfeld and Quandt 1972 andŽ . Ž .Dhrymes 1966 . Argote et al. 1990 used this pro-

cedure successfully to estimate the knowledge depre-ciation factor in a non-linear learning curve model

Ž .similar to the one in Eq. 4 . Nevertheless, thisprocedure may lead to understatement of the stan-dard errors of the other coefficients and the corre-sponding overstatement of the t-statistics. Therefore,we measure the significance of s and p using thelikelihood ratio test.

3. Data

We collected data from two leading manufactur-ing firms, referred to as firm A and firm B. Firm Ais labor-intensive and produces tire tread by extrud-ing and pressing rubber. Firm B is more labor-inten-sive and manufactures a variety of medical instru-ments used in critical-care units in hospitals to moni-tor vital signs. Therefore, the quality of the productin firm B is a critical factor. During the period forwhich data were collected, the two firms were mak-ing serious efforts to reduce their outgoing productdefect rate. Quality-related activities at the firms

Fig. 1. Plots of direct unit labor hours for firms A and B. The unitfor firm A is 100 lbs of tread; the unit for B is one instrument.

Fig. 2. Plots of the defective output per unit for A and B. The unitfor A is 1000 lbs of tread; the unit for B is 100 instruments.

include labor-training in the use of quality tools,regular preventive maintenance, and application ofstatistical process-control techniques. At the plantmaking tire tread, quality training consists of twohour sessions each month for each employee on arotating basis. In addition, 1-2 h per week is spentdiscussing problems that surfaced during the week inproduction, approaches to solve these problems andresults of previous efforts. The quality training ses-sions are coordinated by an internal team togetherwith an outside consultant. Similar quality-relatedtraining and activities are carried out at the secondplant, but the details are different. In addition, therewere not any major changes in production processesor technologies during this period.

We collected monthly data at firm A for a majorproduct over a period of three years starting from theinitial date of production. The data collected includethe output of tire treads each month, total direct laborhours worked, and a monthly count of the defective

Žoutput by the quality control department see Figs. 1and 2, respectively, for plots of direct labor hours per

.unit and defect rates . This defect rate measure isprecisely the conformance quality measure discussedin this work. At firm B, we collected monthly datafor one major medical instrument over a period oftwo and a half years from the initial date of produc-tion of this product to the end of 1993. The datainclude the number of instruments produced eachmonth, total direct hours used, and a monthly count

Ž .of the number of defective units see Figs. 1 and 2 .

4. Empirical results

In this section, we present the results of statisticalanalysis concerning the four models discussed in


Table 1Tests of the impact of cumulative units on learning for firms A

Ž .and B using the model in Eq. 12Firm ln H b k R n0

a aA 3475 y0.109 y0.138 0.673 34Ž . Ž . Ž .6.474 y6.822 y1.630

a bB 3.32 y0.081 y0.11 0.717 27Ž . Ž . Ž .9.86 y1.75 y1.12

Values in parentheses are t statistics.a Significant at the 1% level, two-tailed test.b Significant at the 10% level, two-tailed test.

Section 2. The implications of these results are dis-cussed in Section 5.

4.1. Learning from cumulatiÕe units

Ž .The model described in Eq. 1 was used to testthe traditional learning effect: direct unit labor hoursdecreases with cumulative output. The parameterswere initially estimated using the least squares pro-

Ž .cedure. Durbin–Watson statistics Berndt, 1991 in-dicated that the regression residuals exhibit no auto-correlation for firm A but strong first-order autocor-relation for firm B. Therefore, we then estimatedparameters for firm B using the Cochrane–Orcutt

Ž .procedure Cochrane and Orcutt, 1949 . The result-Ž .ing Box Pierce Q statistics Box and Jenkins, 1976

confirmed that the remaining residuals approximateda stationary, white noise process. Furthermore, auto-correlation and partial autocorrelation correlogramsrevealed no spikes to suggest any remaining system-atic autoregressive or moving average component inthe residuals. Table 1 provides the estimated coeffi-

Ž .cients in Eq. 1 for firms A and B.As shown in Table 1, the log of cumulative

output, ln C , is negatively correlated with the log oft

direct unit labor hours, ln H , for both firms A andt

B. This indicates that direct unit labor hours decreasewith cumulative output and so, cumulative units helpexplain learning curve effects. The learning rate canbe characterized in terms of the progress ratio, ps2 m, where m is the estimate of coefficient b in Eq.Ž .1 . The progress ratio means that each doubling ofcumulative output leads to a reduction in direct unitlabor hours to a percentage, p, of its former value.The estimated values of b for firms A and B are

y0.109 and y0.08, respectively. Thus, each dou-bling of cumulative output leads to a 7.2% reductionin unit labor hours for firm A and a 5.4% reductionin direct unit labor hours for firm B. The correlationbetween the log of the production level, ln P , andt

the log of the direct unit labor hours, ln H , ist

negative but insignificant, indicating that scaleeconomies have little impact on the reduction ofdirect unit labor hours in both firms A and B.

