the effects of six sigma on corporate performance- an empirical investigation
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Journal of Operations Management 30 (2012) 521532
Contents lists available at SciVerse ScienceDirect
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.
http://dx.doi.org/10.1016/j.jom.2012.10.002
http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jom.2012.10.002http://www.sciencedirect.com/science/journal/02726963http://www.elsevier.com/locate/jommailto:[email protected]:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jom.2012.10.002http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jom.2012.10.002mailto:[email protected]:[email protected]://www.elsevier.com/locate/jomhttp://www.sciencedirect.com/science/journal/02726963http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jom.2012.10.002 -
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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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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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524 S.M. Shafer,S.B. Moeller / Journal of Operations Management 30 (2012) 521532
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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S.M. Shafer, S.B. Moeller / Journal of OperationsManagement 30 (2012) 521532 525
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.
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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)
N Median N Median N Median N Median N Median
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)
N Median N Median N Median N Median N Median
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.
*** Significant at 1% 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)
N Median N Median N Median N Median N Median
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.
*** Significant at 1% 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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530 S.M. Shafer,S.B. Moeller / Journal of Operations Management 30 (2012) 521532
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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