The Effects of Six Sigma on Corporate Performance- An Empirical Investigation

8/10/2019 The Effects of Six Sigma on Corporate Performance- An Empirical Investigation

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Journal of Operations Management 30 (2012) 521532

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Journal ofOperations Management

j ournal homepage: www.elsevier .com/ locate / jom

The effects ofSix Sigma on corporate performance: An empirical investigation

Scott M. Shafer a,, Sara B. Moeller b,1

a Wake ForestUniversity, Schools of Business,Winston-Salem, NC 27109, United Statesb University of Pittsburgh, Katz Graduate Schoolof Business andCollege of Business Administration, Pittsburgh, PA 15260,United States

a r t i c l e i n f o

Article history:

Received 22 July 2011

Received in revised form

13 September 2012

Accepted 18 October 2012

Available online 29 October 2012

Keywords:

Six sigma

Event study

Process improvement

Corporate performance

a b s t r a c t

The purpose ofthis study is to investigate the impact ofadopting Six Sigma on corporate performance.

Although there is a fairly large and growing body ofanecdotal evidence associated with the benefits of

implementing Six Sigma, there is very little systematic and rigorous research investigating these benefits.This research extends previous research in several important ways including utilizing a sample of84 Six

Sigma firms that represent a wide variety ofindustries and firm characteristics, utilizing rigorously con-

structed control groups to ensure the validity ofour comparisons and conclusions, and investigating the

impact ofadopting Six Sigma on corporate performance over a ten year period. To carry out this investi-

gation, the event study methodology is employed. The ten year period consists ofthree years prior to Six

Sigma implementation, the event year corresponding to the year Six Sigma is adopted, and six years post

Six Sigma implementation. To assess the impact ofadopting Six Sigma on corporate performance we uti-

lize commonly used measures including Operating Income/Total Assets (OI/A), Operating Income/Sales

(OI/S), Operating Income/Number ofEmployees (OI/E), Sales/Assets (S/A), and Sales/Number ofEmploy-

ees(S/E). The sample Six Sigma firms are compared to different benchmarksincluding the overall industry

performance and to the performance ofcarefully selected portfolios of control firms. The results ofthe

study indicate that adopting Six Sigma positively impacts organizational performance primarily through

the efficiency with which employees are deployed. More specifically, enhanced employee productivity

results were observed in both static analyses that assessed the performance of the sample Six Sigma

firms relative to their control groups at discrete points in time and dynamic analyses of the Six Sigma

firms rate ofimprovement relative to the rate ofimprovement oftheir control groups. Benefits in termsofimproved asset efficiency were not observed. Finally, there was no evidence that Six Sigma negatively

impacts corporate performance.

2012 Elsevier B.V. All rights reserved.

1. Introduction

The Six Sigma methodology was created by Motorola in the mid

1980s. Over time it has evolved into a comprehensive approach for

improving business performance. Key elements of the Six Sigma

approach include a clear focus on the customers needs, the use

of performance metrics, a focus on improving business processes

often through the reduction of inherent variation in the processes,

clearly defined process improvement specialist roles, the use ofdata-driven and highly structured problem solving methodologies,

and ultimately the generation of tangible business results (Hahn

et al., 1999; Linderman et al., 2003; Schroeder et al., 2008). Pande

et al. (2000, p. xi) provide a representative definition of Six Sigma

as:

Corresponding author. Tel.: +1 336 758 3687.

E-mail addresses: [email protected] (S.M. Shafer), [email protected]

(S.B. Moeller).1 Tel.: +1 412 6480137.

A comprehensive and flexible system for achieving, sustain-

ing, and maximizing business success. Six Sigma is uniquely

driven by close understanding of customer needs, disciplined

use of facts, data, and statistical analysis, and diligent attention

to managing, improving, and reinventing business processes.

SixSigmais a particularlytimely topic andappears to be gaining

momentum in practice (Linderman et al., 2003; Schroeder et al.,

2008). Perhaps one factor driving the current popularity of Six

Sigma is the growing body of anecdotal evidence touting the ben-

efits high profile organizations have reported from their Six Sigma

initiatives. For example, in the three years ending in 2001, GE esti-

mated that it saved $8 billion as a result of its Six Sigma initiatives

(Arndt, 2002). In the following year, GE budgeted $600 million for

SixSigmaprojectsand targeted an additional $2.5 billion in savings.

As another example, Bank of America claimed benefits in excess

of $2 billion and increased customer delight by 25% in less than

three years through its Six Sigma initiatives (Jones, 2004). Impor-

tantly, Bankof Americasexperiencedemonstratesthe applicability

of Six Sigma beyond traditional manufacturing processes. Indeed,

Honeywell found that the average savings it achieved from service

0272-6963/$ seefrontmatter 2012 Elsevier B.V. All rightsreserved.

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522 S.M. Shafer,S.B. Moeller / Journal of Operations Management 30 (2012) 521532

projects were double that of manufacturing projects (Bossidy and

Bonsignore,1999). Motorola,the inventor of theSix Sigmamethod-

ology, estimated that over the 20 plus years it has deployed Six

Sigma it hasdocumented savings in excess of $20billion (Motorola,

2011). Six Sigma has also been credited as an important contribu-

tor to its winning the Malcom Baldrige Award for Quality in 1988

(Hahn et al., 1999).

Although there is fairly large and growing body of anecdotal

evidence associated with the benefits of implementing Six Sigma,

there is very little systematic and rigorous research investigating

these benefits. Linderman et al. (2003) argue that although Six

Sigma has hada substantial impact on industry,the academic com-

munity lacks theory as a basis for research on Six Sigma. Antony

(2004) agrees andnotes that the despite the huge impact Six Sigma

has had on industry, the academic community lags behind in its

understanding of it. Schroeder et al. (2008) further argue that

research is needed to determine the impact Six Sigma has on per-

formance improvement.

The purpose of this study is to investigate the impact adopting

Six Sigma has on corporate performance. To accomplish this objec-

tive we studythe performance of organizations thathave publically

announced or have received other publicity about their adoption

of Six Sigma. Beyond providing a clear adoption date, such public

disclosures may also serve as a proxy regarding the organizations

commitment to Six Sigma in a similar fashion tothe way Hendricks

and Singhal (1997) usedquality award winners as a proxy foreffec-

tive TQM implementation.

The results of the study indicate the adoption of Six Sigma posi-

tively impacts organizational performance primarily through the

efficiency with which employees, but not assets, are deployed.

There is no evidence that Six Sigma negatively impacts corporate

performance. In addition,the results suggest thatbetter performing

firms adopt Six Sigma and they continue their performance advan-

tage after adoption. Furthermore, the performance advantage for

the Six Sigma firms in terms of employee productivity tended to be

largerafter adopting SixSigmaand tendedto increase as additional

experience was gained with Six Sigma. The benefits of adopting

Six Sigma were observed in both the static analysis that assessedthe performance of the sample Six Sigma firms at discrete points

in time and the dynamic analysis of the Six Sigma firms rate of

improvement on many different benchmarks.

This research extends previous research in several important

ways. First,we evaluate a variety of differentbenchmarks to ensure

that the benchmark choice is not driving the results. At one end of

the spectrum of benchmarks, we take a nave viewpoint and use an

industryadjustedperformanceof oursample SixSigma firms. At the

other end of the spectrum, we follow Barber and Lyon (1996) and

compare a sample Six Sigmafirms performance to the performance

of theclosest matched firmand a portfolioof control firms matched

to it on the basis of industry, year, and similar past performance.

On all of the benchmarks, we do many robustness tests including

when and how we match the sample firm to the benchmark andacross all of these variations, our results are consistent.

