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Does Six SigmaImprove Performance?

S. THOMAS FOSTER JR. BRIGHAM YOUNG UNIVERSITY

© 2007, ASQ

This article presents the long-term financial and oper-ational results coming from the implementation of SixSigma. The results from Six Sigma programs weresomewhat mixed. The author found a significant effecton free cash flow (FCF), earnings before interest,taxes, depreciation, and amortization (EBITDA), andasset turnover. Six Sigma did not seem to affect salesreturn on assets, return on investment, or firmgrowth. As a result, if firms want to improve cash,earnings, or productivity in using assets, Six Sigmamight be of use. In 1998, companies with high cashflows and no quality management program (QMP)had lower FCF in 2002 than companies that hadadopted Six Sigma. Companies with low cash flow andno QMP did better than companies using Six Sigma,suggesting that for cash-poor firms, Six Sigma may bea drain on resources. Also, these companies may nothave the cash necessary to sustain effective Six Sigmaresults over four years. Among companies with lowand medium asset turnover, Six Sigma led to higherasset turnover. It could be that companies with lowasset turnover could benefi t more from processimprovement implicit in Six Sigma than firms withhigh asset turnover.

Key words: financial and operational outcomes, qualitymanagement, Six Sigma

INTRODUCTIONIn recent years, many firms have adopted Six Sigma inan effort to improve quality and reduce costs (Pyzdek2003). Six Sigma has been attractive to business exec-utives, as it is thought to overcome some of the pitfallsof historical quality management implementations(Linderman et al. 2003). Quality management pro-grams have been criticized for relying on improvementwithout mechanisms for ensuring that positive resultswill ensue (Howard, Foster, and Shannon 2005). Thatis, if employees are trained and empowered in qualityimprovement approaches, quality improvements andbenefits will “percolate to the top,” thereby improvingcompany performance.

On the other hand, the Six Sigma approachrequires more direction and leadership from topmanagement than traditional quality management.This is termed “leadership for Six Sigma” (Foster2007; Treichler et al. 2002). Along with this higherdegree of leadership is a more structured process forimproving performance. One approach to Six Sigmais the five-phase define, measure, analyze, improve,and control (DMAIC) process. Included with DMAICis a method for leaders to priorit ize potentialimprovement projects based on the probability thatsuch projects will result in financial benefit to theorganization.

Similarly, another defining aspect of Six Sigma isits greater emphasis on cost reduction through qualityimprovement. This aspect includes a target of at least$200,000 in cost reduction for each Six Sigma projecttaken on with two to three projects per Black Belt overa one- to two-year period (Bisgaard and DeMast2006). Such returns are the results expected from thehigh cost of investing in Black Belt employees.

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The convergence of these dimensions of leadership,structured process for improvement, and focus onfinancial results present in Six Sigma was designed toaddress the perceived weaknesses of traditional qualitymanagement. It is expected that this increased focuson financial outcomes should result in improvementsin financial and operational results. The purpose ofthis research is to determine if this is indeed the case.

LITERATURE REVIEW ANDHYPOTHESIS DEVELOPMENTSeveral studies have examined the impact of qualitypractices on financial and operational results(Kaynak 2003). Most of these studies compare theresults of firms that adopt quality practices versuscontrol groups to determine if quality practices signif-icantly influence financial and operational results.The purpose of this literature review is threefold. Theauthor defines Six Sigma. Then he identifies similarstudies that have examined the financial and opera-tional impacts of different quality improvementapproaches such as total quality management (TQM).Finally, he uses this review to provide a basis forselecting variables to be studied in this research.

