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Journal of Operations Management 30 (2012) 437453
Contents lists available at SciVerse ScienceDirect
Journal ofOperations Management
j ournal homepage: www.elsevier .com/ locate / jom
Six Sigma adoption: Operating performance impacts and contextual
drivers ofsuccess
Morgan Swink a,, Brian W.Jacobs b,1
a Neeley Business School, TCU, PO Box298530, Fort Worth,TX 76129,UnitedStatesb Broad College of Business, Michigan State University, N370 North Business Complex, East Lansing, MI 48824-1122, United States
a r t i c l e i n f o
Article history:
Received 18 October 2011
Received in revised form 24 February 2012
Accepted 25 May 2012
Available online 4 June 2012
Keywords:
Six sigma
Process innovation
Operating performance
Event study
a b s t r a c t
We assess the operational impacts ofSix Sigma program adoptions through an event study methodology,
comparing financial data for 200 Six Sigma adopting firms against data for matched firms, which serve as
control groups for the analyses. We employ various matching procedures using different combinations
of pre-adoption return on assets (ROA), industry, and size as matching criteria. By comparing perfor-
mance outcomes across a hierarchy of operating metrics, we establish a pattern of Six Sigma adoption
effects that provides strong evidence of a positive impact on ROA. Interestingly, these ROA improve-
ments arise mostly from significant reductions in indirect costs; significant improvements in direct costs
and asset productivity are not evident. We also find small improvements in sales growth due to Six
Sigma adoption. Cross-sectional analyses ofthe performance results reveal that distinctions in Six Sigma
impacts across manufacturingand service firms are negligible. Interestingly,we find that the performance
impact ofSix Sigma adoption is negatively correlated to the firms quality system maturity (indicated by
prior ISO 9000 certification). Further analyses ofmanufacturing and service firms reveals that Six Sigma
benefits are significantly correlated with intensity in manufacturing, and with financial performance
before adoption in services. We discuss the implications of these findings for practice and for future
research.
2012 Published by Elsevier B.V.
1. Introduction
Since its origins in the mid-1980s, the Six Sigma program
for process improvement has become widely embraced. One
report suggests that many Fortune 500 firms have adopted Six
Sigma (Nakhai and Neves, 2009). Early successes in high pro-
file companies such as Motorola, Allied Signal (now Honeywell),
and General Electric helped to both popularize and legitimize
the approach, and dozens of books have been devoted to the
topic.
The practitioner literature documents substantial cost savings
and other benefits from Six Sigma program adoptions (Pande
et al., 2000; Harry and Schroeder, 2000). However, some stillquestion whether these benefits sufficiently exceed the costs of
adoption. Corporate-wide adoption of Six Sigma often involves
considerable investments in consulting support, training, organi-
zational restructurings, and associated information and reporting
systems. For example, over a four year period (19961999)
Corresponding author. Tel.: +1 817 257 7463.
E-mail addresses:[email protected] (M. Swink),[email protected]
(B.W. Jacobs).1 Tel.: +1 517 8846370.
General Electric reportedly spent more than $1.6 billion on Six
Sigma investments. Researchers report that training costs are typi-
cally as much as $50,000 pertrained worker (Antony, 2006; Fahmy,
2006). The net operating effects of these types of investments
have not been rigorously examined. Most scholarly work to date
involves perceptual data from surveys, or financial studies of a
few select companies (Goh et al., 2003; Zu et al., 2008; Gutierrez
et al., 2009; Braunscheidel et al., 2011). In fact, some writers have
even questioned the validity and originality of Six Sigma, calling it
repackaging, a fad, and a PR ploy (Clifford, 2001; Rowlands,
2003).
Other questions pertain to the types of benefits provided by
Six Sigma, and their limitations. A number of researchers discussthe potential for capability gains in one area of performance to be
offset by added constraints or losses in another. In particular, Six
Sigma potentially creates a trade-off between gains in efficiency
versus growth. Several important studies suggest that process
improvement regimes can stifle innovative exploration in favor
of exploitation, thus impeding sales growth (Abernathy, 1978;
Tushman and OReilly, 1996; Benner and Tushman, 2002, 2003;
Naveh and Erez, 2004). Moreover, recent anecdotes from compa-
nies like General Electric and 3 M indicate that managers believe
Six Sigma practices may severely constrain innovation needed to
drive growth (Brady, 2005; Hindo, 2007).
0272-6963/$ seefrontmatter. 2012 Published by Elsevier B.V.
http://dx.doi.org/10.1016/j.jom.2012.05.001
http://localhost/var/www/apps/conversion/tmp/scratch_6/dx.doi.org/10.1016/j.jom.2012.05.001http://www.sciencedirect.com/science/journal/02726963http://www.elsevier.com/locate/jommailto:[email protected]:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_6/dx.doi.org/10.1016/j.jom.2012.05.001http://localhost/var/www/apps/conversion/tmp/scratch_6/dx.doi.org/10.1016/j.jom.2012.05.001mailto:[email protected]:[email protected]://www.elsevier.com/locate/jomhttp://www.sciencedirect.com/science/journal/02726963http://localhost/var/www/apps/conversion/tmp/scratch_6/dx.doi.org/10.1016/j.jom.2012.05.0017/30/2019 Tema 11 i CA Six Sigma Success
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438 M. Swink, B.W. Jacobs/ Journal of Operations Management 30 (2012) 437453
Limitations might also stem from the context within which Six
Sigma is adopted. Like many process improvement programs, Six
Sigmaoriginated in manufacturingfirms; manyof its principlesand
tenets were developed in a setting of asset-intensive, repeatable
processes. The name itself, Six Sigma, refers to limits in mea-
surable variations of outputs that were established in Motorolas
manufacturing processes. In addition, researchers maintain that
a firm must possess certain resources and make certain commit-
ments in order to make Six Sigma successful (Antony et al., 2008;
Schroeder et al., 2008). Hence, Six Sigma methods and tools may
be more or less effective in certain technological and operational
contexts.
In thisarticle,we examine the operatingperformance impacts of
SixSigma adoptions. Thestudy seeksanswers tothe followingthree
research questions. First, does Six Sigmaadoption consistently pro-
duce a significant net effect on operating performance? Given the
widespreadadoptionand continued popularity of this program, we
consider this a very important question. A sizable literature on the
efficacy of other process management strategies exists, providing
mixed results. However, researchers argue that Six Sigma is differ-
ent fromother process management approaches; it is distinguished
by its requisite organizational structures, structured methods, and
emphasis on customer-oriented metrics (Linderman et al., 2003;
Sinha and Van de Ven, 2005; Schroeder et al., 2008). Given these
proposed distinctions, it is important to determine whether or not
managers should have reason to expect that Six Sigma will provide
benefits that exceed alternative programs for improvement.
Our second research question addresses the nature of Six
Sigmas impacts. Whattypes of beneficialimpacts are manifestedin
theoperating data of SixSigmaadopters? By examiningthe compo-
nents of both profit and growth-oriented financial outcomes of Six
Sigma adopters, we develop insights into the types of impacts pro-
vided by theprogram.These results serve to informthe debateover
the roles of process management programs in creating competitive
advantages for their adopters; they also point to some interesting
propositions for future research.
Our third research question is: are Six Sigma impacts related to
operating contexts? As Six Sigma adoptions have grown to includea wider scope of businesses, researchers have begun to question
the applicability and effectiveness of related tools and techniques
in certain contexts. In addition, case studies and anecdotal evi-
dence is suggestive of factors that may be critical to successful
implementation. We study differences in Six Sigma success asso-
ciated with industry (manufacturing or service), labor intensity,
R&D intensity, prior operating performance, and quality maturity.
Our examination of these factors provides insights into the sources
of, and constraints on, process improvements emerging from Six
Sigma adoption.
We address the foregoing questions through an event study
methodology, comparing financial data for about 200 Six Sigma
adopting firms against data for matched firms, which provide
control groups for the analyses. We employ various matching pro-cedures using different combinations of pre-adoption operating
performance (measured by return on assets (ROA)), industry, and
size as matching criteria. By comparing performance outcomes
across a hierarchy of operating metrics, we establish a pattern
of Six Sigma adoption effects that provides strong evidence of a
positive impact on ROA. Interestingly, these ROA improvements
arise mostly from significant reductions in indirect costs. Improve-
ments in direct costs and asset productivities are not evident. We
also find small improvements in sales growth due to Six Sigma
adoption.From cross-sectional analyses, we determine that perfor-
mance improvement due to Six Sigma adoption is not significantly
related to industry (manufacturing or service) or R&D intensity.
However, changes in performance are significantly correlated with
the quality maturityof the adopting firms. Interestingly, firms with
greater quality experience (as indicated by ISO 9000 certification)
appearto benefitlessfromSix Sigma. Forfirmsin service industries,
operating performance before Six Sigma adoption is a significant
determinant of performance changes. However, labor intensity is
the most significant driver of performance benefits in manufactur-
ing firms.
In the next section, we formulate hypotheses relating Six Sigma
adoption to operating performance by drawing uponthe literatures
on process improvement in general, and Six Sigma in particular.
Section 3 describes the sample data and event study method. Sec-
tion 4 presents the results. Section 5 discusses the findings and their
implications. Section 6 summarizesthe conclusions and limitations
of the study, and identifies opportunities for future research.