Second, we investigate the impact of Six Sigma on operating

performance over a ten year period. Investigating the long-term

effects of adopting Six Sigma addresses important gaps in the

literature. To carry out this investigation, the event study method-

ology is employed. The ten year period consists of three years

prior to Six Sigma implementation, the event year corresponding

to the year Six Sigma was adopted, and six years post Six Sigma

implementation. Pre-implementation performance data is used for

performance matching Six Sigmasamplefirms withcontrol firmsas

well as to investigate the role past firm performance plays in moti-

vating firms to adopt Six Sigma. A six-year post-implementation

period is used given an expected lag between Six Sigma imple-

mentation and the realization of performance benefits. Previous

research has indicated a two and a half year or longer lag between

implementing total quality management(TQM) and improved per-

formance (GAO, 1991; Powell, 1995). Likewise, Hendricks and

Singhal (2001a, b) suggest a three to five year period to implement

an effective TQM program. The ten year period was also chosen

so that short-term and longer-term patterns in the performance

of the sample Six Sigma firms could be investigated. For example,

one of the most interesting results observed was that the Six Sigma

firms outperformed their matched portfolios in year 3 intermsof

Operating Income/Total Assets (OI/A), Operating Income/Number

of Employees (OI/E), and Sales/Number of Employees (S/E), then

experienced a significant decline in performance prior to adopting

Six Sigma on these three measures, and finally exhibited a quick

rebound in year +1 upon adopting Six Sigma.Likewise, as an exam-

ple of longer term patterns, the performance advantage for the

Six Sigma firms in terms of employee productivity tended to be

larger after adopting Six Sigma andtended to increase as additional

experience was gained with Six Sigma.

Third, beyond extending the research investigating the impact

of SixSigmaon firmperformance, an additional contributionof this

research is to provide performance benchmarks for organizations

that have adopted or are considering adopting Six Sigma. Also, the

inclusion of commonly used measures of corporate performance

including OI/A, Operating Income/Sales (OI/S), OI/E, Sales/Assets

(S/A), and S/E facilitate comparisons with previous research.

This paper is organizedas follows. Section 2 reviewsthe existing

empirical research related to process improvement methodologies

and firm performance. Section 3 provides the theoretical devel-

opment for Six Sigmas impact on corporate performance, our

research hypotheses, and the performance variables included in

the study.Following this, our research methodology is discussed in

Section 4. Ourempirical results are presentedand discussedin Sec-

tion 5. Finally, the paper is concludedin Section 6 with a discussion

of limitations and avenues for future research.

2. Review of empirical evidence of quality and process

improvement initiatives on corporate performance

While Six Sigma is the latest process improvement methodol-

ogy,the influence of earlier process improvement methodologies in

its development, particularly TQM and JIT/lean, are readily appar-

ent. In this section we critically review the empirical research

investigating process improvement methodologies on corporate

performance in order to understand what has been studied and

then based on this understanding highlight the gaps in the litera-

ture addressed by the present study.

While there is a substantial body of empirical research

investigating quality and process improvement initiatives on cor-

porate performance, rigorous research investigating the impact

of Six Sigma on corporate performance has been limited (Foster,

2007). This is supported by observing that only two of the 23research contributions encountered in the literature review for

this study investigated the impact of Six Sigma on corporate

performance. Approximately half the studies investigating the

impact of various process improvement approaches on corpo-

rate performance utilized event studies and the other half utilized

surveys.

Fortunately, rigorous empirical research investigating the

impact of Six Sigma is beginning to emerge including the use of

event studies (Goh et al., 2003; Foster, 2007) and surveys (Lee

and Choi, 2006). While limited in quantity, this research tends

to contradict much of the anecdotal evidence because an over-

whelmingly positive relationship between Six Sigmaand corporate

performance has not been found. For example, Foster (2007) found

the impact of Six Sigma on operating and financial performance


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S.M. Shafer, S.B. Moeller / Journal of OperationsManagement 30 (2012) 521532 523

was mixed. Significant main effects were found for cost per dollar

sales, EBITDA, sales, S/E, and number of employees while signifi-

cant main effects were not found for free cash flow per share, asset

turnover, return on assets (ROA), return on investment (ROI), and

total assets. A significant limitation of the study is that the perfor-

mance of the Six Sigma firms was compared to a random sample

chosen from the Fortune 500as opposed to carefully selecting con-

trol groups. Barber and Lyon (1996) highlight the importance of

performance matching sample firms with control firms to ensure

that test statistics are well specified. Furthermore, the sample size

of 24 Six Sigma firms and the fact that only firms that announced

their adoption of Six Sigma from 1996 to 1998 limits the gener-

alizability of the results. For example, if differences exist between

earlier adopters of Six Sigma and more recent adopters then the

firms included in the study would not provide a representative

sample of all organizations that have adopted Six Sigma.

Utilizing structural equation modeling, Lee and Choi (2006)

investigated how four Six Sigma management activities impact

process innovation, quality improvement, and corporate compet-

itiveness improvement. The results of the study indicated that all

fourmanagement activitieshave a positive impact on process inno-

vation. Furthermore, the results indicated that process innovation

significantly affects quality improvement which in turn affects cor-

porate competitiveness. Key limitations of this study are that only

a single organization was studied and as is the case with all survey

based research, there may be a self-report bias.

Of the process improvement approaches researched to date,

TQM has been researched the longest and accounts for almost half

of the empirical studies reviewed. In contrast to the studies inves-

tigating Six Sigma, the TQM event studies have generally found

a positive relationship between TQM and corporate performance

(Hendricks and Singhal, 1997; Easton and Jarrell, 1998; Hendricks

and Singhal, 2001b; Eriksson and Hansson, 2003). Hendricks and

Singhal (1997) found strong support for thehypothesis that quality

award winning firms outperformed a control sample on operating

income-based measures. In another studyof quality awardwinning

firms, Eriksson and Hansson (2003) found that the quality award

recipients outperformed the competitor they were matched to interms of change in sales during the implementation period and

post implementation outperformed their matched competitor on

changein sales andreturn on assets. Easton and Jarrell (1998) found

strong evidence of overall improvement in operational and finan-

cial performance over the long-term. More specifically, across all

the variables investigated, more than half the TQM sample firms

outperformed their control group in terms of exceeding analysts

forecasts. Hendricks and Singhal (2001b) extended their previ-

ous research and investigated the role several firm characteristics

play in moderating the impact implementing effective TQM pro-

grams has on financial performance over a four to five year period.

The results indicated that smaller firms do significantly better

than larger firms and firms that received awards from indepen-

dent organizations significantly outperformed firms that receivedsupplier-based awards. The results of the study provided weak

support that less capital intense firms outperform more capital

intense firms and that more focused firms outperform more diver-

sified firms. Finally, there were no significant differences observed

between early and late adopters of TQM.

A key strength of many of the TQM event studies are the use

of quality award winning firms which helps ensure only sam-

ple firms that effectively implemented TQM were included in the

study (Hendricks and Singhal, 1997, 2001b; Eriksson and Hansson,

2003). A key weakness of the TQM event studies is that TQM

sample firms were not performance matched with control firms

(Hendricks and Singhal, 1997, 2001b; Eriksson and Hansson, 2003)

or were matched in unconventional ways (Easton and Jarrell,

1998).

In addition to the use of event studies, the impact of TQM on

corporate performance has been investigated with other method-

ologies. For example, twenty of the highest scoring Malcolm

Baldrige National Quality Award applicants from 1988 and 1989

were studied by the United States General Accounting Office at the

request of Congressman Donald Ritter (GAO,1991). Thestudyfound

that in almost all 20 cases, the companies had improved employee

relations, productivity, customer satisfaction, market share, and

profitability. Key limitations of the study include the small sample

size andthe fact that theresults were notbasedon rigorous statisti-

calanalysis. Like theTQM event studies discussedearlier,a strength

of the study was the use of quality award winners which serves as

a proxy for having implemented an effective TQM program.