The finding that direct unit labor hours decreaseswith cumulative output is consistent with many em-

Žpirical findings in the learning curve literature see.Yelle, 1979 .

4.2. Learning from good units

Ž .The model described in Eq. 2 was used to testthe first hypothesis: are good units significant in

Ž .explaining learning curve benefits? As in Eq. 1 , theparameters were estimated initially using the leastsquare procedure. Durbin–Watson statistics indi-cated that the regression residuals exhibit no autocor-relation for firm A but strong first-order autocorrela-tion for firm B. Therefore, we then estimated param-eters for firm B using the Cochrane–Orcutt proce-dure. Table 2 provides the estimated coefficients in

Ž .Eq. 2 for firms A and B.As shown in Table 2, the log of cumulative good

output, ln N , is negatively correlated with the log oft

direct unit labor hours, ln H , for both firms A andt

B. This indicates that direct unit labor hours decreasewith cumulative good output and so, good units helpexplain learning curve effects. The estimated valuesof f for firms A and B are y0.108 and y0.08,1

respectively. Using the progress ratio to characterize

Table 2Tests of the impact of good units on learning for firms A and B

Ž .using the model in Eq. 22Firm f f f R n0 1 2

a aA 3475 y0.108 y0.138 0.673 34Ž . Ž . Ž .6.476 y6.829 y1.631

a bB 3.32 y0.08 y0.11 0.717 27Ž . Ž . Ž .9.86 y1.73 y1.17



the learning rate, we find that each doubling ofcumulative good output leads to a 7.2% reduction inunit labor hours for firm A and a 5.4% reduction indirect unit labor hours for firm B. The correlationbetween the log of the production level, ln P , andt

the log of the direct unit labor hours, ln H , ist

negative but insignificant, indicating that scaleeconomies have little impact on the reduction ofdirect unit labor hours in both firms A and B.

The finding that cumulative output of good unitsexplain learning curve effects supports Fine’s as-sumption. Nevertheless, this does not imply thatdefective output does not impact learning, which is

Ž .implicitly assumed in the model in Fine 1986 .

4.3. Learning from defectiÕe units

Ž .The model described in Eq. 3 was used to testwhether defective output can explain learning curve

Ž .effects. As in Eq. 2 , the parameters were initiallyestimated by the least square procedure. Durbin–Watson statistics indicated that the regression residu-als exhibit no autocorrelation for firm A but strongfirst-order autocorrelation for firm B. Therefore, weestimated parameters for firm B using theCochrane–Orcutt procedure.

Table 3 reports the estimated coefficients in Eq.Ž .3 for firms A and B. As expected, in both firms Aand B, the log of the cumulative defective output, lnD , is negatively correlated with the log of the directt

unit labor hours, ln H . This implies that direct unitt

labor hours in both firms decrease with cumulativedefective output, i.e. defective output is significant inexplaining learning curve effects. The estimated val-

Table 3Tests of the impact of defective units on learning for firms A and

Ž .B using the model in Eq. 32Firm g g g R n0 1 2

a aA 3.71 y0.31 y0.129 0.708 34Ž . Ž . Ž .7.341 y7.475 y1.618

a bB 3.418 y0.14 y0.10 0.737 27Ž . Ž . Ž .10.59 y2.42 y1.27


ues of g for firms A and B are y0.312 and y0.14,1

respectively. Using the progress ratio to characterizethe learning rate, we find that each doubling ofcumulative defective output leads to a 19% reductionin unit labor hours for firm A and a 9% reduction indirect unit labor hours for firm B. However, with thepassage of time, greater experience with the processand the resultant improvement in quality increasesthe time required to double the cumulative defectiveoutput. Therefore, there is less defective output toanalyze and so, opportunities for additional improve-ment in the process decrease. Correspondingly, thelearning rate may also slow down.

Again, the correlation between the log of produc-tion level, ln P , and the log of direct unit labort

hours, ln H , is negative but insignificant. Thist

implies that scale economies have little impact onreduction of direct unit labor hours in firms A and B.

The finding that defective units help explainlearning curve benefits is consistent with anecdotalevidence from Japanese manufacturing. For example,

Ž . Ž .Hayes 1981 and Schonberger 1982 point out thatJapanese firms treat each defect as a ‘‘treasure’’ foruncovering the sources of imperfections in the pro-duction process and for helping workers and engi-neers in learning to improve the production process.The finding is also consistent with state-of-the-artquality management practices in the U.S. Motorola,for example, makes use of each defective unit to findthe ‘‘bugs’’ in the process and then improve the

Ž .process Omachonu and Ross, 1994 .