2. Theory development and hypotheses
Researchers have placed Six Sigma in the realm of operational
improvement programs that are oriented toward improvements in
quality or variability of process outcomes (Zu et al., 2008). There
are several scholarly studies of the impacts of process improve-
ment programs, yet none provide a rigorous examination of Six
Sigma adoptions. The existing literature can be classified into
three streams addressing the performance impacts of: (1) gen-eral process management strategies (Ittner and Larcker, 1997;
Schmenner, 1991), (2) Total Quality Management (TQM) imple-
mentations (Hendricks and Singhal, 1996, 1997, 2001a,b; Ittner
et al., 2001; Powell, 1995; Sila, 2007; York and Miree, 2004; Nair,
2006), and (3) ISO 9000 and other quality certifications (Corbett
et al., 2005; Martinez-Costa et al., 2009; Westphal et al., 1997;
Yeung et al., 2006; Naveh and Erez, 2004; Benner and Tushman,
2002; Benner and Veloso, 2008; Levine and Toffel, 2010). These
research streams provide an overall positive, though mixed, set
of conclusions regarding the effectiveness of respective process
improvement programs. Importantly, however, researchers have
argued that Six Sigma is distinguished from these other programs
by several characteristics.
2.1. The distinctive characteristics of Six Sigma
Researchers describe Six Sigma as a data driven approach to
problem solving, as a business process, as a disciplined statistical
approach, and as a management strategy (Blakeslee, 1999; Hahn
et al., 1999; Harry and Schroeder, 2000;Braunscheidel et al., 2011).
While these monikers have been applied to other process improve-
ment strategies as well, proponents and researchers argue that
Six Sigma is different than other process improvement programs
because it is exclusively a customer-driven and data-defined sys-
tem(Breyfogle, 2003). Schroederet al. (2008)suggestthat SixSigma
must be different by virtue of the fact that it has been adopted by
many firms that had already possessed quite mature quality man-
agement processes (e.g., 3M, Ford, Honeywell, American Express).Perhaps more compellingly, Schroeder et al. (2008) and Zu
et al. (2008) argue that, while Six Sigma shares some philosoph-
ical underpinnings and techniques with other quality and process
management approaches, it is distinguished by four attributes
of its unique organizational approach. Schroeder et al. (2008, p.
540) define Six Sigma as an organized, parallel-meso structure
used to reduce variation in organizational processes by employing
improvement specialists, a structured method, and customer-
oriented performance metrics with the aim of achieving strategic
objectives. The typical parallel-meso structure for Six Sigma
includes a centralized office within the firm that oversees a dis-
persed training and project execution hierarchy. The central office
has several purposes. It creates an authoritystructurethat acquires,
develops, and assigns resources for training and improvement
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M. Swink, B.W. Jacobs/ Journal of OperationsManagement 30 (2012) 437453 439
projects.It alsousually assemblesan executive team(or teams) that
sets criteria and guides improvement project selection (Carnell,
2003; Snee and Hoerl, 2003). In addition, this structure enables
top management engagement and status reviews (Schroeder et al.,
2008). By engaging high level managers in a centralized way, Six
Sigmaprojectsare thought to beless myopicand more aligned with
business strategy. Finally, the structure purportedly affords more
effective diffusion of lessons learned from projects, thus creating
greater multi-level understanding (Sinha and Van de Ven, 2005).
Six Sigma programs involve a variety of both part-time and
full-time improvement specialists, including champions (execu-
tive project sponsors), master black belts, black belts, green belts,
and lower level designations. The different belts denote different
levels of training and experience with Six Sigma methods. Mas-
ter black belts are typically full-time trainers and project mentors,
while black belts and green belts are workers who may apply Six
Sigmaconcepts andtoolsto drive improvementsin their respective
areas of functional responsibility. Black belts often have the same
leadership characteristics as heavyweight project managers (Clark
and Fujimoto, 1991). This hierarchy of improvement specialists
is thought to enhance the coordination of work across organiza-
tional levels (Sinha and Van de Ven, 2005; Barney, 2002), and again
to ensure the matching of tactical tasks with business strategy
(Henderson and Evans, 2000; Linderman et al., 2003).
Six Sigma improvement projects follow a structured method
which has been recognized as a variation of the Plan, Do, Check,
Act (PDCA) cycle (Shewhart, 1931; Schroeder et al., 2008). The
Six Sigma method includes five steps known as Define, Measure,
Analyze, Improve, and Control (DMAIC). A variant of the method
used in design-oriented processes is Define, Measure, Analyze,
Design, and Verify (DMADV). Choo et al. (2007) suggest that the Six
Sigma method provides an effective learning framework to guide
knowledge acquisition and to ensure that project team members
execute a more complete search of problem solving alternatives.
It also provides a common language enabling workers to effec-
tively communicate project status and to make comparisons across
improvement efforts.
Finally, Six Sigma projects are guided and assessed by a mix-ture of common and unique performance metrics. In addition
to using typical financial and operational project metrics, Six
Sigma applies unique measures including process sigma, critical-
to-quality (CTQ) attributes, and defects per million opportunities
(DPMO). Researchers argue that such metrics establish challeng-
ing goals and guidance for project teams (Linderman et al., 2003;
Pande et al., 2000), that they focus on objective data which tend to
mitigate political agendas (Brewer, 2004), that they embody out-
comes at business, process, and project levels, and ultimately that
they prioritize a customer focus.
2.2. Six Sigmas operating performance impact
While the popular press contains examples of both positiveand negative Six Sigma impacts on performance (e.g., Pande et al.,
2000; Harry and Schroeder, 2000; Chakravorty, 2010), few rigor-
ous studies exist. A study of twenty firms by Goh et al. (2003)
indicates that announcements of Six Sigma adoption produce no
significant changes in stock returns on announcement day. A fur-
ther analysis of six of the firms shows that their long run stock
performance is not better than the S&P 500. Zu et al. (2008, p.
643) find that Six Sigma practices and traditional QM practices
work together to generate improved quality performance, which
then leads to higher business performance. However, they base
these conclusions on self reported, perceptual measures of quality
and performance. Also using a survey with perceptual measures,
Gutierrezet al. (2009) find that SixSigma improves shared vision,
but relationships to self-reported organizational performance are
not significant. Braunscheidel et al. (2011) conducts case studies
of seven firms and concludes that Six Sigma leads to documented
savings and perceived innovation benefits.
The fundamental argument for net positive financial impacts
of Six Sigma adoption is that it creates new learning and adap-
tation capabilities within the firm. In short, Six Sigma is thought
to both provide a structure and promote a culture that fosters
problem/opportunity identification, process analysis, and the cre-
ation of sustained improvements. Gowen and Tallon (2005) use
the dynamic capabilities theoretical perspective to describe the
learning and adaptation capabilities associated with Six Sigma
adoption. This perspective emphasizes the need for organizations
to dynamically align their processes with changes in the busi-
ness environment. Dynamic capability is defined as a learned and
stable pattern of collective activity through which the organiza-
tion systematically generates and modifies its operating routines
in pursuit of improved effectiveness (Zollo and Winter, 2002,
p. 340). Dynamic capabilities enable organizations to efficiently
adapt, integrate, and reconfigure resources (Teece et al., 1997).
Gowen and Tallon (2005) argue that, by addressing both techni-
cal designs and human resources, the structured approach of Six
Sigma imbues the adopting organization with greater dynamic
capabilities. In essence, the structured attributes of best practice
identification, customer focus, and disciplined project selection
and execution provide organizationalarchitecture neededfor these
capabilities. Gowen andTallon (2005)further suggest that effective
Six Sigmaimplementations embody the value, rareness, inimitabil-
ity, and non-substitutability (VRIN) characteristics associated with
resources that provide competitive advantage, as specified in the
resource-based view (Barney, 2002). Theirsurvey dataindicatethat
managers perceive this to be true; they findsignificant correlations
between various Six Sigma program design factors and each of the
VRIN elements.
More generally, Anand et al. (2009) describe infrastructural ele-
ments of continuous improvement programs that foster dynamic
capabilities. Indeed, they represent continuous improvement itself
as a dynamic capability, when it is embedded in a comprehen-
sive organizational context (p. 445). They further identify andstudy Six Sigma as a particular continuous improvement ini-
tiative that provides such a context. Their case study analyses
indicate that infrastructural elements such as balanced innova-
tion and improvement, a constant change culture, standardized
improvement processes, and training are important enablers of
organizational learning and dynamic capabilities.
Consistent with the above arguments, other researchers also
suggest that structured improvement methods lead to better orga-
nizationallearningand knowledge transfer (Ittneret al., 2001;Choo
et al., 2007; Molina et al., 2007), as well as overall improved job
quality (Levine and Toffel, 2010). Linderman et al. (2003, 2006)
demonstrate that the interaction of the structured method and
rigorous goal setting of Six Sigma explains its impact on the perfor-
manceof specific projects. Other researchersargue thatadvantagesfrom process improvement programs derive mainly from social
aspects (Powell, 1995), including a supportive learning culture
(Detert et al., 2000; Schroeder et al., 2008; Naor et al., 2008) and
cooperative values (Kull and Narasimhan, 2010). Gutierrez et al.
(2009) maintain that Six Sigma adoption creates a shared vision
that impels team members to work together to achieve common
goals.