Surveys have also been used to investigate TQM. Adam (1994)

surveyed manufacturing firms and found a significant relationship

between the quality improvement approach and operating and

financial performance. Powell (1995) found that TQM firms out-

performed non-TQM firms which in turn provided evidence that

TQM provides economic value to organizations. Powell also found

that long time adopters tended to be more satisfied with their TQM

programs. Handfield et al. (1998) found that customer satisfaction

was a primary driver of financial performance. A key implication

associated with this finding is that the financial returns associated

with investments in quality may be highly dependent on customer

satisfaction. Samson and Terziovksi(1999) investigated theissueof

whether elements of TQM could be used to predict organizational

performance and if so which elements are the best predictors of

organizational performance. Three of the elements of TQM: lead-

ership, human resources management, and customer focus were

found to be significant andpositively related to organizational per-

formance. Planning and process management were not found to be

significantly related to organizational performance, while informa-

tion andanalysis was found to be significant andnegatively related

to performance. Other surveys have found a positive relationship

between theextent to which TQMpractices were implemented and

firm performance (Douglas and Judge, 2001; Kaynak, 2003). While

these surveys each provide important insights it is also worth not-

ing that survey results are limited by the respondents memory,ability to respond, and honesty. Furthermore, there is always the

concern that those that responded to the survey are not represen-

tative of the general population of interest.

Nair (2006) performed a meta-analysis on 23 quality manage-

ment empirical studies over the period of 19952004. The results

of the meta-analysis support the hypotheses that quality manage-

ment practices are positively related to aggregate performance and

that this relationship is influenced by moderating factors. Interest-

ingly, while the results supported a positive relationship between

the use of quality information tools and aggregate performance,

support for a direct relationship between quality information tools

and financial or operational performance was not found. Quality

information tools are widely used as part of Six Sigma programs.

Beyond TQM, a number of studies have also investigated therelationship between ISO 9000 certification and firm performance.

On one side, Terziovski et al. (1997) and Singels et al. (2001) were

unable to find evidence that ISO certification was related to orga-

nizational performance. In contrast, Terziovski et al. (2003) found

a positive relationship between organizational performance and

managements motives for adopting ISO 9000 and Corbett et al.

(2005) found that ISO 9000 manufacturing firms achieved signif-

icantly better financial performance three years after obtaining

certification, however, the magnitude and timing of the improved

performance depended on theway thecontrol group wasspecified.

Given ISO 9000s focus on documenting processes as opposed to

improving them per se, the mixed results across the studies inves-

tigating the adoption of ISO 9000 and organizational performance

are not surprising.


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Finally, the research conducted to date has generally found a

positive relationship between lean/just-in-time (JIT) and corpo-

rate performance. Using the event study methodology, Huson and

Nanda (1995) foundthat adopting JIT resulted in increasedearnings

per share driven primarily by a 24% increase in inventory turnover.

In another event study, Balakrishnan et al. (1996) reported simi-

lar results finding that the JIT sample firms substantially increased

theirinventoryturnoverand decreased WIP inventory as a percent-

ageofsalesthroughtheadoptionofJIT.Inafinaleventstudy,Kinney

and Wempe (2002) found that both components of returnon assets,

asset turnover and profit margin, improved for the JIT adopters

compared to the non-adopters. These researchers also found that

improved margins were the primary driver of the improvements in

ROA and that the improvements in ROA were concentrated among

the early adopters of JIT indicating a first-mover advantage.

The relationship between JIT/lean and corporate performance

has also been investigated through survey based research. Shah

and Ward (2003) combined 22 lean practices into four lean bun-

dles: JIT, continuous improvement, total preventive maintenance,

and human resource management. A positive association between

each lean bundle and operational performance was found and,

in total, the lean bundles explained 23% of the variation in oper-

ational performance. Fullerton et al. (2003) found statistically

significant relationships between measures of profitability and the

degree to which JIT practices were used. Cua et al. (2001) investi-

gated the relationships between manufacturing performance and

multiple process improvement approaches related to continuous

improvement including TQM, JIT, and total productive mainte-

nance. The results of the study supported the hypothesis that

higher levels of manufacturing performance can be attained when

multiple continuous improvement approaches are simultaneously

implemented.

2.1. Literature synthesis

The literature related to the impact of Six Sigma on corpo-

rate performance is largely anecdotal in nature and tends tooverwhelmingly cite the benefits of Six Sigma on corporate perfor-

mance (e.g. Benitez et al., 2007; Craven et al., 2006; Daniels, 2009;

Deshpande et al., 2004; Dudman, 2005; Johnson, 2005; Jones, 2004;

Mukherjee, 2008). However, not all the empirical evidence is posi-

tive. Chakravorty (2010) cites research suggesting that almost 60%

of Six Sigma initiatives at corporations do not generate the desired

results.

Furthermore, in reviewing the literature on quality and process

improvement methodologies, there is a lack of rigorous research

investigating Six Sigma in comparison to the volume of research

conducted in other areas despite the substantial interest in Six

Sigma from industry. In addition to this lack of research, the

research conducted to date suffers from significant shortcomings.

For example, the scope of the Six Sigma studies to date has beenquite limited. Fosters (2007) event study included only 24 Six

Sigmaorganizations and Leeand Chois (2006) study surveyed only

employees of Samsung. Furthermore, in contrast to the empirical

TQMand JIT/lean research,the SixSigmastudies have notgenerally

found an overwhelmingly positive relationship between Six Sigma

and corporate performance. This is somewhat surprising given the

extentto which SixSigma practices overlap with andperhaps com-

plement TQM practices. For example, Zu et al. (2008) concluded

that Six Sigma includes practices distinct from TQM practices and

thatthese distinct Six Sigmapractices complementtraditional TQM

practices in terms of improving corporate performance. Thus, the

purpose of this study is to build on the limited empirical research

on Six Sigma and rigorously investigate the impact adopting Six

Sigma has on corporate performance.

3. Theorydevelopment, research hypotheses, and

performance variables

Six Sigma is theoretically different from other process improve-

ment methodologies so investigating its effect on performance

is valuable. However, because of its similarities with other pro-

cess improvement approaches, particularly TQM, there has been

an ongoing debate related to the extent to which it differs from

TQM (Schroeder et al., 2008). In terms of similarities between Six

Sigma and TQM, Schroeder et al. (2008) note the following:

Both TQM and Six Sigma emphasize the value of obtaining cus-

tomer input and the use of quality function deployment in

product/service design. Both Six Sigma and TQM emphasize process ownership and hav-

ing clearly defined processes. Both approaches recognize the importance of top management

leadership and support. Involving employees is emphasized by both approaches. How-

ever, the approaches differ in the employees involved. In

particular, Six Sigma tends to rely on process improvement

specialists while TQM emphasizes involving all employees, espe-

cially shop floor employees. Both approaches recognize the importance of collecting and

reporting quality data. Considerableemphasisis given to understanding theneedsof the

customer in both Six Sigma and TQM.

In terms of differences between Six Sigma and TQM, Zu et al.

(2008) identified three new practices associated with Six Sigma:

Six Sigma has well-defined process improvement specialist roles

(e.g. Green Belt, Black Belt, Master Black Belt) that are supported

with extensive training. Others have also highlighted the use

of full-time specialist roles as a key characteristic of Six Sigma

(Schroeder et al., 2008; Antony, 2004). Six Sigma utilizes a structured process improvement method-

ology called DMAIC in combination with a well-defined set oftools that are applied at various phases of the DMAIC methodol-

ogy. The acronym DMAIC refers to the five phase in a Six Sigma

process improvementproject: define, measure, analyze,improve,

and control. An important focus of Six Sigma is the use of process

improvement metrics to monitor process performance and set

improvement goals. Six Sigma advocates the use of several new

process performance metrics such as defects per million opportu-

nities (DPMO) and process sigma as well as the use of traditional

process performance metrics such as process capability and

rolled throughput yield.

Using exploratory and confirmatory factory analyses, Zu et al.