Six Sigma The author’s literature review revealed only one articlerelating to Six Sigma in an A-level journal (Lindermanet al. 2003). Linderman et al. studied Six Sigma froma goal-theoretic perspective. They examined the rela-tionship between goals and Six Sigma success anddeveloped a series of propositions suggesting that high,yet attainable, goals were important to the success ofSix Sigma programs. They also examined the inter-vening effects of effort, persistence, and directionresulting from explicit Six Sigma goals. Given the wideadoption of Six Sigma in many organizations aroundthe world, there is a need for more research elucidat-ing the benefits and costs of Six Sigma implementa-tion. Currently, no studies investigate the relationshipbetween Six Sigma implementation and financial andoperational results.

The most common process for Six Sigma is DMAIC.This is similar to the plan, do, check, act (PDCA) cycleproposed by Walter Shewhart and W. Edwards Deming(Deming 1986). Since results from Six Sigma areclosely related to the DMAIC process, the author dis-cusses it in detail. It should be noted that as recentlyas the 1990s, many companies used DMAIC to guideSix Sigma projects.

In the define phase, projects are identified andselected. Project selection is performed under thedirection and with the participation of a Six Sigmachampion. Also involved are Master Black Belts andBlack Belts or Green Belts. Project selection is per-formed in four steps: 1) developing the business case;2) project evaluation; 3) Pareto analysis; and 4) projectdefinition. Business case development involves identify-ing a group of possible projects, writing the businesscase, and stratifying the business case into problem andobjective statements. Project evaluation often involvesrisk and return assessment. The key individuals in per-forming this analysis are project champions. Projectchampions are usually top management executiveswho have legitimate and financial authority to supportSix Sigma projects (Treichler et al. 2002). The processfor defining Six Sigma projects helps in prioritizingwhich projects will provide the greatest financial andoperational returns.

The measure phase involves two major steps: 1) selecting process outcomes; and 2) verifying meas-urements. To select process outcomes, process mappingis used to help understand and define the processitself. A process map is a flowchart showing responsi-bilities (Gourishankar 2003). The goal of a processmap is to identify nonvalue-added activities. Twoimportant measures that are monitored are defects perunit (DPU) and defects per million opportunities(DPMO). Measurement systems analysis (MSA) isused to determine if measurements are consistent(Conklin 2006).

The analyze phase involves gathering and ana-lyzing data relative to a particular Black Belt project(Pyzdek 2003). The analyze-phase steps are as follows:1) define the performance objectives; 2) identify inde-pendent variables (Xs); and 3) analyze sources ofvariability. Defining objectives involves determining

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what characteristics of the process need to be changed toachieve improvement. Next, independent variables areidentified for gathering data. These are variables thatsignificantly contribute to process or product variation.The goal of analyzing sources of variability is to usevisual and statistical tools to better understand the rela-tionships between dependent (X) and independent (Y)variables for use in future experimentation. A varietyof tools are used in this analysis, including his-tograms, box plots, scatter plots, regression analysis,and hypothesis tests.

The improve phase of the DMAIC process involvesoff-line experimentation (Antony and Esamilla 2003;Montgomery 2004). Off-line experimentation involvesstudying the identified variables and using design ofexperiments (DOE) to determine whether the inde-pendent variables significantly affect variation independent variables.

The control phase involves putting into placeprocess checks to ensure that improvements are longlasting. The DMAIC process provides a process forimproving operational results that is cost-reductionoriented. This results in the removal of waste fromprocesses. These improvements are expected to result inoperational and financial improvements. In thisresearch, the author examines whether this is the case.

Financial ResultsPrevious studies on the financial and operationalimpacts of quality efforts have provided mixed results.The Jacobsen and Aaker (1987) study using the ProfitImpact of Market Strategy (PIMS) database examinedthe relationship between quality and market share,return on investment (ROI), relative price, relativecost, and relative quality. Jacobsen and Aaker (1987)found the impacts of quality improvement practices tobe positive and significant. Depending on how busi-nesses were grouped, however, they found variance inresults that might be reflective of business strategies.Phillips, Chang, and Buzzell (1983) found similarrelationships and added that product quality appearedto be negatively related to cost.