An integration of these arguments and findings suggests that
Six Sigma adoption provides a structured approach to organi-
zational learning that creates dynamic capabilities, specifically,
capabilities to consistently improve current processes (Ittner and
Larcker, 1997), thereby raising quality and lowering costs. Teece
(2007) maintains that such capabilities are critical for business
success; due to the increasing pace and complexity of business
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440 M. Swink, B.W. Jacobs/ Journal of Operations Management 30 (2012) 437453
environments, organizations no longer compete on processes, but
on the ability to continually improve processes.
In order to test this proposition, our starting point is to test the
hypothesis that Six Sigmaadoption positively impacts overall prof-
itability, as commonly measured by ROA (e.g., Corbett et al., 2005;
Hendricks and Singhal, 1997), and defined as operating income per
total assets, assessed before depreciation.
Hypothesis 1. Six Sigma adoption produces a significant positive
effect on profitability (ROA).
Profitability ( ROA) is determined by return on sales (ROS,
defined as operating income/sales) and asset turnover (ATO,
defined as sales/assets). In turn, both ROS and ATO can also be
broken down into their components, including cost of goods sold
(COGS), sales,general,and administrativeexpenses (SG&A), current
andfixed assets, etc. Consistent with our second research question,
should hypothesis H1 be supported, we plan to examine significant
differences in the constituent elements of ROA in order to develop
insights into the nature of contributions attributable to Six Sigma
adoption.
In addition to profit, companies commonly seek growth, often
measured by year-on-yearincreases in sales.A debatehas emerged
over the effects of process management programs on the growth
of adopting firms. Researchers suggest that the disciplined struc-
ture of process management programs tends to crowd out growth
oriented innovation in favor of exploitation (Benner and Tushman,
2002, 2003). Emphases on waste reduction, standardization, and
continuous improvement are sometimes considered incompatible
with the slack resources, flexibility, and risk taking propensities
needed to support more explorative efforts. At least two stud-
ies provide evidence of such effects from ISO 9000 certification.
Benner and Tushman (2002) posit that an emphasis on process
management biases innovation project selection toward incre-
mental improvements and away from more exploratory efforts.
In a longitudinal study of patent activity in the photography and
paint industries, they document an increased share of the firms
total innovations that are exploitative and build upon existing firmknowledge, post ISO 9000 certification. Similarly, Naveh and Erez
(2004) find that ISO 9000 certification is positively associated with
greater process control, but negatively associated with innovation
outcomes.
As a process management strategy, Six Sigma has been criti-
cized as being narrowly designed to improve existing processes,
and not being helpful in the development of new products or dis-
ruptive technologies needed to drive sales growth (Morris, 2006).
Hence, the dynamic capabilities stemming from Six Sigma adop-
tion could be considered to be limited to continuous improvement,
rather than also applying to more radical changes (Anand et al.,
2009). Indeed, reports fromwell-known companiessuch as General
Electric and3M document managersfeelingsthatthey need topro-
tect growth-oriented functions in the firm (e.g., marketing, R&D)from possible strictures imposed by Six Sigmas disciplined struc-
ture (Brady, 2005; Hindo, 2007; Parast, 2011). However, Schroeder
et al. (2008) counter these concerns by maintaining that Six Sigma
provides switching structures that simultaneously promote the
conflicting demands of exploration and control in the improve-
ment effort (p. 537). Further, they identify Six Sigma black belts as
boundary spanning actors, who integrate strategic concerns with
tactical improvement efforts, thus facilitating exploration (Manev
and Stevenson, 2001). Finally, the active engagement of top man-
agersin project selection andmonitoring are thought to foster more
strategicand exploratory efforts. Theseconflicting viewsleave open
thequestionof whether SixSigmaadoptionaids growthfrom inno-
vation, whether it stifles it, or whether it has no significant effect
at all.
Six Sigma adoption may produce other effects on sales growth
that are independent of its effects on innovation and exploration.
Six Sigma adoption can also serve as a signal to customer markets
of improved product quality. First, as Six Sigma has gained notori-
ety (Gowen and Tallon, 2005), the reputational effects of adoption
have potentially created greater pricing power for adopting firms.
Second, real increases in product quality can be expected to lead to
higher customer satisfaction. As a result, customers may be willing
to pay more or to buy more; both outcomes increase sales revenue.
These expectations are consistent with the findings ofHendricks
and Singhal (1997) and Corbettet al. (2005); thesetwostudiesshow
significant increases in sales for firms that won quality awards and
obtained ISO 9000 certifications, respectively.
In sum, the foregoing discussion describes three mechanisms
through which Six Sigma adoption might foster greater sales
growth: (1) through supporting product innovation (though this
is contested in the literature), (2) through reputational enhance-
ments that improve product and brand image, and (3) through
process improvements that create better product quality (fewer
defects). All three effects presumably lead to improved customer
satisfaction and associated growth in sales.
Hypothesis 2. Six Sigma adoption produces a significant positive
effect on sales growth.
2.3. Contextual drivers of Six Sigma implementation success
Our final research question addresses potential relationships
between Six Sigma impacts and the operating context of adop-
tion. The nature of our study affords us the opportunity to examine
a number of contextual factors that can enhance or impede an
adopting firms abilities to extract real benefits from Six Sigma
implementation.
2.3.1. Manufacturing versus service
Of particular interest are differences in adoptions by primarily
manufacturing versus primarily service firms. Case studies doc-
ument Six Sigma adoptions in a wide variety of service firms
including hospitals, government, banks and financial services, util-
ities, fitness clubs, retailers, and so on. Some researchers study Six
Sigma programs and projects in both manufacturing and service
firms as if they are universal (Schroeder et al., 2008; Nair et al.,
2011). However, others identify limitations and challenges of Six
Sigma methodology and tools in service settings. Some argue that
it is harder to measure outcomes, collect reliable data, and control
service processes (Hensley and Dobie, 2005; Antony et al., 2007).
The ambiguous and customer-specific nature of critical-to-quality
service features can make them difficult to define, such that Six
Sigma metrics such as DPMO are unnecessarily stringent and dif-
ficult to apply (Nakhai and Neves, 2009). Moreover, common Six
Sigma tools and training topics may not adequately address differ-
ences between service customers expectations and perceptions.
For example, Six Sigma does not typically address marketing com-munications or other influencers of customers expectations. In
addition, Six Sigma rarely addresses softer dimensions of service
quality such as empathy (Nakhai and Neves, 2009). A survey by
Antony et al. (2007) indicates that core Six Sigma methods such as
statistical tools, process capability, and design of experiments are
among the least commonly used tools in services. Their research
also suggests that service process improvements are more depen-
dent on organizationalchanges than manufacturing improvements
are, and service organizations are at the same time more resistant
to change because of the higher personal involvement of workers.
Accordingly, we posit:
Hypothesis 3. The positive effect on profitability from Six Sigma
adoption is greater for manufacturing firms than for service firms.
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2.3.2. Labor intensity
Rather than drawing contrasts between manufacturing and ser-
vice firms, the more salient differentiator might be whether firms
in heavier, more asset-intensive industries experience different
results from Six Sigma adoption than those of firms in more labor-
intensive industries. Hendricks and Singhal (2001b) argue that
labor-intense firms provide more fertile ground for quality pro-
cess improvements because they have more process options and
because they depend more on training and skills. Because labor-
intensive processes are inherently more variable, they likely offer
a greater range of variance reduction opportunities. Indeed, vari-
ance reduction is the essential value in the Six Sigma paradigm. On
theotherhand, highlyautomated processestend to beless variable.
Furthermore, because of theirhigh fixedcosts, processes employing
heavy and automated equipment typically already receive spe-
cialized attention by highly trained process and manufacturing
engineers, even in the absence of Six Sigma. Therefore, the impacts
of Six Sigma adoption in these contexts may be muted by compar-
ison.
Hypothesis4. The effect on profitability from Six Sigma adoption
is positively associated with the firms labor intensity.
2.3.3. R&D intensityGiven the debate over the effects of Six Sigma adoption on inno-
vation and growth, we are motivated to evaluate the relationship
between R&D intensity and the performance effects of Six Sigma
adoption. As noted in the discussion for hypothesis H2, a stream of
research indicates that process management programs such as Six
Sigma can exert a stifling effect on more exploratory innovation
efforts. We would expect this effect to be particularly damaging
to overall firm performance if the firm has strategically positioned
itself as a leading innovator. As was the case documented at 3M
(Hindo, 2007), if advanced R&D is the firms lifeblood, then Six
Sigmas highly structured approach to continuous improvement
might be regarded negatively by employees, potentially raising
resistance to change and hampering implementation. On the other
hand, if Six Sigma is embraced, organizational units might beat least implicitly encouraged to favor incremental exploitation
projects over more radical exploratory efforts, thus destroying the
innovative firms competitive advantage.
Similarly, the benefits of Six Sigmas program structure might
be heightened or lessened by the importance of staying techno-
logically current. Again, if a firm positions itself as a technology
leader, danger is associated with becoming too focused on the sta-
tus quo. In such a context, continuous improvement efforts that
focus on process refinement might be less important to success
than efforts aimed at uncovering replacement technologies and
entirelynew opportunities.As mentionedearlier,researchers argue
that Six Sigma builds dynamic capabilities and an organizational
learning infrastructure that enables adopting firms to adapt more
readily to changing environmental conditions (Gowen and Tallon,
2005; Anand et al., 2009). The central question becomes whether
these abilities are limited to incremental changes, or whether they
also apply to more dynamic, technologically intensive contexts.