(2008) found that these three Six Sigma practices were imple-mented as distinct practices from seven traditional quality

management practices also considered in the study. Based on the

significant relationships observed in the structural model devel-

oped by Zu et al. (2008), a model of the relationships between the

adoption of Six Sigmaand organizational performance is presented

in Fig. 1. The three new Six Sigma practices which are highlighted

in the figure as dashed boxes illustrate not only how Six Sigma is

different from TQM, butalso how Six Sigma impacts corporate per-

formance. The seven TQM practices also investigated by Zu et al.

(2008) are not included in Fig. 1 for clarity of presentation and

because they are beyond the scope of the present study.

The model presentedin Fig.1 is consistentwith otherdefinitions

of Six Sigma in the literature. For example, Schroeder et al. (2008)

defineSix Sigmaas an organized, parallel-meso structureto reduce


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Top

Management

Support

Six Sigma

Focus on

Metrics

Six Sigma

Role

Structure

Six SigmaImprovement

Procedure

Product/

Service

Design

Process

Management

Quality

Performance

BusinessPerformance

Fig. 1. Theoretical model of relationship between Six Sigma and organizational performance.

variation in organizational processes by using improvement spe-

cialists,a structuredmethod, andperformance metrics withthe aim

of achieving strategic objectives. Importantly, all three Six Sigma

practices shown in Fig. 1 are key elements of this definition.

Beyond identifying the elements of Six Sigma, the definition

offered by Schroederet al. (2008) describes the goalassociated with

Six Sigma, namely, to reduce the variation inherent in organiza-

tional processes and as such clarifies how the Six Sigma practicesshown in Fig. 1 contribute to corporate performance. Accordingly,

Six Sigma seeks to improve business processes by studying and

reducing the inherent variation present in the process. Less pro-

cess variation boosts quality performance by allowing the process

to obtain more consistent outcomes in terms of quality, lead times,

yield rates, and so on.

Of course, and is shown in Fig. 1, reducing process variation and

improving quality performance are simply the means to an end.

Fundamentally, the overarching goal is enhanced business perfor-

mance including higher sales, increased market share, increased

operating income, improved return on assets, and so on. For exam-

ple, improved process execution resulting in less process variation

leads to higher quality performance which in turn could provide a

competitive advantage that translates into increased market shareand higher revenues. Likewise, less process variation can help to

reduce waste and inefficiency which in turn leads to lower costs

and higher profitability.

Based on these insights and the model shown in Fig. 1, we offer

two hypotheses related to the impact of Six Sigma on business

performance. First, we hypothesize that implementing Six Sigma

will improve the organizations profitability. To assess profitability

and to be consistent with Zu et al. (2008), we rely on Operating

Income (OI) before depreciation, interest, and taxes as opposed to

net income (NI). OI is calculated as sales minus total cost (cost of

goods sold + selling andadministrative expenses). OI provides a rel-

atively clean measure of the cash that is generated from operations

sinceit is not impacted by decisions made on how to treat depreci-

ation and amortization, by interest charges which are impacted bythe capital structure of the firm, or by taxes that can be influenced

by a variety of decisions. However, two factors OI does not control

for are the affect that capital expenditures (particularly mergers

and acquisitions) have and the size of the firm. Therefore, in order

to retain this clean measure of cash flows while at the same time

controlling for capital expenditures and firm size, we normalize OI

by dividing it by the average of total assets (A), sales (S), and num-

ber of employees (E). Thus, we assess the impact of Six Sigma on

profitability based on OI/A (orreturn on assets, ROA), OI/S (orreturn

on sales, ROS), and OI/E. According to Barber and Lyon (1996), ROA

is the most commonly used measure in studies aimed at detecting

abnormal operating performance.

Second, consistent with Zu et al. (2008), we hypothesize that

implementing Six Sigma will increase the organizations revenues.

Like OI, to control for capital expenditures and firm size, we nor-

malizeS by dividing it by theaverage of total assetsand thenumber

of employees. Thus, we assess the impact of Six Sigma on revenues

based on S/A and S/E.

Zu et al. did not investigate Total Costs as a separate factor and

investigating it in the present study would not provide additional

insight since it can be derived from OI and S which are already

included (i.e.,totalcost= S OI). Essentially then,this study focuseson the impact Six Sigma has on an organizations the top line (S)

and bottom line (OI).

4. Research methodology

4.1. Sample firm selection

In searching the web for organizations that adopted Six Sigma,

a list of approximately 400 organizations that were reported

adopters of Six Sigma was discovered. This list became the starting

point for identifying the sample Six Sigma organizations for this

study. Systematically, follow-up Google queries, searches of each

organizations website and annual reports, and queries of publi-

cation databases were executed for each public organization onthe list in an effort to identify the date when the organization

adopted Six Sigma.One advantage to studying Six Sigma compared

to other process improvement methodologies such as TQM or lean

is that with Six Sigma there is typically a clear start date as Six

Sigma is often initiatedwith some type of formal training program.

Ultimately, we were able to find public announcements or arti-

cles discussing the year that Six Sigma was adopted for 88 of the

public organizations on the list. Of these 88 organizations, Com-

pustat financial data was available for 84 of the firms and these 84

firms comprised our sample Six Sigma firms. Because the disclo-

sures used to identify the adoption date tended to coincide with

the actual adoption of Six Sigma, the inclusion of the sample Six

Sigma firms in this study is unrelated to their ultimate success or

lack of success with Six Sigma. Thus, while public disclosures maybe related to an organizations commitment to its Six Sigma ini-

tiative, there is no bias toward including only firms that have had

success with Six Sigma.

Table 1 provides distribution data on when the 84 sample firms

adopted SixSigma. Approximately 45% of thesample firms adopted

Six Sigma in 2000 or 2001 and 81% adopted it between 1998 and

2002. It is interesting to observe the decline in identifying firms

announcing or receiving otherpublicityregardingtheir adoption of

Six Sigma post 2001. This may reflect a perception that Six Sigma

hasbecome more mainstream andtherefore less worthyof a formal

announcement.

The 84 sample Six Sigma firms included in this study represents

a diverse set of firms. In particular, the sample firms represent 57

distinct 4-digit SIC codes and 27 unique 2-digit SIC Codes. Table 2


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Table 1

Distribution of theyear when samplefirms adopted Six Sigma.

Year Number of firms Percentage of firms

1986 1 1.2

1988 1 1.2

1990 1 1.2

1994 2 2.4

1995 1 1.2

1997 5 6.0

1998 13 15.51999 8 9.5

2000 16 19.0

2001 22 26.2

2002 9 10.7

2003 3 3.6

2004 2 2.4

19862004 84 100

Table 2

Summary financial data for Six Sigma sample firms in year Six Sigma adopted.a

OI S A E

(000,000) (000,000) (000,000) (000)

Mean 3851 18,001 51,036 59.6

Median 1380 10,072 9569 35.5

Std Dev 7005 26,987 120,304 74.3Max 37,895 183,691 665,287 373.8

Min 1391 506 454 1.9

a OI,S, and A in each samplefirms event year adjustedto equivalent 2004 dollars

using CPI, The Federal Reserve Bank of Minneapolis, http://www.minneapolisfed.

org, May 23,2011.

provides summary financial data on the sample firms based on the

year the firms adopted Six Sigma. All financial data were obtained

from the Compustat Annual Industrial File.

The research methodology employed in the present study

overcomes the shortcomings of other methodologies. To begin,

corporate performance is assessed on the basis of publically avail-

able and audited data thereby eliminating biases that may exist

in self-reported data. Also, with surveys it is not clear if the wayrespondents interpret survey questions influences their responses.

Another limitation associated with survey research is that it is

assumed that respondents have the knowledge to answer items

when in fact they may not. With interviews, the quality of the

data and its interpretation are highly dependent on the skills of

the interviewer.