Several researchers have investigated the impactof quality practices on stock prices. Easton and Jarrell

(1998) reviewed the financial results of 108 firms thatstarted TQM programs between 1981 and 1991. Theyfound that these firms’ stocks outperformed a controlgroup by finding excess cumulative returns. Hendricksand Singhal (1996; 1997; 2001a; 2001b) found similarresults by analyzing the market returns of qualityaward winners. Hendricks and Singhal used winners ofvarious awards to determine whether implementingeffective TQM resulted in improved operating perform-ance in firms. They used the winning of a qualityaward as a proxy for effective implementation of TQM.In their research, they found strong evidence thatwinners of quality awards outperformed control firmson operating-income-based measures. Subsequent totheir studies, other researchers have continued tostudy receipt of awards such as the Malcolm BaldrigeNational Quality Award as indicative of mature qualityimplementation (Rajan and Tamimi 1999; Wilson andCollier 2000; Dean and Tomovic 2004). The literature,however, is not unanimous. York and Miree (2004)studied the links between TQM and financial perform-ance. Conducting a comparison of the financial per-formance of quality award winners against theircontrol firms by SIC groups, they studied performanceboth before and after the winning of the BaldrigeAward. York and Miree found that TQM firms had betterfinancial performance before and after they won theawards—some for even 20 years prior to winning theawards. The author suggested that winning the awardwas a covariate for financial success.

Adams, McQueen, and Seawright (1999) studiedthe stock performance of Baldrige Award winnersfrom the day their award was announced. They foundonly limited evidence to support the hypothesis thatstockholders are rewarded with abnormal returns onthe day of the quality award announcement. Theysuggested that stock analysts may have been fore-warned that the company was winning or that theywere at least aware of prior TQM efforts. They alsosuggested that there may be little impact on stockprice, as stock impact is not the purpose of theBaldrige Award. They do propose that stock impactsrelating to announcements are not as important asimprovements relating to quality improvementefforts. While many studies have focused on the

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financial and operational results of TQM and theBaldrige Award (Pannirselvam, Siferd, and Ruch1998), the author’s literature review revealed no suchstudies relative to Six Sigma.

It is expected that Six Sigma should result inimproved financial performance. Since Six Sigmaemphasizes reductions in cost and increases in meas-ures such as rolled-throughput, one should find thatcash flows, earnings, and other financial measures willshow improvement. General Electric (GE) has reportedmore than $12 billion in savings due to Six Sigma.Similarly, Motorola reported $15 billion in savings over11 years from Six Sigma implementation. As processesand products are improved, it is also expected that saleswill improve. This research was performed to see if theexpected financial results occurred for companies thatimplemented Six Sigma overall.

As the author has discussed, when compared withtraditional quality management, Six Sigma is muchmore cost-reduction and financial-results oriented. Acomprehensive review of the quality management lit-erature reveals that financial and operational perform-ance could generally be categorized as measuringprofitability, cost, efficiency, and growth (Phillips,Chang, and Buzzell 1983; Jacobsen and Aaker 1987;Ward, Leong, and Boyer 1994; Flynn, Schroeder, andSakakibara 1995; Mohrman et al. 1995; Hendricks andSinghal 1996; 1997; Easton and Jarrell 1998; Samsonand Terziovski 1999; Curkovic, Vickery, and Droge2000; Lapre, Mukherjee, and VanWassenhove 2000;Wilson and Collier 2000; Cua, McKone, and Schroeder2001; Devaraj, Matta, and Conlon 2001; Douglas andJudge 2001; Fynes and Voss 2001; Hendricks andSinghal 2001a; 2001b; Park, Hartley, and Wilson 2001;Sousa and Voss 2001; Eriksson and Hansson 2003;Fullerton, McWatters, and Fawson 2003; Kaynak2003). For this study, the author used a modified ver-sion of Hendricks and Singhal’s (1997) performancemeasures. Financial measures used in this studyinclude free cash flow per share; cost per dollar share;earnings before interest, taxes, depreciation, andamortization (EBITDA); sales; and sales per employee.The author starts with the following proposition:

Proposition 1: Six Sigma adoption will positivelyaffect financial results.