4.4. Incorporating the quality leÕel in the learningcurÕe model

To test whether cumulative output should be dif-ferentiated based on quality in the traditional learn-ing curve model to capture quality-based learningeffects, we used the search procedure discussed ear-lier to estimate the parameters of the non-linear

Ž .model in Eq. 4 . The results are given in Table 4.At the 10% significance level, the hypothesis

Ž 2ssp is rejected for firm A x s4.76, dfs2,.P-0.1 . At the 7% significance level, the hypothe-

Ž 2sis ssp is rejected for firm B x s5.91, dfs2,.P-0.07 . Therefore, the representation of experi-

ence in the traditional learning curve model ignores


Table 4Ž .Estimated results of model in Eq. 4

2Firm u u u s p R ln L0 1 2

a aA 3.70 y0.31 y0.129 1.00 0.00 0.71 18.5Ž . Ž . Ž .7.33 y7.48 y1.618

a bB 3.41 y0.14 y0.10 1.00 0.00 0.74 25.6Ž . Ž . Ž .10.59 y2.41 y1.27

Values in parentheses are t statistics.ln L is the natural logarithm of the likelihood function.a Significant at the 1% level, two-tailed test.b Significant at the 5% level.

differences in the relative impact of defective unitsand good units on learning.

4.5. Learning more from defectiÕe units

Tables 2 and 3 indicate that the learning model inŽ .Eq. 3 fits the data a little better for both firms A

Ž .and B than the model in Eq. 2 in terms of the valueof R2 and the statistical significance of the estimates

Žof g and f the results in Tables 1 and 2 indicate1 1

that cumulative output and cumulative good outputare equally significant in explaining reductions in

.unit labor hours . This indicates that cumulativedefective output may have a larger correlation withthe reduction in direct unit labor hours than thecumulative good output, thus implying that defectiveunits may explain learning effects somewhat betterthan good units. However, the statistical evidence forthe difference in impact of defective and good unitson learning is not overwhelming. Moreover, we findthat cumulative good output is highly correlated withcumulative defective output. The resulting multi-collinearity problem can bias the estimate of therelative significance of defective and good units inexplaining learning curve effects in a linear regres-sion model, which includes both cumulative goodoutput and cumulative defective output as indepen-dent variables. Therefore, we use the non-linear

Ž .learning curve model in Eq. 4 to investigate whetherdefective or good units are more significant in ex-plaining learning curve effects.

The results of the non-linear learning curve modelŽ .in Eq. 4 were used to test whether more is learned

from defective units or from good units. As shown in

Table 4, the estimate of s for both firms is 1.0 andthe estimate of p for both firms is 0.0. Sinceestimation does not yield a standard error for s and

Ž .p , following Argote et al. 1990 , we use the distri-bution of the likelihood ratio to determine the confi-dence intervals for s and p , respectively. For firmA, the 95% confidence interval for s is roughlyŽ .0.70, 1.30 , and the 95% confidence interval for p

Ž .is about 0, 0.30 . For firm B, the 95% confidenceŽ .interval for s is roughly 0.65, 1.35 , and the 95%

Ž .confidence interval for p is about 0, 0.35 . There-fore, at the 5% significance level, the hypothesissFp is rejected for firms A and B. This providesfurther evidence that defective units are more signifi-cant than good units in explaining learning curveeffects.

5. Discussion

In this paper, we presented an empirical studyinvestigating the impact of quality on learning. Theevidence from the two manufacturing firms indicatesthat both cumulative defective output and cumulativegood output are significant in explaining learningcurve effects. Moreover, the results indicate thatdefective units may be more significant than goodunits in explaining learning effects. The results ofour study raise a number of interesting issues whichare discussed next.

Why is there a difference in the relative impact ofgood and defective units on learning? Also, whymight defective units explain learning curve effectsbetter? We provide some possible answers to thesequestions next. Managers often react to changes inquality levels. If the defective level in a period isvery high, it immediately gets the attention of man-agers and other employees and considerable effort isdirected at identifying the source of defectives. Ifdefect levels continue to be high for a few consecu-tive periods, increased attention is paid and addi-tional resources are devoted to investigate the causeof the defects. Generally, these efforts lead to abetter understanding of the process variables andinteractions which is useful in avoiding such defectsin the future. The better understanding may also lead


Žto process changes which require less labor or lessmachine time or lower-cost material in other con-

.texts .For example, in the plant making tire tread, after

one of the machines produced a high level of defectsin October 1992, the workers developed some re-vised procedures for loading the machine and check-lists for ensuring the completion of all key activitiesbefore running the machine. This reduced the num-ber of defects as well as the average time for loadingthe machine. Similarly, in the plant producing medi-cal instruments, specialized assembly kits and fix-tures were developed and standard operating proce-dures were adopted. This resulted in lower defectsand eliminated wasted time searching for parts, fix-

Ž .tures, etc. Shingo 1992 mentions similar benefitsfrom adopting standard operating procedures andchecklists. Also, both firms believe that moralepicked up owing to better quality and this improvedproductivity too.