4.2. Time period of analysis

To investigate the impact of Six Sigma on corporate perfor-

mance, a study period of ten years was employed. This ten year

periodconsistedof theevent year or year the company adopted Six

Sigma, a three year pre-implementation period prior to the event

year, and a six year post-implementation period following theevent year. Including the three-year pre-implementation period

permits investigating whether there are performance differences

between the sample Six Sigma firms that may have influenced

their implementing Six Sigma in the first place and also investi-

gating the relationship between pre-implementation performance

and post-implementation performance. Another important reason

for assessing performance pre-implementation is so performance

matched control groups can be constructed to ensure that our test

statistics are well-specified (Barber and Lyon, 1996).

The six year post-implementation period is chosen to ensure

that the Six Sigma firms were given adequate time to realize

the benefits of adopting Six Sigma. While there is little research

addressing the lag between adopting Six Sigma and the realiza-

tion of benefits from doing so, the research on TQM suggests a two

to three year lag period. For example, the study by the US GAO

found an average lag of approximately 2.5 years from the time

when the companies initiated their focus on quality until the per-

formance improvements became evident (GAO, 1991). Easton and

Jarrell (1998) found no statistically significant effects in terms of

unexpected performance for NI/S, NI/A, and OI/S one and twoyears

after the event year in their study of TQM and corporate perfor-

mance. However, the unexpected average performance over years

three to five on these three variables was statistically significant.

In the Easton and Jarrell (1998) study, the lag in improved perfor-

mance is even more understated since the event year was defined

as six months after the first major TQM initiative.

For the purpose of this study, fiscal years are converted into

event years in order to pool observations over time. By convention,

event year 0 corresponds to the fiscal year a given sample firm

adopted Six Sigma, eventyear1 corresponds to the yearpreceding

the year Six Sigma was adopted, and event year +1 corresponds to

the year following the year Six Sigma was adopted.

4.3. Assessing corporate performance

In an ideal world the impact of implementing Six Sigma on

a firms performance would be assessed by comparing how thecompany performed both with and without Six Sigma. Unfortu-

nately making this type of assessment is not possible and therefore

assessing the impact of Six Sigma on a firms performance requires

the identification of relevant performance benchmarks. We evalu-

ate a variety of different benchmarks to ensure that the benchmark

choice is not driving the results.

Atone endof thespectrumof benchmarks, we takeanaveview-

point and use an industry adjusted performance of our sample Six

Sigma firms. We calculate this by subtracting the industry median

performance from the performance of our sample firms. Indus-

try median performance is used because of the non-normality of

the financial data. Industry medians are created by first computing

the 4-digit, 3-digit, 2-digit, and 1-digit SIC industry classifications

medians excluding sample firms. Then the sample firm SIC code ismatched to the most detailed industry level median which has at

least five other companies in the industry classification.2

There are several benefits associated with using industry

adjusted performance. First, because organizations in different

industries face unique challenges and market conditions, adjus-

ting a firms performance relative to its industrys performance

permits comparisons across industries. Second, assessing a firms

performance relative to its industry provides an evaluation of the

company relative to a large sample of its competitors. Third,indus-

try adjusted performance is a very nave measure which relies

upon minimal assumptions. Across all performance measures in

year 1, the average number of firms included in a given Six

Sigma sample firms industry was 61.4 with a range of six to

590.

At the other end of the continuum, we also compare a sam-

ple Six Sigma firms performance to the performance of the closest

matched firm and a portfolio of control firms matched to it on the

basis of industry, year, and similar past performance. Because the

results were similar with either the best match or the portfolio of

matching firms, we report the results of the portfolio of matching

firms.

Matching control firms on the basis of similar pre-event per-

formance helps on several dimensions (Barber and Lyon, 1996).

First, it helps to control for the endogeneity problem because the

levelof performance may be due to managerial ability,firm-specific

2

We also match on 4-digit only andobtain similar results.
http://www.minneapolisfed.org/http://www.minneapolisfed.org/http://www.minneapolisfed.org/http://www.minneapolisfed.org/


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Table 3

Six Sigma firms median industry adjusted performance by event year.

Event year OI/A OI/S OI/E

($000/employee)

S/A S/E

($000/employee)

N Median N Median N Median N Median N Median

3 76 0.0290*** 76 0.0363*** 76 11.3375*** 77 0.0538 77 37.5602***

2 77 0.0258*** 77 0.0471*** 76 11.5158*** 78 0.0429 77 26.0943***

1 79 0.0258*** 79 0.0487*** 79 11.3365*** 80 0.0046 80 23.7185***

0 82 0.0264*** 82 0.0442*** 79 13.2159*** 83 0.0240 80 35.2630***

+1 83 0.0353*** 83 0.0449*** 83 17.3888*** 84 0.0086 84 33.5807***

+2 83 0.0391*** 83 0.0404*** 83 18.7008*** 84 0.0121 84 40.8392***

+3 83 0.0380*** 83 0.0418*** 82 13.4381*** 84 0.0420* 83 45.7888***

+4 83 0.0313*** 83 0.0329*** 82 12.1594*** 84 0.0195 83 49.6187***

+5 82 0.0278*** 82 0.0346*** 81 11.7895*** 82 0.0303 81 55.6561***

+6 81 0.0269*** 81 0.0327*** 79 15.0333*** 81 0.0516 79 59.7682***

* Significant at 10%level, sign test formedian two-sidedtail.** Significant at 5% level,sign test formedian two-sided tail.

*** Significant at 1% level,sign test formedian two-sidedtail.

choices or theset of investmentopportunities. Bymatchingon per-

formance, a researcher can control for various factors, unrelated to

an event, that affect the operating performance of assets (Barber

and Lyon, 1996, p. 366).

Second, matching on pre-event performance eliminates thereversion to the mean effects that are common in accounting

data. Finally, matching sample firms with control firms based on

performance is also important to help ensure that our test statis-

tics are well specified. Barber and Lyon concluded that selecting

control firms on the basis of pre-event performance is the only

way to ensure that the test statistics are well specified and that

matching on pre-event performance is considerably more impor-

tant than selecting control firms on the basis of industry and/or

size.

The portfolio of same 4-digit SIC industry and year control firms

were selected based on similar performance on the variable being

assessed in the year prior (event year 1) and four years prior

(event year 4) to the sample firm adopting Six Sigma.3 Because

the results were similar with either event year match, we focuson the year 1 match. Similar performance was operationalized

as the control firms performance being within 10% of the sample

firms performance in eventyear1.4 Barber andLyon (1996) found

that using a 90110% performance filter yields well-specified test

statistics. Note that because performance was matched separately

for each performance measure, the composition of the portfolio of

performance matched control firms for a given sample firm could

vary across the five performance measures. Across all performance

measures in year 1, the average number of firms included in a

given Six Sigma sample firms matched portfolio was 4.4 with a

range of 134.

Finally, forthe purposes of this study,our benchmarks area con-

servative measure of performance because it is possible that other

firms included in the benchmark may have also implemented Six

Sigma or other process improvement programs. Along these lines,

if adopting Six Sigma does indeed enhance organizational perfor-

mance, then including other Six Sigma firms in the benchmark

3 We also matched on 4-digit, 3-digit, 2-digit and 1-digit industry and found

similar results.4 Because some of our measures are a very small percentage, we expanded the

match to include anythingwithinan absoluteone percentif theratio is between10

and +10 percent. In other words, if the sample firms operating income to sales is

three percent, thematching firmratio is between twoand four percent. Not includ-

ing these expanded matches reduces our sample size but it does not change the

results. In addition, we required control firms to have five years of data, event year

1 through +3.

wouldenhance the overall benchmark performance and reduce the

sample Six Sigma firms adjusted performance.5

5. Empirical results

5.1. Six Sigma firms industry adjusted performance

Tables 3 and 4 summarize the industry adjusted performance

for the Six Sigma sample firms.