We forward the following hypothesis as a frame for testing these
competing propositions.
Hypothesis5. The effect on profitability from Six Sigma adoption
is associated with the firms R&D intensity.
2.3.4. Prior financial performance
Following the logic ofHendricks and Singhal (2008), we rec-
ognize that prior operating performance can potentially affect the
impacts of Six Sigma on performance, in two different ways. First,
implementation resources can be a function of the firms pre-
adoption profitability. Highly profitable firms likely have greater
reserves of cash and other needed resources to invest in process
management infrastructural changes, training, and administration
(Hendricks and Singhal, 2008). Therefore, such firms are likely to
affect broader and more complete implementations. In addition,
available resources enable adoption on a larger scale. For example,
profitable firms will likely have the capital needed to fund a larger
number of simultaneous improvement projects. In these ways,
profitable firms can attain greater leverage from Six Sigma adop-
tions, thus leading to greater abnormal operating performance.
On the other hand, poor performing firms may be better posi-
tioned for the changes required by Six Sigma adoption. Poor
profitability can be a source of motivation; i.e., employees in a
loss-making firm might have a greater sense of urgency needed
to implement organization changes like Six Sigma. Kotter (1995)
suggests that poor business results can increase the probability of
successful implementation of organizational changes, because the
need for change is more apparent, and consequently the urgency
and motivation required for successful implementations is more
readily found in poor performers. As a result, levels of top manage-
ment and organizational commitment may be higher, leading to
more aggressive goal setting and a more effective implementation.
Numerous writers highlight the importance of such commitment
to Six Sigma success (e.g., Chakravorty, 2009; Antony et al., 2008;
Linderman et al., 2006; Kumar and Antony, 2009)
Hypothesis6. The effect on profitability from Six Sigma adoption
is associated with the firms prior financial performance.
2.3.5. Quality maturity
Schroeder et al. (2008) note that some firms adopting Six Sigma
already have quite mature quality management processes. This
prompts the question of whether the impacts of Six Sigma on
firm performance are contingent upon the firms prior quality
management knowledge. If Six Sigma simply replicates the capa-
bilities engendered by other quality management programs, then
we might expect little additional performance gain from Six Sigmaadoption. If, on the other hand, Six Sigmas program attributes are
trulydistinctive,as a number of researchersassert (Breyfogle, 2003;
Schroederet al., 2008;Zu et al., 2008), then we might expectunique
performance gains.
Operating on the premise that Six Sigma is related to, but truly
distinctive from, other programs, an absorptive capacity perspec-
tive would suggest that more quality mature firms possess greater
abilities to acquire, evaluate, assimilate, and exploit Six Sigma pro-
cess knowledge (Zahra and George, 2002). Cohen and Levinthal
(1990) describe absorptive capacity as an organizational ability to
embrace and exploit new knowledge. Further, they argue that this
ability depends on priorknowledge andexperience.Relatedknowl-
edge and experience provides a foundation for new knowledge
absorption, as it creates familiarity and lessens causal ambiguities.For example, organizations experienced in quality management
programs would likely speak much of the language of Six Sigma,
even before adoption. In addition, employees who have gone
through similar organizational transformations are likely to feel
less threatened by Six Sigma-driven changes. For these reasons,
we expect that firms experienced with quality oriented process
managementprograms willimplementSix Sigmafaster,more com-
pletely, andperhaps more effectively.As a result, they shouldenjoy
greater performance benefits from Six Sigma program adoption
than their less experienced counterparts.
Hypothesis 7. The effect on profitability from Six Sigma adop-
tion is positively associated with the firms quality maturity (prior
quality program experience).
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3. Researchmethod
3.1. Sample collection and description
We used multiple sources web searches, books, practitioner
journals, and academic journals to identify a preliminary list of
over 600 companies named as adopters of the Six Sigma philoso-
phy and methodologies. While the list is certainly not exhaustive,
it appears to be fairly representative, as it includes a wide range
of industries, firm sizes, and adoption years. Of the identified
firms, 421 are publicly traded companies with financial data in
Compustat.
To corroborate whether the identified firms actually adopted
Six Sigma, and to determine their Six Sigma adoption dates, we
used key words such as Six Sigma in conjunction with history
or adoption to thoroughly search publicly available documents
(e.g., all publication sources in the Factiva database, corporate 10-
K reports, corporate websites, the Internet) for each of the 421
publicly traded firms. We retained in the sample only firms that
had adopted Six Sigma in 2007 or earlier, in order to have suf-
ficient data to establish post-adoption performance. Using these
sources, we found definite, pre-2008 adoption years for 214 of
the 421 firms (50.8%). For 143 firms (34.0%), we could not deter-
mine a specific adoption year, but we found enough evidence
of Six Sigma activity to establish a late bound for adoption. For
the remaining 64 firms (15.2%), we did not find sufficient infor-
mation to establish either an adoption date or a late bound for
adoption.
Because the available public information sometimes required
interpretation and/or was conflicting from different sources, each
firm was researched independently by two members of our
research team. The two independent researchers agreed on 351 of
421 findings (83.4%) of adoption dates, late bounds, or no adoption
dates. For the remaining 70 firms with disputed adoption dates or
late bounds, the mean (median) difference in designated adoption
years was0.6(1.0)years. Toresolve thedifferences, wesupplied the
datasourcesdiscoveredby the two researchersto a thirdresearcher
whoindependently weighed theevidence anddetermined thespe-cific adoption years for use in our analyses.
Early on in this research, we sent a survey to each publicly
held Six Sigma adopter for which we could identify a credible
contact person. The survey asked about adoption date and extent
of adoption of the aforementioned practices that are distinct to
Six Sigma (centralized team structure, improvement specialists,
structured methods/DMAIC, and customer-focused metrics). We
secured survey responses from 58 of the 214 publicly traded firms
with identified adoption dates (23.8% of our sample). Of the 58
single respondents: 38 (65.5%) agreed with our identified adop-
tion years; 9 (17.6%) were unable to provide a specific adoption
year; 7 (12.1%) supplied an adoption date one year earlier than our
finding; 3 (5.9%) supplied an adoption date more than one year
later than our finding; and 1 (1.7%) supplied an adoption date oneyear later than our finding. We note that all three respondents
with adoption dates greater than one year later from our finding
were reporting only for their division within the overall firm. To be
conservative, we usedthe earliest adoptionyear identified. Further-
more, the survey data indicate a remarkably uniform application
of Six Sigma practices across the respondents. For example, over
90% of the respondents indicatedthat they employed a black/green
belt structure, and over 95% designated that DMAIC and other Six
Sigma tools were used on at least 80% of improvement projects.
These results reinforce our overall confidence in the accuracy of
our estimates for both the timing and extent of adoptions in our
sample firms.
For the 214 firms with specific adoption years, Panel A of
Table 1 presents the number of adopting firms by year. The earliest
Table 1
Sampledescriptionfor 214 firms with specific Six Sigma adoptionyears.
Panel A: Frequency of Six Sigma adoption years
Year Frequency Year Frequency
1986 2 1997 16
1987 0 1998 12
1988 2 1999 18
1989 1 2000 38
1990 1 2001 40
1991 1 2002 18
1992 1 2003 18
1993 0 2004 15
1994 0 2005 9
1995 2 2006 13
1996 3 2007 4
Panel B: Occurrence (percentage) of most-frequent SIC codes
2-Digit code Frequency 3-Digit code Frequency
35 24 (11.2%) 371 12 (5.6%)
36 23 (10.7%) 602 11 (5.1%)
28 21 (9.8%) 357 10 (4.7%)
37 21 (9.8%) 283 6 (2.8%)
38 14 (6.5%) 367 6 (2.8%)
adoption year in oursample is 1986 andthe most frequently occur-
ring adoption year is 2001. We note the drop-off in Six Sigma
adoptions in our sample post-2001. Given the continued interest
and relevance of Six Sigma,as evidenced by academic publications,
current business school textbooks and curriculums, and practi-
tioner seminar offerings, we suspect that the drop-off of Six Sigma
adoptions in our sample is indicative of non-newsworthiness. In
other words, Six Sigma has become an accepted part of everyday
business, much like TQM or Lean. This highlights the importance
of rigorously studying the impact Six Sigma adoption on operating
performance.
Table 1 Panel B presents themostfrequently occurringSIC codes
within thesample firms. The sample contains firms from 47 unique
two-digit SIC codes and 101 unique three-digit SIC codes. Thoughthe majority of firms represent manufacturing industries, about
one-third of the firms are services. Table 1 provides more informa-
tion on the most frequently represented industries. Table 2 Panel
A presents descriptive statistics for our sample based on the 2001
fiscal year, the most common Six Sigma adoption year in our sam-
ple. The median observation in the sample represents a firm with
$5.6B in market value, $7.5B in total assets, $6.2B in annual sales,
$0.8B in annual operating income, and28,300 employees. Forcom-
parison, Table 2 Panel B presents descriptive statistics for the 207
suspected Six Sigma adopters for which we could not determine a
specific adoptionyear. In addition,Table 2 Panel C presents descrip-
tive statistics for all firms listed in the New York Stock Exchange
(NYSE), also for the 2001 fiscal year. In summary, our sample rep-
resents a wide variety of industries,and is not significantly differentfrom the suspected Six Sigma adopters for which we could not
determinea specific adoption year. However, when compared to all
NYSE firms, oursample is notrepresentative of smaller enterprises.