Next, this proposition is translated into five hypothe-ses for this study. The author posits that investment inSix Sigma training and implementation signals afocus on aggressive cost reduction coupled withprocess and organizational improvement. Given thatcosts are reduced, cash is freed up for other uses. Thisresults in hypotheses H1a and H1b:

• H1a: Six Sigma adoption is positively associated withfree cash flow per share.

• H1b: Six Sigma adoption is positively associated withcost per dollar sales.

As a result of this signaling of a focus on processand organizational improvement, operating marginsshould improve. This argument is similar to Deming’svalue chain—that focusing on quality will result inlower costs and improved performance. Since operatingmargin can be expressed as EBITDA/sales, the authorproposes the following three hypotheses:

• H1c: Six Sigma adoption is positively associatedwith EBITDA.

• H1d: Six Sigma adoption is positively associatedwith sales.

• H1e: Six Sigma adoption is positively associatedwith sales per employee.

Operational ResultsIn addition to looking at financial results, many stud-ies have examined the relationship between qualityimplementation and operational results. Again, theauthor reviews these studies to understand impactsfound by traditional quality improvement approaches.Dow, Samson, and Ford (1999) studied the effect ofquality practices on quality outcomes. They categorizedquality practices into nine dimensions. Not all of theirdimensions, however, contributed to superior qualityperformance. Employee commitment, shared vision,and customer focus yielded positive correlations withquality outcomes. Conversely, other hard qualitypractices, such as benchmarking, work teams,advanced manufacturing technologies, and closesupplier relations, were not related to superior qualityoperational results.

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Focusing on the competitive aspects of quality per-formance, Douglas and Judge (2001) found strongempirical support for a positive relationship betweenthe degree of TQM implementation and organizationalperformance. They also found some empirical evidencethat this relationship between TQM implementationand organizational performance was moderated byorganizational structure.

Das et al. (2000) used structural equation modelingto study the impacts of international competition onquality. They found that competitive intensity providedan explanation for the absence of returns from invest-ments in quality capital. Obtaining customer satisfactionperformance from quality practices was shown to becontingent on the degree of international competitionpresent in the business environment.

Curkovic, Vickery, and Droge (2000) studied thedirect effects of 10 quality action programs on six firmperformance outcomes as well as their indirect effectsthrough eight quality performance dimensions. Twopaths from action programs through quality perform-ance to firm performance in the automotive supplyindustry were identified. The first path was productquality, whose hallmarks were superior performance onconformance and design quality. The second pathinvolved relationship quality. This path included supe-rior customer responsiveness and service. Both theproduct-quality and the relationship-quality paths ledto superior operational ROI. Product quality led toenhanced return on assets (ROA), and relationshipquality led to enhanced market share performance.

Flynn, Schroeder, and Sakakibara (1995) con-structed a framework that focuses on both core qualitymanagement practices and the infrastructure used toengender an environment supportive of their use. Theyincorporated two measures of quality performance andtheir roles in establishing and sustaining competitiveadvantage. The author used path analysis to test a pro-posed model that explained that perceived qualitymarket outcomes were primarily related to statisticalcontrol/feedback and the product design process, whilethe internal measure of percent that passed finalinspection without requiring rework was stronglyrelated to process flow management and statisticalcontrol/feedback. The focus of most Six Sigma efforts

is on operational improvements. By focusing on theprocesses and variables associated with operationaloutcomes (for example, y=f(x)), it is expected thatSix Sigma programs will result in more efficient usesof assets and higher ROI. The general proposition isstated as:

Proposition 2: Six Sigma adoption results in betteroperational results.