In Table 3 the industry adjusted performance is calculated by

subtracting the sample firms median industry performance from

its individualperformance. Positive industry adjusted performance

indicates thatthe sample firm outperformed its industry.For exam-

ple,themedianOI/AofasampleSixSigmafirmintheyearSixSigma

was adopted (event year 0) was 0.0264 higher than its median

industry OI/A. Also note that the difference between the sampleSix

Sigma firm and its industry was used as opposed to using the per-

centage change in order to avoid problems with having a negative

denominator which can occur when OI is used. The percentchangehas no meaning when the denominator is negative and removing

sample firms with a negative OI could bias our results.

Because the sample firms median industry performance is

subtracted from its individual performance, industry adjusted per-

formance is effectively a paired difference. Histograms of the

industry adjusted performance measures were non-normal and

not symmetric as is often the case with financial data, and there-

fore the non-parametric Wilcoxon sign rank test was used to

test the hypotheses that the industry adjusted performance was

equal to zero. Statistically significant results are reported on the

basis of two-tailed tests. The use of non-parametric tests in this

study is supported by Barber and Lyon (1996) who found that

non-parametric tests are more powerful than their parametric

counterparts.As Table 3 illustrates, the Six Sigma sample firms outperformed

their respective industries on all of the performance variables

except S/A across all event years including both the years prior to

implementing Six Sigma and the years post Six Sigma implemen-

tation. Thus, firms that adopted Six Sigma, on average, performed

better than the industry prior to their announcement and they

maintained their significantly better performance after adoption.

Specifically, while they do not generate more sales per assets, they

5 Though the results are not reported, we also winsorized and trimmed the data

and found similar results. We winsorized at the2.5 and97.5percentileswhichsets

the observationsbelow the 2.5percentile and above the 97.5percentile equal to the

values at the2.5 and 97.5 percentiles,respectively. We similarlytrimmed thedata.


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Table 4

Industry adjusted rate of improvement.

Time period OI/A OI/S OI/E

($000/employee)

S/A S/E

($000/employee)


3 to 1 76 0.0097 76 0.0041 76 1.3027 77 0.0099 77 8.8337

1 to +1 79 0.0055 79 0.0035 79 0.5119 80 0.0502* 80 7.7962

1 to +3 79 0.0099 79 0.0026 78 2.3733** 80 0.0017 79 13.6938**

+1 to +3 83 0.0077 83 0.0023 82 2.0001 84 0.0074 83 14.4206**

+1 to +6 79 0.0075 79 0.0045 77 2.5371 80 0.0072 78 20.0235***

3 to +6 72 0.0044 72 0.0012 70 6.7310*** 73 0.0511 71 22.9155***

* Significant at 10%level, sign test formedian two-sided tail.** Significant at 5% level,sign test formedian two-sided tail.

*** Significant at 1% level,sign test formedian two-sided tail.

didgeneratemoresalesper employee andare more efficientat gen-

erating operating income relative to assets, sales and employees.

Furthermore, the Six Sigma firms performance advantage over

the industry generally increased in the short-term period immedi-

ately after implementing Six Sigma compared to the period prior

to Six Sigma implementation. For example, referring to Table 3, the

average industry adjusted S/E over the period 3 to 1 was 29.12.

In contrast, the average industry adjusted S/E over the periods +1

to +3 was 40.07 indicating a larger performance gap after imple-menting Six Sigma. Similar improvements in the performance gap

were also observed for OI/A and OI/E. Over the longer-term period

of +4 to +6, the Six Sigma firms continued to increase their perfor-

mance advantagein terms of S/E. In particular, theaverage industry

adjusted S/Eover theperiods +4to +6increasedto 55.01(compared

to an average of 29.12 over 3 to 1 and 40.07 over +1 to +3).

In terms of OI/A and OI/E, the Six Sigma firms outperformed their

industry over the period +4 to +6 by a larger margin than during

the pre-implementation period of3 to 1, but by a smaller mar-

gin than the short-term period of +1 to +3 immediately following

Six Sigma adoption.

In contrast to the static analysis summarized in Table 3, Table 4

provides a dynamic comparison between the rate of improvement

of the sample Six Sigma firms and the median industry rate of improvement. This provides a direct test whether Six Sigma firms

improve performance after adoption. More specifically, Table 4

summarizes the industry adjusted rate of improvement across var-

ious time periods by calculating the difference between the change

ina given sampleSix Sigmafirms performance over thetimeperiod

andthe changein medianindustry performance over thesametime

period. A positive industry adjusted rate of improvement indicates

that the sample Six Sigma firms rate of improvement was greater

than the overall industrys rate of improvement over the specified

time period.

As illustrated in Table 4, in the pre-implementation period

comprised of event years 3 to 1, the sample firms rate of

improvement was not statistically different with the industry on all

five performance measures. So prior to the adoption of Six Sigma,the sample firms were not on a different performance trajectory

than the industry. Thus, the adoption of Six Sigma was not moti-

vated by a change in performance in the two years prior.

Once Six Sigma was adopted, we find evidence that the sample

firms rate of improvement in utilizing employees was significantly

better than the industry. Over event year 3 to +6 and 1 to +3

the sample Six Sigma firms outperformed their industries on both

employee productivity measures OI/E and S/E (see Table 4). Cor-

respondingly, the sample Six Sigma firms rate of improvement in

terms of OI/A, OI/S and S/A was not statistically different from their

respective industries.

More specifically, the sample Six Sigma firms rate of improve-

ment was greatest relative to their firms respective industry

rate of improvement on S/E. The sample Six Sigma firms rate of

improvement on S/E was significantly greater than their respective

industries rate of improvement in the short term periods 1 t o + 3

and +1 to +3 at a 5% level of significance and longer term over the

periods +1 to +6 and 3 to+6 ata 1%level ofsignificance. The sam-

ple Six Sigma firms also had a greater rate of improvement on the

other employee productivity measure, OI/E, over the short term

period 1 to +3 at the 5% level of significance and over the long

term period 3 to +6 at the 1% level of significance. Differences in

the rate of improvement between the sample Six Sigma firms andtheir respective industries were not observed for OI/A and OI/S.

Statistically significant results in terms of the rate of improvement

of S/A were also generally not observed except for the period 1

to +1 where the sample Six Sigma firms rate of improvement was

greater at the 10% level of significance.

Aside from S/A, we did not find other significant improvements

in the short run as measured by the 1 to +1 event year period.

This is consistent with the relatively long time it takes to roll out

Six Sigma programs.

So relative to their industry, we find the Six Sigma firms are

better performers both prior to and after the adoption of Six Sigma.

In terms of the rate of improvement, the results suggest that the

adoption of Six Sigma significantly improves the efficiency with

which an organization deploys its employees but it does not affectits efficiency in deploying assets or operating income relative to

sales.

5.2. Six Sigma firms performance relative to matched portfolios

of control firms

Tables 5 and 6 summarize the performance for the Six Sigma

sample firms relative to the performance of matched portfolios of

control firms. In Table 5 the sampleof SixSigmafirms adjusted per-

formance is calculated by subtracting the median performance of

its matched portfolioof control firmsfrom its performance. Positive

portfoliomatched performance indicates that the sample Six Sigma

firm outperformed its matched portfolio of control firms. For exam-ple, the sample Six Sigma firms OI/A was a median 0.0014 higher

than their respective matched portfolio of control firms in the year

Six Sigma was adopted (event year 0). Like the industry adjusted

analysis, the difference in performance between the sample firm

and its matched portfolio of control firms was used as opposed to

calculating the percent change to avoid the negative denominator

problem.

Paired differences were created by subtracting the sample firms

matched portfolios performance from its performance. For the

same reasons as discussed for the industry adjusted data, the non-

parametric sign test was used to test the hypotheses that the

differencesbetween the sample firmsand theirportfolios of control

firms were equal to zero. Statistically significant results are again

reported on the basis of two-tailed tests.


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Table 5

Six Sigma firms medianadjusted performance basedon portfolio of matched control firms by eventyear.