This outcome raises a question regarding thegeneralizability of our
findings, as the cause of the difference is not known. Research indi-
cates that small and medium sized firms are less likely to adopt Six
Sigma, mainly because theylack requisiteresourcesand knowledge
(Antony et al., 2008; Kumar and Antony, 2009). Thus, our sample
firms might be larger because of sampling bias (i.e., larger firms
are more likely to be identified by our sources), but the sample
firms might also be larger because they truly represent the pop-
ulation (i.e., larger firms are more likely to adopt Six Sigma). We
note that large-firm bias is common in OM research. We discuss
this limitation further in Section 6.
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Table 2
Descriptivestatistics for2001,the most frequent sample Six Sigma adoption year.
Market value ($M) Total assets ($M) Sales ($M) Operating income ($M) Employees (000s)
Panel A: Samplefirms (N= 214)
Median 5616.4 7477.1 6204.9 760.2 28.3
Mean 19,394.2 38,370.3 14,205.1 2337.4 51.9
Std Dev 42,211.0 102,806.9 21,799.3 4695.6 69.2
Max 397,831.6 693,575.0 162,412.0 37,966.0 395.0
Min 0.1 44.2 49.1 (5062.0) 0.4
Panel B: Suspected Six Sigma adopting firms without known adoption years (N= 207)Median 4764.1 4684.2 3986.3 494.0 17.8
Mean 23,233.4 24,763.0 11,625.6 1754.2 47.6
Std Dev 52,321.7 76,197.4 24,409.1 3429.2 119.6
Max 392,959.0 695,877.0 218,529.0 29,602.0 1383.0
Min 0.4 0.2
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Table 3
Matching process and benchmark group statistics.
Matching method
Performance in
year 2a, and
industry
Performance in
years 2,3,4b,
and industry
Performance and size in
years2,3,4c, and
industry
Step 1 matchesd 203 200 198
Step 2 matchese 6 6 7
Step 3 matchesf 0 0 1
Total firms matched 209 206 206
Mean group size 34.3 34.1 14.9
Median group size 18.0 21.5 9.0
Maximum group size 470 423 86
No. of groups with a single firm 6 2 8
a Performance definedas ROA in year 2; matching rangeis 90110%.b Performance definedas median ROAin years2, 3, and 4;datarequired in at least two years.c Size defined as medianTotal Assets in years2,3,and4;datarequired in atleast two years; matching rangeis withinfactor of 25.d All firms within thesame two-digit SICcode as thesample firm, and whoseperformanceand/or size arewithin the specified rangeof thesample firm.e All firms within thesame one-digit SICcode as thesample firm, and whoseperformanceand/or size arewithin the specified rangeof thesample firm.f All firms whoseperformance and/or size arewithin thespecifiedrangeof thesample firmregardless of SICcode.
Table 4
Median descriptive statistics for the matching period (years2, 3, and 4) prior to Six Sigma adoption.
ROA Total assets ($M) Market value ($M) Sales ($M) Operating income ($M) Employees (000s)
Panel A: Samplefirms (N= 214)
Mean 0.1362 12,932.2 23,086.6 10,450.9 1882.1 47.9
Median 0.1333 4303.1 5356.9 4889.7 761.9 25.5
Std Dev 0.0755 23,901.5 52,784.0 16,311.7 3672.0 64.5
Max 0.4627 250,138.5 303,989.0 146,991.0 32,291.0 413.0
Min (0.2629) 22.1 8.3 15.7 (22.2) 0.3
Panel B: Benchmark firms (obtained frommedian-performance-size-industrymatching method (N= 3077)
Mean 0.1313 9869.5 3981.7 2928.1 625.2 11.1
Median 0.1307 915.1 735.9 738.1 122.0 3.2
Std Dev 0.0605 44,271.1 20,646.7 8732.1 2014.6 28.4
Max 0.4331 933,559.1 911,494.2 174,694.0 31,750.0 775.1
Min (0.2736) 5.1 1.5 0.2 (9.8)
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Table 5
Annual abnormalchanges in ROA forsample firms foryear1 through year +4.
From year: N Median Z-Statistic Mean t-Statistic % Positive Z-Statistic
Panel A: Performance-industry matching
1 to 0 197 0.121% 0.674 0.001% 0.004 52.28% 0.641
0 to +1 194 0.469% 2.741*** 0.600% 2.884*** 59.28% 2.585***
+1 to +2 191 0.180% 1.040 0.129% 0.633 53.40% 0.941
+2 to +3 181 0.113% 0.578 0.115% 0.491 51.93% 0.520
+3 to +4 166 0.423% 2.192** 0.447% 1.947** 58.43% 2.173**
1 to +4 167 0.355% 2.390***
1.214% 2.729***
55.69% 1.470*
0 to +4 167 0.077% 1.639* 0.994% 2.314** 52.10% 0.542
+1 to +4 167 0.030% 0.917 0.540% 1.406* 50.30% 0.077
Panel B:Median-performance-industrymatching
1 to 0 194 0.256% 1.002 0.130% 0.568 55.67% 1.580*
0 to +1 191 0.378% 2.208** 0.391% 1.904** 58.64% 2.388***
+1 to +2 189 0.241% 2.120** 0.367% 1.834** 56.61% 1.818**
+2 to +3 181 0.099% 0.634 0.034% 0.170 48.07% 0.520
+3 to +4 164 0.552% 1.679** 0.336% 1.497* 57.93% 2.030**
1 to +4 164 0.703% 2.632*** 1.294% 3.108*** 56.10% 1.562*
0 to +4 164 0.530% 2.019** 0.901% 2.262** 56.71% 1.718**
+1 to +4 164 0.411% 1.541* 0.559% 1.730** 55.49% 1.406*
Panel C:Median-performance-size-industrymatching
1 to 0 194 0.055% 0.415 0.049% 0.190 51.03% 0.287
0 to +1 191 0.080% 0.482 0.021% 0.103 50.79% 0.217
+1 to +2 188 0.061% 0.989 0.223% 1.071 51.60% 0.438
+2 to +3 179 0.000% 0.172 0.017% 0.086 50.28% 0.075
+3 to +4 163 0.465% 1.995** 0.471% 1.897** 59.51% 2.428***
1 to +4 163 0.433% 1.900** 1.029% 2.322** 53.99% 1.018
0 to +4 163 0.479% 1.569* 0.914% 2.097** 52.76% 0.705
+1 to +4 163 0.395% 1.696** 0.826% 2.223** 53.99% 1.018
Panel D: One-on-one median-performance-size-industry matching using only early adopters matched against samplefirms that adopted at least fiveyears later
1 to 0 34 0.150% 0.244 0.111% 0.222 55.88% 0.686
0 to +1 34 0.592% 1.362* 0.932% 1.488* 62.16% 1.480*
+1 to +2 34 0.172% 0.904 0.717% 1.244 61.54% 1.441*
+2 to +3 34 0.413% 0.962 0.280% 0.588 41.67% 1.000
+3 to +4 34 1.197% 2.039** 1.220% 2.370*** 58.82% 1.029
1 to +4 34 1.383% 1.406* 1.784% 1.628* 55.88% 0.686
0 to +4 34 1.740% 1.863** 2.025% 2.220** 62.86% 1.521*
+1 to +4 34 1.759% 2.101** 2.211% 2.362*** 66.67% 2.000**
Allsamples trimmed at 2.5% each tail.
Z-Statistics for medians are obtained using Wilcoxon Signed-Rank tests.
Z-Statistics for% positive areobtained using Binomial Sign tests.* Significance is one-tailed:p .10
**
Significance is one-tailed:p .05.*** Significance is one-tailed:p .01.
The change per firm over the 5-yearperiod (from year 0 to+4) is
also significantly positive for both the mean and median, using all
three matching methods. The % firms with positive 5-year changes
are significantly greater than 50% using the median-performance-
industry matching method. As expected, the magnitudes of the 5-
year changes are generally less than those of the 6-year changes.
The change per firm over the 4-year period (from year +1 to +4)
follows a similar pattern and is again generally lesser in magnitude
and significance than the 5-year changes.
As we noted in Section 3.2, a limitation of our study is that we
could notdefinitivelydetermine that all benchmark firms were not
also Six Sigma-adopters during thesampling time frame.If many ofthe benchmark firms were truly adopters, then estimates of abnor-
mal performance would be muted, making significant differences
difficult to detect. The fact that we do find significant differences
suggests that, in the worst case, our findings of abnormal perfor-
mance due to Six Sigma adoption are conservative.
To address this limitation, we would ideally match known
adopters only with known non-adopters. Such a pure comparison
would produce a more reliable estimate of expected performance
improvement from Six Sigma adoption. To approximate this pure
comparison, we identified knownnon-adoptersas firmsfrom our
sample that adopted Six Sigma at least five years later than earlier
sample adopter firms. The five-year delay allows us to consider
operating performance impacts to the early adopters during the
post-adoption period through year +4. Given that we are limited to
only the 214 firms in our sample, and to permit the greatest num-
ber of comparisons, we matched each adopter against only a single
firm, and did not use data from any one firm more than once; this
method permitted 41 matches using the ROA, assets, and industry
criteria from median-performance-size-industry matching. Table 5
Panel D presents the results for abnormal changes in ROA for year
1 through year +4.TheROA improvementsaresignificant andgen-
erally stronger than in our other matching methods. These results
should be regarded as somewhat tentative, given the small sample
size,andtheconfoundingwithtimeduetothismatchingmethodol-
ogy (the adopters all adopted prior to 2003). However, the results
do strongly reinforce the conclusion that the estimated improve-ments from our larger analyses are real, albeit conservative.