A good illustration of this phenomenon is givenŽ .by Shingo 1992 relating to the machining of preci-

sion adjustment screws at a plant making opticalmicroscopes. He found that the screws were takenoff the lathe between intermediate and final machin-ing operations, and a three-pin gauge was used tomeasure the effective diameter in order to determinehow much material remained to be taken off. The actof removing and remounting screws on the lathe inorder to measure them itself generated defects. Hesuggested the use of a special gauge and limitswitches to stop the machining once the allowabledimensions were achieved. This reduced defects tozero and also eliminated the measuring operation,thus increasing labor and machine productivity.

There is another implication of the finding thatdefective and good units do not explain learning

Ž .curve benefits equally. Kini 1994 proposed a dy-namic learning curve model, similar to the model in

Ž .Eq. 4 , where experience is represented by aweighted linear combination of the cumulative out-put of defective and good units. He showed thatwhen the weight of cumulative defective output is

Ž .greater less than the weight of cumulative goodoutput in the equation, the optimal quality level is

Ž .increasing decreasing over time. Our empirical re-sults indicate that defective units are more significantin explaining learning curve benefits. So, our results

provide some empirical support for the condition inKini’s model that is required for quality to improveover time.

As quality improves over time, do our resultsimply that the learning rate will slow down? whenthe first units of a product are produced, there islimited experience with the process and so defectlevels may be high. As pointed out earlier, this leadsto analysis of the causes of defects and better under-standing of the process. Over time, defect levels godown as the process is better understood and thismay have the following effects. First, because de-fects are less frequent, the opportunities for learningfrom analysis of these defects are also fewer. Sec-ond, managers probably pay less attention to processimprovement as quality levels and experience withthe process are higher. Also, less effort may bedirected because there are diminishing marginal re-turns from the effort directed at process improve-

Ž .ment owing to the Pareto principle Juran, 1979 .According to this principle, a few root causes con-tribute the most to the inefficiencies or ‘‘bugs’’ inthe process and managers, technicians and workersfirst identify and eliminate these causes. Identifyingand eliminating each of the other causes may requirethe same effort, but they lead to smaller improve-ments in the process as there are more such causeswith smaller individual contributions to learning. Forthese reasons, learning may take place at a slowerrate over time, which is consistent with the results of

Ž .empirical studies of learning curves Yelle, 1979 .Our results do not imply that defective units cause

reduction in labor hours or that intentionally produc-ing defectives results in learning curve benefits. Ifdefects are produced intentionally, then the cause ofthe defects is known and no new knowledge isacquired. A firm does not intentionally determine thelevel of defectives to be produced, instead it deter-mines the level of effort directed to quality improve-ment and quality assurance. This effort, as discussedearlier, may be a function of quality levels and thecurrent level of experience with the process. Also, afirm may deliberately experiment with a process,aware that the output may turn out to be defective.This is an integral part of the continuous improve-ment philosophy.

Thus, this empirical study supports Fine’s argu-ment that learning is the ‘‘bridge’’ between quality


Ž .improvement and productivity increase Fine, 1986 .That is, quality-related activities can lead to discov-ery of ‘‘bugs’’ or inefficiencies in the productionprocess, thus providing opportunities for learningand gaining more knowledge about the productionprocess. This increased knowledge leads to increasein productivity, represented in this study by reduc-tion in unit labor hours. The findings help corrobo-

Ž .rate the observation of Deming 1982 that qualityand productivity are not to be traded off against eachother; instead, productivity increases follow fromquality improvement efforts.

The study also provides some initial evidence thatcumulative production volume is not the only vari-able to be considered in explaining learning curvebenefits, as is implicitly assumed in many economicmodels deriving implications of the learning curve

Ž .effect Spence, 1981 . In fact, Dutton and ThomasŽ .1984 state that predictions of learning curve bene-fits using only cumulative volume as a policy vari-able have proven to be unreliable. Furthermore, Abell

Ž .and Hammond 1979 aptly note that, ‘‘Experiencedoes not cause cost reductions but rather provides anopportunity that alert management can exploit.’’

While our results provide a number of insights, afew caveats are in order. Firstly, this study involvesa small sample of only two manufacturing firmsthough the data are over a 2–3 year period. Sec-ondly, the difference between defective and goodunits in explaining learning curve effects was notoverwhelmingly significant. Thirdly, a more compre-hensive empirical study that considers, in detail,various process improvement activities would pro-vide deeper insights into the firm-level variablesinfluencing learning rates.

Acknowledgements

We are grateful to three anonymous referees andan associate editor for helpful comments on earlierversions of the paper.

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The impact of quality on learning