Event year OI/A OI/S OI/E

($000/employee)

S/A S/E

($000/employee)


3 39 0.0131* 39 0.0006 31 2.1602* 40 0.0409 44 15.9693*

2 43 0.0079 41 0.0012 33 0.9961 43 0.0317 46 13.1148***

1 48 0.0003 46 0.0012 37 0.3529 46 0.0029 48 0.5978

0 48 0.0014 46 0.0023 37 2.7288** 46 0.0023 48 7.6650

+1 48 0.0125** 46 0.0063 37 7.7801** 46 0.0202 48 19.0194***

+2 48 0.0114** 46 0.0101* 37 6.7384*** 46 0.0675 48 26.7210***

+3 48 0.0036 46 0.0097** 37 9.6990*** 46 0.0004 48 25.6801***

+4 48 0.0018 46 0.0121*** 36 10.3645*** 44 0.0090 46 35.8037***

+5 45 0.0144 42 0.0130** 34 14.5282*** 40 0.0080 43 29.8120**

+6 42 0.0032 39 0.0279*** 32 14.6827* 39 0.0254 41 35.6440**

* Significant at 10%level, sign test formedian two-sidedtail.** Significant at 5% level,sign test formedian two-sidedtail.


Table 6

Adjusted rate of improvement based on portfolio of matched control firms.

Time period OI/A OI/S OI/E

($000/employee)

S/A S/E

($000/employee)


3 to 1 39 0.0144* 39 0.0031 31 2.3375** 40 0.0272 44 12.9774*

1 to +1 48 0.0140** 46 0.0041 37 7.1443** 46 0.0212 48 11.7228**

1 to +3 48 0.0037 46 0.0117** 37 11.4627*** 46 0.0057 48 26.0909***

+1 to +3 48 0.0038 46 0.0140 37 3.6265* 46 0.0071 48 20.6390***

+1 to +6 42 0.0145 39 0.0048 32 8.5194 39 0.0030 41 17.2024**

3 to +6 33 0.0048 33 0.0342*** 27 11.2845 35 0.0290 37 12.3567

* Significant at 10%level, sign test formedian two-sidedtail.** Significant at 5% level,sign test formedian two-sidedtail.


While statistically significant differences were observed

between the performance of the sample Six Sigma firms and their

performance-matched portfolios of control firms in various event

years, as expected fewer statistically significant results were foundin comparison to the industry adjusted results discussed earlier. For

example, the lack of significant results in year 1 is expected due

to fact that control firms were selected based on their performance

relative to the sample firms performance in event year 1. Refer-

ring to the results in Table 5, prior to implementing Six Sigma, the

sample firms were generally at parity with their control firm port-

folios while they outperformed their respective industries in the

industry adjusted analysis. Notable exceptions to this (see Table 5)

include slightly significant results at the 10% level for OI/A, OI/E,

and S/E in year 3 and S/E at the 1% level in year 2. Likewise, in

theyear of implementation (eventyear 0),the samplefirms were at

parity on allperformance measures exceptfor OI/E at the 5% level.6

While generally at parity prior to adopting Six Sigma, over the

short-term the sampleSix Sigma firms made gains on several of the

performance variables. For example, the sample Six Sigma firms

OI/A was significantly higher at the 5% level in years +1 and +2.

Likewise, the sample firms had significantly higher OI/S at the 10%

level in year +2 and in year +3 at the 5% level. In terms of OI/E,

the sample Six Sigma firms outperformed their matched portfolio

in year +1 at the 5% level and in years +2 and +3 at the 1% level.

Finally, the sample Six Sigma firms outperformed their matched

portfolios on S/E in years +1, +2, and +3 at the 1% level.

6 In unreported analysis, we matched on year 4, rather than year 1, perfor-

mance and though there were some minor differences in the pre-adoption period,

the post-adoption results and rates of improvement results are unchanged.

Turning to long-term performance, the sample Six Sigma firms

did not outperform their matched portfolio on either asset pro-

ductivity measure, namely, OI/A and S/A. Significant results for S/A

were also not found in the industry adjusted analysis. The sampleSixSigmafirmsoutperformed their matchedportfolio on OI/S atthe

1%level in years +4and +6and in year+5 atthe 5%level. Similar to

the industry adjusted analysis,the sample SixSigma firms also out-

performed theirmatched portfolios on both employee productivity

measures (OI/E and S/E) in all years +4 through +6.

Overall, though the results are generally a bit weaker, similar to

the industry adjusted analysis we find firms that adopt Six Sigma

have higher performance in terms of employee productivity. Fur-

thermore, in both the industry adjusted analysis and the matched

portfolio analysis, the firms that adopt Six Sigma did not outper-

form their respective benchmarks in terms of their sales to asset

efficiency. Finally, while the Six Sigma firms outperformed their

industry in allyearsinvestigated in terms of OI/S, evidence of better

performance relative to their matched portfolios was not observed

until event year +2 with the results generally becoming more sig-

nificant as additional experience was gained with Six Sigma.

In contrast to the static analysis summarized in Table 5 com-

paring the performance of the sample firms to matched portfolios

of control firms at particular points in time, Table 6 provides a

dynamic comparison between the rates of improvement of the

sample firms and their respective matched portfolios of control

firms. In particular, Table 6 summarizes the portfolio adjusted

rate of improvement across alternative time periods by calculat-

ing the difference between the change in a sample Six Sigma firms

performance over the time period and the median change in its

portfolios performance over the same time period. A positive port-

folio adjusted rate of improvement indicates that the sample Six

Sigma firms rate of improvement was greater than the rate of


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improvement of their matched portfolios of control firms over the

specified time period.

As illustrated in Table 6, in the pre-implementation period con-

sisting of event years 3 to 1, the sample Six Sigma firms rate of

improvement is significantly lower than the rate of improvement

of their respective matched portfolios in terms of OI/A, OI/E, and

S/E. Since Table 5 shows that the sample firms outperformed the

portfolio in event year 3 on these measures, the previously well

performing sample firms were experiencing a decline in their rel-

ative performance in the two years prior to adopting Six Sigma.

In addition, for only these three performance measures there is a

quick significant improvement in performance from the 1 to +1

period. So for sample firms that outperformed the portfolio in year

3, they experience a significant decline in performance prior to

Six Sigma then a similar quick rebound upon adopting Six Sigma.

This result raises an interesting question regarding to the extent

to which contextual factors may influence the lag between adopt-

ing Six Sigma and its impact on organizational performance. In the

present case, firms that outperformed their matched portfolio in

event year 3 entered a downward trajectory and then experi-

enced a quick bump in performance immediately after adopting

Six Sigma. This evidence is consistent with better performing firms

having an advantage when it comes to reaping the benefits of

adopting Six Sigma and/or better performing firms react faster at

the first signs of deteriorating performance.

The significantlyhigher rate of improvement forSix Sigma firms

relative to the matched portfolio for operating income and sales

per employee are generally consistent with the industry adjusted

results. Specifically, the sample Six Sigma firms had a greater rate

of improvement relative to their matched portfolio in years 1 to

+3 on OI/Eand S/E atthe 1% levelandthe +1to +6period for S/E at

the 5% level.

In contrast to the industry adjusted results, we also find sig-

nificant improvement in operating income to sales. Specifically, in

the 1 to +3 period OI/S is significantly higher at the 5% level. Also,

over theentireten year periodstudiedfromeventyears3to+6the

sample firms rate of improvement exceeded the matched portfolio

rate of improvement on OI/S at the 1% level.

5.3. Results summary

The purpose of this research was to investigate the impact the

adoption of Six Sigma has on corporate performance. The results of

the study indicate Six Sigma positively impacts organizational per-

formance primarily through the efficiency with which employees

are deployed. The benefits of adopting Six Sigma were observed in

both the static analysis and the analysis of the Six Sigma firms rate

of improvement when the sample firms were compared to both

their industries and a portfolio of performance matched firms.