Considering boththe annual changes and multiple-year changes
indicates that Six Sigma adoption produces an immediate and per-
sistent positive effect on ROA. These results provide support for
hypothesis H1.
4.2. Decomposition of ROA effects
For brevity in presenting and discussing the remainder of
our results, we concentrate on our most conservative matching
method,median-performance-size-industry, noting that the pattern
of results is similar regardless of matching method. Table 6 Panel
A presents the results for the abnormal changes in the level of ROS
on an annual basis and for multiple-year periods. For the 6-year
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Table 6
Annual abnormal changes in ROS, COGS/sales, SG&A/sales, and ATO for sample firms for year 1 through year +4 using the median-performance-size-industrymatching
method.
From year: N Median Z-Statistic Mean t-Statistic % Positive Z-Statistic
Panel A: Changes in abnormal ROS
1 to 0 194 0.117% 0.320 0.324% 1.406* 51.55% 0.431
0 to +1 191 0.048% 0.009 0.117% 0.492 50.79% 0.217
+1 to +2 188 0.127% 1.151 0.328% 1.449* 51.60% 0.438
+2 to +3 179 0.167% 0.249 0.065% 0.287 52.51% 0.673
+3 to +4 163 0.482% 1.955**
0.364% 1.639*
59.51% 2.428***
1 to +4 163 0.899% 0.937 0.342% 0.774 52.76% 0.705
0 to +4 163 0.632% 1.363* 0.490% 1.114 56.44% 1.645*
+1 to +4 163 0.892% 1.761** 0.596% 1.563* 56.44% 1.645*
+1to +4effecta 163 0.528% 2.063** 0.645% 1.976** 56.44% 1.645*
Panel B: Changes in abnormal COGS/sales
1 to 0 160 0.260% 0.106 0.222% 0.987 45.00% 1.265
0 to +1 159 0.214% 0.193 0.006% 0.026 45.91% 1.031
+1 to +2 157 0.200% 1.927** 0.447% 2.346*** 45.86% 1.038
+2 to +3 149 0.173% 0.344 0.086% 0.407 46.98% 0.737
+3 to +4 135 0.095% 0.738 0.211% 0.952 48.89% 0.258
1 to +4 132 0.533% 0.374 0.281% 0.539 53.03% 0.696
0 to +4 133 0.336% 0.225 0.150% 0.317 51.13% 0.260
+1 to +4 132 0.438% 0.018 0.033% 0.087 46.97% 0.696
Panel C: Changes in abnormal SG&A/sales
1 to 0 160 0.007% 0.319 0.051% 0.370 49.38% 0.158
0 to +1 159 0.096% 0.061 0.123% 0.855 55.35% 1.348*
+1 to +2 157 0.155% 0.762 0.023% 0.156 47.13% 0.718
+2 to +3 149 0.149% 2.277** 0.342% 2.639*** 41.61% 2.048**
+3 to +4 135 0.219% 1.979** 0.297% 2.190** 40.00% 2.324**
1 to +4 132 0.842% 2.846*** 1.094% 2.832*** 38.64% 2.611***
0 to +4 133 0.639% 2.140** 0.771% 2.108** 42.86% 1.648**
+1 to +4 132 0.604% 2.476*** 0.747% 2.707*** 38.64% 2.611***
Panel D: Changes in abnormal ATO
1 to 0 194 0.295% 0.882 0.200% 0.203 44.33% 1.580*
0 to +1 191 0.055% 0.512 0.866% 1.112 49.74% 0.072
+1 to +2 188 0.633% 1.299* 0.680% 0.795 44.68% 1.459*
+2 to +3 179 0.605% 0.626 0.164% 0.184 45.25% 1.271
+3 to +4 163 0.130% 0.802 1.067% 1.199 51.53% 0.392
1 to +4 163 0.004% 0.715 1.944% 0.992 49.69% 0.078
0 to +4 163 0.027% 0.367 1.120% 0.634 49.08% 0.235
+1 to +4 163 0.253% 0.095 0.026% 0.018 48.47% 0.392
Allsamples trimmed at 2.5% each tail.
Z-Statistics for medians are obtained using Wilcoxon Signed-Rank tests.
Z-Statistics for% positive areobtained using Binomial Sign tests.a Effect onROA computed per firm asROS Change+1 to +4 Firm ATO in year0.
* Significance is one-tailed:p .10.** Significance is one-tailed:p .05.
*** Significance is one-tailed:p .01.
periodfrom year1 to +4, the median and meanchange per adopt-
ing firm, and the % of sample firms experiencing positive change,
are all positive but insignificant. For the 5-year period from year 0
to +4,the median change is 0.632%, significantlypositive at the 10%
level. The mean change is 0.490%, positive but insignificant, and
56.44% of firms experience positive changes, significantly greater
than 50% at the 10% level. For the 4-year period from year +1 to +4,
the median and mean changes are 0.528% and0.645%, respectively,
both significantlypositiveat the 5% level, and56.44% of firms expe-
rience positive changes, significantly greater than 50% at the 10%level.
To determine whether the improvement in ROS from year +1
to +4 contributes significantly to the improvement in ROA, we
employ the method ofKinney and Wempe (2002). For each adopt-
ing firm, we compute the effect of ROS on ROA by multiplying the
change in abnormal ROS from year +1 to +4 with the firms ATO at
adoption (year 0). The median and mean ROS effects over the 4-
year period are 0.528% and 0.645%, respectively, both significantly
positive at the5% level, and56.44% of samplefirms experience pos-
itive ROS effects, significantly greater than 50% at the 10% level.
These results indicate that the ROS improvement from Six Sigma
adoption significantly contributes to the overall improvement
in ROA.
The primary cost components that impact operating income
(and hence, ROS and ROA) are COGS and SG&A. To determine the
contributions of bothcomponents, we examined abnormalchanges
in COGS/sales and SG&A/sales. Given that all firms do not consis-
tently report COGS and SG&A separately each year, we included
only adopting firms and benchmark firms in our analyses that did
report both accounts. This eliminated approximately 35 adopting
firms from oursample. Table 6 Panels B andC present theresults for
the abnormal changes in the level of COGS/sales and SG&A/sales,
respectively, on an annual basis and for the multiple-year periodsof interest. Although the median annual changes in COGS/sales are
all negative, only the change from year +1 to +2 is significant. The
mean (median) abnormal change from year +1 to +2 is 0.200%
(0.444%), significant at the 5% (1%) level. For the 6-year period
(year 1 to +4) and the 5-year period (year 0 to +4), the median,
mean, and % positive changes are all positive but insignificant. For
the 4-year period (year +1 to +4), the change statistics are negative
but insignificant. The evidence provides no support that Six Sigma
adoption produces significant reductions in COGS.
Considering the annual abnormal changes in SG&A/sales for the
sample firms, we see that they are consistently negative. Four of
5 (5 of 5) median (mean) annual SG&A/sales changes are negative,
and the % firms with positive changes is less than 50% for 4 of 5
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Table 7
Annual abnormalchanges in sales growthfor samplefirms foryear1 through year +4.
From year: N Median Z-Statistic Mean t-Statistic % Positive Z-Statistic
Panel A: Performance-industry matching
1 to 0 197 1.398% 1.693 ** 0.555% 0.589 43.65% 1.781**
0 to +1 194 0.646% 0.570 1.148% 1.284* 46.91% 0.862
+1 to +2 191 0.664% 0.753 0.498% 0.501 47.64% 0.651
+2 to +3 181 0.305% 1.278 1.613% 1.536* 47.51% 0.669
+3 to +4 166 0.689% 0.250 0.749% 0.853 46.39% 0.931
1 to +4 167 5.718% 0.229 3.179% 0.704 43.11% 1.780**
0 to +4 167 0.094% 0.103 1.985% 0.545 49.70% 0.077
+1 to +4 167 1.533% 0.378 0.034% 0.012 48.50% 0.387
Panel B:Median-performance-industrymatching
1 to 0 194 0.710% 0.260 0.957% 0.950 47.94% 0.574
0 to +1 191 0.532% 0.225 1.101% 1.182 46.60% 0.941
+1 to +2 189 0.486% 0.856 0.417% 0.411 48.15% 0.509
+2 to +3 181 0.030% 0.396 0.208% 0.217 49.72% 0.074
+3 to +4 164 1.540% 2.681*** 3.138% 3.296*** 57.32% 1.874**
1 to +4 164 2.463% 1.725** 11.897% 2.803*** 52.44% 0.625
0 to +4 164 3.485% 1.876** 8.118% 2.416*** 54.88% 1.249
+1 to +4 164 5.631% 1.952** 5.344% 2.028** 56.71% 1.718**
Panel C:Median-performance-size-industrymatching
1 to 0 194 0.008% 0.575 2.127% 1.973** 50.00% 0.000
0 to +1 191 1.485% 0.326 0.774% 0.859 46.07% 1.085
+1 to +2 188 0.271% 0.158 0.237% 0.234 51.06% 0.292
+2 to +3 179 0.030% 0.046 0.071% 0.072 49.72% 0.075
+3 to +4 163 1.259% 1.410* 2.098% 2.345*** 53.37% 0.862
1 to +4 163 0.695% 1.483* 11.022% 2.485*** 50.92% 0.235
0 to +4 163 3.502% 1.078 5.006% 1.422* 53.99% 1.018
+1 to +4 163 6.182% 1.403* 3.759% 1.441* 55.83% 1.488*
Allsamples trimmed at 2.5% each tail.