The results of the static analysis provide evidence that firms

that adopt Six Sigma are strong performers. The clearest evi-

dence of this was observed in the industry adjusted analysis wherethe sample Six Sigma firms outperformed their respective indus-

tries on all performance measures except S/A in all years prior to

adopting Six Sigma and in all years studied post adoption. Further-

more, while the results were not as strong, the sample Six Sigma

firms also outperformed their matched portfolios of control firms.

More specifically, while the Six Sigma firms tended to be at par-

ity with their matched portfolio prior to adopting Six Sigma, they

outperformed theirmatched portfolioin the yearsfollowingimple-

mentation on OI/A (event years +1 and +2), OI/S (event years +2

through +6), OI/E (event years +1 through +6), and S/E (event years

+1 through +6). Again, there was no difference in S/A.

In contrast to the static analysis, in the dynamic analysis of

the Six Sigma firms rate of improvement, the strongest evidence

that Six Sigma firms achieve a greater rate of improvement was

observed in comparisonto theirmatchedportfolios of control firms.

One of themost interesting results observed wasthat the SixSigma

firms outperformed their matched portfolios in year 3 intermsof

OI/A, OI/E, and S/E, then experienced a significant decline in per-

formance prior to Six Sigma on these three measures, and finally

exhibited a quick rebound upon adopting Six Sigma. This evidence

is consistent with previously better performing firms having an

advantage when it comes to reaping the benefits of adopting Six

Sigma and/or better performing firms reacting faster at the first

signs of deteriorating performance.

We also found evidence that the sample firms rate of improve-

ment in terms of employee productivity was significantly better

thantheir industries andmatchedportfolios. The sample Six Sigma

firms rate of improvement relative to the benchmarks perfor-

mance was most persistent in terms of S/E where statistically

significant results were found over the periods 1 to +3, +1 to +3,

and +1 to +6 in both the industry adjusted and matched portfolio

analysis. Statistically significant results were also found for OI/E in

both the industry adjusted and matched portfolio analysis over the

period 1 to +3. Thus, the results of this study indicate that Six

Sigmas greatest impact was on the efficiency with which employ-

ees are deployed. In both the industry adjusted analysis and the

performance-matched analysis, the Six Sigma firms outperformed

their respective benchmark groups on both OI/E and S/E in all

years afterimplementing Six Sigma. Furthermore, the performance

advantage for the Six Sigma firms on both employee productivity

measures tended to be larger after adopting Six Sigma and tended

to increase as additional experience was gained with Six Sigma.

Other results of the study further support increasing benefits

from Six Sigma as experience is gained. In the performance-

matched portfolio analysis, there was no difference between the

sample firms and their portfolio of firms on OI/S until event year

+2 with the results generally becoming more statistically signifi-

cant (i.e., lowerp-values) as additional experiencewas gained with

Six Sigma. As another example, the rate of improvement increased

as additional experience was gained with Six Sigma on S/E in the

industry adjusted analysis (see Table 4).

In total, the results suggest that better performing firms adoptSix Sigmaand thatthey continue theirperformance advantageafter

adopting SixSigma. Furthermore,the results suggest that SixSigma

has its greatest impact on employee productivity and not on asset

productivity. Finally, therewas no evidence thatadoptingSix Sigma

negatively impacts corporate performance.

6. Limitations and future research directions

6.1. Limitations

As an empirical research study,it is appropriate to highlight and

discuss the key limitations associatedwith the study. First, it should

be noted that the source of the data for the study was Compustat.While Compustat data is a widely used and highly regarded source

of data, errors in the data and missing data items could have an

impact on the results obtained in the study.

Second, the focus of our study was on firms that publically

announced or have received other publicity about their adoption

of Six Sigma. While such publicity could serve as a proxy for the

sample firms commitment to Six Sigma, no attempt was made

to assess the effectiveness to which the sample Six Sigma firms

implemented Six Sigma. This is in contrast to several of the TQM

studies where the sample firms only consisted of quality award

winners as a proxy for effective TQM implementation. Thus, any

statistically significant results regarding superior performance of

the sample Six Sigma firms are conservative to the extent that the

sample included firmsthat did not implementSix Sigmaeffectively.


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In particular, observing statistically significant differencesbetween

the Six Sigma sample firms and their industries or their matched

portfolios becomes more difficult as the percentage of sample Six

Sigma firms that effectively implemented Six Sigma decreases.

Third, our sample of Six Sigma firms only included firms that

publically announced or received other publicity about their adop-

tion of Six Sigma. Thus, it is possible that these firms are not

representative of all firms that have adopted Six Sigma.

Fourth, all empirical research on operational systems encoun-

ters the challenge of finding direct measures for performance

outcomes. Our choice of performance measures was driven by our

theoretical framework and the measures used in past research.

Fifth, though we did analyze prior performance trends, we did

notexplicitly control fora firms motivation foradopting SixSigma.

There are clearly a variety of endogenous factors, including moti-

vation that could influence why a firm chooses to adopt Six Sigma

and that also impact performance. For instance, the adoption of

Six Sigma may be motivated by recent poor financial performance,

as a complement to a successful TQM implementation, or perhaps

because firms with better management tend to adopt Six Sigma.

Theissue of endogeneity is a serious issue butany study of this type

suffers from thesame problem. In this paper we use many methods

to attempt to manage endogeneity in order to isolate Six Sigma as

the important factor and we are careful to appropriately interpret

our results. However, as with all empirical research, it is a balanc-

ing act. While knowing a firms motivation for adopting Six Sigma

might be insightful, we are not concerned that not controlling for

thisbiased our results basedon the performance matching research

ofBarber and Lyon (1996).

Finally, as with all studies of this type, adopting Six Sigma may

serve as a proxy for unobservable firm characteristics. As such, this

type of research is not prescriptive and does not imply that merely

adopting Six Sigma guarantees improved firm performance.

6.2. Future research issues

As an early research study addressing the relationship betweenSixSigma andcorporate performance, there arenumerousavenues

for extending this research that would contribute to our under-

standing of the impact Six Sigma has on corporate performance.

To begin, research is needed that investigates firm characteristics

such as firm size, capital intensity, degree of diversification, indus-

try, and the maturity of Six Sigma implementation. For example,

do larger firms have an advantage in deploying Six Sigma because

it is easier for them to absorb the training cost and dedicating

full-time resources to process improvement activities? Likewise,

given the evidence from the study that Six Sigma facilitates the

deployment of employees, are highly capital intensive firms able

to reap benefits similar in magnitude to less capital intensive

firms? Lastly, are theredifferences in performance between various

industries?Research is also needed to investigate the relationship between

how Six Sigma was implemented and corporate performance. For

example, in this study, the extent to which Six Sigma was imple-

mented throughout the firm was not controlled for. Logically we

would not expect to observe the same magnitude of benefits for

firms that adopted Six Sigma across the entire enterprise versus

others that implemented it more narrowly in a limited number of

divisions, geographies, or plants. Another implementation issue in

need of additional research relates to whether there aredifferences

in performance between early and late adopters of Six Sigma. In a

related vein, does the previous adoption of other process improve-

ment initiatives such as TQM, ISO 9000, and/or lean moderate the

relationship between the adoption of Six Sigma and corporate per-

formance? Is there a difference in performance between firms that

have integrated their Six Sigma initiatives with lean versus firms

that have kept them separate or only adopted Six Sigma?

Related to these research issues, another important area of

research is to investigate the relationship between an orga-

nizations motivation for adopting Six Sigma and corporate

performance. For example, can patterns be observed suggesting

that some firms adopted Six Sigma during periods when they were

outperforming the industry while other firms adopted Six Sigma

during periods when they were underperforming the industry? If

so,is there a difference in performance between themore proactive

firms versus the more reactive firms? And even more fundamen-

tally, is there a difference between the types of firms that benefit

from adopting SixSigmaversus those that donot? Addressing these

research issues would provide important insights to managers and

researchers on the relationship between Six Sigma and corporate

performance.

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The Effects of Six Sigma on Corporate Performance- An Empirical Investigation