Z-Statistics for medians are obtained using Wilcoxon Signed-Rank tests.
Z-Statistics for% positive areobtained using Binomial Sign tests.* Significance is one-tailed:p .10.
** Significance is one-tailed:p .05.*** Significance is one-tailed:p .01.
annual changes. Two of the 5 annual changes (from year +2 to +3,
and year +3 to +4) are statistically significant in all three tests for
median, mean, and% positive.For the6-year periodfromyear1 to+4, the median (mean) change is0.842% (1.094%), significantly
negative atthe 1% (1%) level,and 38.64% of samplefirmsexperience
positive median annual changes, significantly less than 50% at the
1% level. For the 5-year period from year 0 to +4, the median and
mean changes are0.639% and0.771%, respectively, both signifi-
cant at the 5% level. 42.86% of firms experience positive changes in
SG&A/sales for the 5-year period, significantly less than 50% at the
5% level. Similarly, the mean, median, and % positive changes for
the 4-year periodfrom year +1 to +4 are allnegative andsignificant
at the 1% level. These results suggest that Six Sigma improvements
significantly and persistently reduce SG&A costs.
Table 6 Panel D presents the results for the abnormal changes
in the level of ATO. Interestingly, the annual changes in abnormal
ATO for the sample firms are generally negative. Five of 5 (2 of 5) median (mean) annual changes are negative, and the % firms
with positive changes is less than 50% for 4 of 5 annual changes.
However, only the change from year +1 to +2 is marginally sig-
nificant. For the multiple-year periods, the median and % positive
changes are allnegative butinsignificant. The results thereforeindi-
cate no significant relationship between Six Sigma adoption and
ATO.
4.3. Sales growth effects
In order to test hypothesis H2, we next consider the effect of Six
Sigma adoption on changes in sales. Table 7 presents the results
for abnormal sales growth on an annual basis, for each of our three
matching methods. We note that, more so than for any other of
our performance measures, the results for abnormal sales growth
appear somewhat sensitive to the matching method employed.
There are at least two plausible reasons for this discrepancy: (1)sales growth data are inherently noisier than our other measures
since they are percentage changes in absolute numbers rather than
differencesin ratiosand(2) changes insalesare correlated with firm
size, so the size control added inmedian-performance-size-industry
matching has a greater effect. Accordingly, we again concentrate
our discussion on the results from the most conservative match-
ing method, median-performance-size-industry, presented in Panel
C. The annual abnormal % changes in sales for the sample firms are
neither consistently positive nor negative. Two of5 (5of 5)median
(mean) annual changes are positive, and the % firms with positive
changes is greater than50% for 3 of 5 annualchanges. Onlythe pos-
itive changefrom year +3 to +4 is statistically significant in thetests
for median and mean. For the 6-year period from year1 to+4,the
median (mean) change is 0.695% (11.022%), significantly differentfrom zero at the 10% (1%) level; 50.92% of sample firms experience
positive changes, insignificantly different from 50%. For the 5-year
period from year 0 to +4, the median and % positive changes are
positive but insignificant; the mean change is 5.006%, significant at
the 10% level. For the 4-year period from year 0 to +4, the median,
mean, and % positive changes are all significantly positive at the
10% level. We conclude that the results provide only limited sup-
port for Hypothesis 2, that Six Sigma adoption positively impacts
sales growth.
The foregoing findings collectively indicate that significantly
improved ROA in adopting firms is primarily due to indirect cost
reductions(SG&A),and perhaps mildly dueto positive salesgrowth.
Both of these changesare reflected in improved ROS, rather than to
improvements in ATO.
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M. Swink, B.W. Jacobs/ Journal of OperationsManagement 30 (2012) 437453 449
Table 8
Estimatedcoefficients (standardized,t-Statistics in parentheses) fromregressions ofabnormal ROAchange fromyear1to year +4usingthemedian-performance-size-industry
matching method.
Independent variables Operationalization Model 1
manufacturing and
Services
Model 2 services
only
Model 3
manufacturing
only
Model 4
manufacturing
only
Intercept 3.875 7.646 3.830 5.589
(1.203) (1.565) (0.907) (1.175)
Manufacturing o r services 1 i f manufacturing,
0 if services
0.074
(0.790)Labor intensity
R&D intensity
Employees/sales
R&D/sales
0.242
(2.932)***0.128
(0.852)
0.343
(3.199)***0.325
(2.661)***
0.042
(0.383)
Po sitive fi nan cial pe rf ormance I ndus tr y- adju sted
ROA if positive,
0 otherwise
0.059
(0.725)
0.358
(2.483)***0.081
(0.831)
0.115
(0.934)
Negativefinancialperformance Industry-adjusted
ROA if negative,
0 otherwise
0.136
(1.664)*0.374
(2.499)**0.103
(1.083)
0.118
(1.131)
ISO9000 e xperience Six S igma a doption
year minus 1st
ISO9000
certification
0.254
(2.551)**0.201
(1.278)
0.207
(1.755)*0.264
(2.024)**
Firm size ln(market value)a 0.027
(0.317)
0.192
(1.307)
0.000
(1.000)
0.067
(0.542)
Adoption year YearO 0.113
(1.200)
0.219
(1.553)
0.110
(0.903)
0.168
(1.172)
New CEO 1 if new CEO in
years 0,1,or 2,
0 otherwise
0.049
(0.612)
0.134
(0.954)
0.037
(0.385)
0.023
(0.220)1
Number of
observations
156 47 109 97
Model Fvalue 2.418** 2.346** 2.758** 2.181**
R2 11.63% 29.63% 16.05% 16.55%
AdjustedR2 6.82% 17.01% 10.23% 8.96%
Allsamples trimmed at 2.5% each tail.a Alternative operationalizations of firm size [ln(Sales), ln(Employees), ln(Total Assets)] yield substantively similar results.* Significance is two-tailed:p .10.
** Significance is two-tailed:p .05.*** Significance is two-tailed:p .01.
4.4. Relating abnormal ROA performance to Six Sigma adoptercontext
To examine the data for support ofhypotheses H3H7, which
address the potential roles of contextual factors in Six Sigma adop-
tions, we perform cross-sectional analyses. To assess the impacts
to operating performance over the entire study period, we use
the abnormal ROA change from year1 to +4, obtained using the
median-performance-industry-sizematching method, as our depen-
dent variable.
Table 8 shows the results for regressions of the abnormal per-
formance of Six Sigma adopters on the contextual variables for the
entire sample (Model 1),the service andmanufacturingsubsamples
(Models 2 and 3, respectively), and the manufacturing subsample
with firms reporting R&D intensity (Model 4). We note that all four
models are significant at the 5% level. We review the results in
the order of our hypotheses and discuss them further in the next
section.
In Model 1, the coefficient for the dummy indicator of manufac-
turing or service industry is not statistically significant, indicating
that the ROA benefits of Six Sigma adoption are not signifi-
cantly greater for manufacturing firms than for service firms.
This fails to support hypothesis H3. Despite the lack of sig-
nificant difference in the overall benefit of Six Sigma adoption
between manufacturing and service firms, we examine Models
2, 3, and 4 to determine whether the contextual factors that can
impact an adopting firms abilities to extract benefits from Six
Sigma implementation differ between firms in manufacturing or
services.
A significant association indicated in the results presented inTable 8 pertains to labor intensity. The labor intensity coefficient is
positive and significantly different from zero at the 1% level for the
total sample and manufacturing subsamples (Models 1, 3, and 4).
Labor intensity is positive but insignificant for the service industry
subsample (Model 2). Thus, hypothesis H4 is supported, but only
for manufacturing firms.
As noted previously, the impact of R&D intensity can only be
evaluated for our manufacturing subsample as most services firms
do not report R&D expenses. The results from Model 4 indicate no
significant effect of R&D intensity on abnormal ROA changes. Thus,
hypothesis H5 is not supported.
The results from Model 1 indicate that pre-adoption profitabil-
ity is significantly correlated with abnormal ROA from Six Sigma
adoption by sample firms, but only at a marginal level (p0.10)
and only for firms with negative financial performance. Given that
the values of the negative financial performance variable are non-
positive by definition, the negative coefficient indicates greater
benefits for Six Sigma adopters with more negative prior financial
performance. The results from Model 2 demonstrate that both pos-
itiveand negative pre-adoption financial performance is associated
with greater abnormal performance for service firms. These results
suggest that, while overall effects of Six Sigma adoption are typi-
cally positive in service firms, they may be amplified by either the
financial strength or weakness of the firm at the time of adoption.
Interestingly, these findings are not significant for manufacturing
firms. Thus, hypothesis H6 is supported, but only for service firms.
The cross-sectional analyses yield significant results for the ISO
9000 experience variable. The ISO 9000 experience coefficients for
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450 M. Swink, B.W. Jacobs/ Journal of Operations Management 30 (2012) 437453
thetotal sample and manufacturingsubsamples (Models 1, 3, and4)
are significantly negative at the 5%, 10%, and 5% levels, respectively.
To understand this counter-intuitive result better, we examined
the raw data. For the 21 firms that were certified to ISO 9000 after
Six Sigma adoption, the median and mean abnormal ROA changes
from year 1 to year +4 are 2.615% and 3.579%, respectively, both
significantlygreater than zero at the1% level. Forthe 124firms that
were ISO9000certifiedprior to SixSigmaadoption, themedian and
mean abnormal ROAchanges are0.501% and0.471%,neither signif-
icantly different from zero. The differences in medians and means
between thesetwo groups are bothsignificantly differentfrom zero
at the 1% level. This suggests that firms with greater quality matu-
rity benefit less from Six Sigma adoption, a finding counter to our
hypothesis H7.
We note that none of our control factors firm size, adoption
year, new CEO significantly impact the benefits from Six Sigma
adoption.
5. Discussion of the results
Overall, the results indicate that the benefits of Six Sigma
adoption tend to more than compensate for associated costs and
required investments. Recalling that our estimates are conserva-
tive, SixSigmaadopters shouldexpect an addition to ROAof at least
0.20.3 percentage points each year on average. These magnitudes
of change are both statistically and economically significant. The
results from the median-performance-size-industrymethod, which
are the most conservative, indicate that the sample firms abnor-
mal ROA increased on average by 1.029 percentage points in total
over the 6-year period from year1 to +4, or an average of 0.206
percentage points improvement per year. Given that the median
samplefirmhadanROAof13.22%inyear2, thischange represents
a 7.8% improvement relative to non-adopting firms.For themedian
sample firm with $7.0B in assets in year2, such an improvement
translates into roughly $220M in additional operating income over
the 6-year period for the same asset base.
While quite significant, this ROA boost nevertheless appears tobe modest in comparison to results indicatedin other process man-
agement event studies. Corbett et al. (2005) find an average yearly
ROA increase of 0.89 percentage points associated with ISO 9000
certification. Hendricks and Singhal (1997) find an average yearly
ROAincrease of5.01percentage pointsin years1to+3forwinners
of quality awards. We hesitate to conclude that Six Sigma actually
offers less of an impact than these other programs, however. As we
noted earlier, estimates from our study are conservative, given the
possibility thatsome firmspopulating our benchmark groups could
have also adopted Six Sigma before or during the sampled time
horizon. Indeed, the average ROAboost indicated from our one-on-
one matching process (Table 5 Panel D) is nearly double that of the
more conservative matching process. While Hendricks and Singhal
(1997) cite similar potential pollution of benchmark groups as alimitation oftheirstudy, itis importantto note that they studyqual-
ity award winners, i.e., successful purveyors of quality initiatives.
Thus, their sampleis upwardly biased. There is no reason to believe
that our sample is similarly upward biased; our results may repre-
senta morevariedand realisticsuccess ratein process management
implementation. Also important, our performance matching crite-
rion (3-year median) is stricter than the criteria used by either of
the other two studies.
Six Sigma is a relative newcomer to the ranks of process
improvement programs. It is likely that many Six Sigma adopters
have already put TQM, lean, or other strategies in place prior to
Six Sigma. In these firms, manager may have already targeted
low hanging fruit in previous process improvements, and we
should therefore not be surprised by the relatively small operating
performance improvements associated with Six Sigma reflected in
our overall sample. Indeed, our finding regarding quality maturity
calls into question the argument that Six Sigma is truly distinctive
from other quality oriented management processes. Using the
absorptive capacity perspective, we argued that firms with greater
experience in quality management should benefit more from Six
Sigma adoption, because Six Sigma is at the same time similar to,
yet distinct from, prior quality management programs. Since the
empirical evidence demonstrates less benefit for firms with greater
quality maturity, a more likely conclusion is that the capabilities
stemming fromSix Sigmaadoption addlittle measurable valueover
and above those that emanate from ISO 9000 certification. Thus,
while Six Sigma may entail distinctive organizational structures,
problem solving tools, and metrics, these attributes appear to be
less importantfor already experiencedfirms. The managerial impli-
cations of this finding for firms with and without mature quality
systems could be far-reaching, and thus require further research.
Our findings support the theorythat Six Sigma structure engen-
ders the development of dynamic learning capabilities. However,
one might still question whether the positive returns from these
capability developments justify risks and opportunity costs asso-
ciated with Six Sigma adoption. By using matched, presumed
non-adopting firms as benchmarks in our analysis, we have con-
structed proxies that incorporate such risks and opportunity costs.
However, a given manager considering Six Sigma adoption will
want to consider the specific risks and foregone opportunities that
are relevant to his/her firm. For example, the profits projected by
ourstudymightnot clear thedesired rate of return(hurdle rate) for
a given firm. Moreover, for a given firm a Six Sigma program might
be an inferior investment to a new distribution center, an ERP sys-
tem, a more fuel efficient fleet of trucks, or other such investments
if they have more certain orfaster paybacks.2 Ourresults do suggest
that positive returns from Six Sigma adoption take time. The most
significant driver of ROA in our data, savings in SG&A, first materi-
alizes significantly only in years +2 through +4 (see Table 6 Panel
C). Similarly, with one exception, significant improvements in sales
growthemergeonlyinyears+3and+4(see Table7). Thus,it appears
that dynamic capabilities emerge gradually, or at least take time tobe manifested in operating performance improvements. It is also
possible that Six Sigma is rolled out sequentially across divisions,
extendingthe time forimpactsto manifest in overall corporateper-
formance. Though these findings should be regarded as tentative,
they do suggest that managers shouldbe willing to wait at least 23
yearsbefore netimpacts of Six Sigmaadoption become significantly
positive.
Other insights into thenature of SixSigmas operational impacts
provided by our study are also quite interesting, and somewhat
surprising.Six Sigmais widely recognizedas a methodology for cut-
ting costs and eliminating defects (Byrne and Norris, 2003; Pande
et al., 2000). Six Sigma focuses organizational efforts on process
improvement, especially through reducing variance for outputs of
product (or service) features that are deemed to critically influ-ence customers perceptions of quality. At its core, the DMAIC
method aims to measure and analyze the deviation of a given pro-
cess from its critical-to-quality goals so that workers can install
preventive measures that eliminate the root cause of defects. Such
preventive measures involve the implementation of training, pro-
cedures, monitoring and control systems, tools, technologies, and
product redesign. One would expect that this focus on structural
control would yield improvements in process efficiencies. Reduc-
tions in variation and associated defects are known to create cost
savings in areas of internal product rework, inventories, capacity
2
We thank oneof thereviewers foroffering this observation.
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M. Swink, B.W. Jacobs/ Journal of OperationsManagement 30 (2012) 437453 451
buffers, warranties, and repair work. These also include material
costs savings from reduced scrap and labor savings from reduced
appraisal, material handling, and supervision. We expected that
such efficiency improvements would be reflected in overall low-
ered product or service costs and associated higher margins.
Surprisingly, our results clearly show that efficiencies gained
from Six Sigma adoption are reflected more strongly in indirect
cost savings (SG&A), as opposed to savings in direct operating costs
(COGS). We note that COGS includes direct purchase, labor, and
operating expenses, while SG&A captures indirect expenses asso-
ciated with governance, logistics, advertising, overhead, and other
indirect activities. It is important to note that most SG&A processes
in manufacturing firms are in fact, repeatable service processes
(e.g., customer service, billing, transportation, etc.). Nakhai and
Neves(2009) identify a number of non-manufacturing applications
of Six Sigma inside manufacturing firms. Such processes tend to be
labor-intensive and repetitive. A related finding in our data is that
Six Sigma benefits are strongly correlated with labor-intensity in
manufacturing firms, yet this same correlation is not significant
in service firms. Taken together, both findings suggest that labor-
intensive, repeatable processes offer the greatest opportunities for
applications of Six Sigma methods.
We offer several explanations for this finding, which at first
glance may seem somewhat counter-intuitive. First, Hendricks and
Singhal (2001b) argue that labor-intense firms provide more fer-
tile grounds for quality process improvements because they have
more process options and depend more on training and skills. As
we explained in our motivation for hypothesis H4, processes that
are more automated and less labor-intensive tend to be inherently
less variable.As a result, these processesprovide less overall oppor-
tunityfor improvement from Six Sigma structured methods, which
are mostly aimed at variance reduction.
Second, back-office (SG&A) operationstend to be less influenced
by specific customer requirements and idiosyncrasies, i.e., they are
more repeatable. Consequently, they may present more attractive
targets for Six Sigma projects. Indeed, many of the service firm
examples of Six Sigma applications described in the literature are
actually in back-office or business-to-business contexts (Nakhaiand Neves, 2009; Antony et al., 2007). Given the supposed chal-
lenges of implementing Six Sigma in highly personalized services,
future studies that directly compare Six Sigma implementations
in back-office versus more direct personal service contexts could
reveal important differences in how Six Sigma concepts are opera-
tionalized.
Finally, there is logic suggesting that process variance reduc-
tions will reduce indirect costs, perhaps even more strongly than
direct costs. Schmenner (1988, 1991) notes that overhead costs
often exceed direct costs. Further, he argues that slow mov-
ing and highly variable process flows are the primary drivers
of indirect overhead costs. For example, variable process flows
create requirements for many transactions (purchasing, inven-
tory control, production control, quality control) as well as otheradded overheads (inventory, space, material handling, manage-
ment attention). These arguments are echoed in swift-even
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