Using COMPUSTAT, the author computed severaloperational measures using productivity ratios. Part ofthe process of Six Sigma is to improve the use of assets.During the improve phase of the DMAIC cycle, DOEinvolves tolerance design, systems design, and parame-ter design (Foster 2006). Systems design involveschoices, tradeoffs, and improvements to existing assets.These improvements may include the optimization ofplant, equipment, or technology. Implicit in thisimproved usage is a more productive usage of assets.This results in hypotheses H2a through H2d.

• H2a: Six Sigma adoption is positively associated withasset turnover.

• H2b: Six Sigma adoption is positively associated withreturn on assets.

• H2c: Six Sigma adoption is positively associated withreturn on investment.

• H2d: Six Sigma adoption is positively associated withtotal assets.

Improvement efforts such as Six Sigma could help acompany to grow as profitability improves. Often theseefforts result in downsizing and the more productive useof employees. Therefore, Six Sigma could result in eithergrowth in the number of employees or a reduction in thenumber of employees. The literature is not clear on thisissue. Therefore, hypothesis H2e is stated in null form.

• H2e: Six Sigma adoption is not related to number ofemployees.

METHODSThis study examines the long-term financial and opera-tional impacts of implementing Six Sigma. In order tocreate a population of Six Sigma-adopting firms, theauthor performed a keyword Lexis/Nexis (LexisONE)

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online search of corporate financial reports for the termsSix Sigma, ISO 9000, TQM, and Baldrige through theLexisONE subscription database. Since annual reportsare used to transmit information to shareholders, themention of a quality management initiative such as SixSigma is interpreted as signaling an organizationalcommitment to that effort. Since annual reports areused to elucidate strategic emphases, it can be said thatthese quality improvement efforts are strategic in nature.Using this rationale, the author performed a contentanalysis to determine which firms had announced theimplementation of Six Sigma. Initially, the year 1998was searched. Since only 12 Six Sigma firms werereturned, the author also searched 1996 and 1997. Thiscontent analysis of annual reports resulted in a list of 30companies that had adopted Six Sigma as a means ofimproving performance. In a further step, they assem-bled a panel of six judges, including academics andquality professionals, to perform a content analysis ofthe annual reports found in LexisONE to determinewhich firms showed strategic commitment to Six Sigma.Based on this content analysis, six firms were removedfrom the analysis, resulting in a final set of 24 Six Sigmafirms. They also found 24 Baldrige firms, 26 TQM firms,and 23 ISO 9000 firms. In addition, a randomly selectedlist of 50 companies from the 1998 Fortune 500 list wasused as a control group.

Company ticker symbols were obtained usingHoover’s Online, and the database of firms the authorcreated was searched for duplicates and firms that werereturned in more than one search. If a firm in the controlgroup was also returned in the list of Six Sigma compa-nies, it was deleted from the control group and anotherfirm in the Fortune 500 was randomly selected. In addi-tion, firms no longer in business or that had beenacquired since 1998 were also stricken. In the end, thedatabase of control firms was reduced to 41 companies.

As stated, a modified version of Hendricks andSinghal’s (1997) performance measures was used.Profitability measures used were earnings (EBITDA),ROA, operating margin (EBITDA/sales), and net sales.Cost structure was analyzed by calculating cost perdollar of sales and FCF per share. Relative firm growthmeasures were obtained using total assets and numberof employees.

Firm data were collected using Standard and Poor’sCOMPUSTAT database. Data were collected for the firmsdrawn from the Lexis/Nexis database for fiscal years1998 through 2004. This provided data for the author’slongitudinal analysis.

In this article, the author compares results from thetime Six Sigma was mentioned in the annual report tofour years later. This four-year interval is needed, as ittakes time for quality improvement programs to yieldsignificant results. In a study of best quality practices,Ozan (1992) found that quality improvement pro-grams should be implemented gradually. A study by theUnited States General Accounting Office (1991) statedthat, on average, 3.5 years were required to see signifi-cant results for TQM programs. In a study of the U.S.auto industry, Narasimhan, Ghosh, and Mendez (1993)found a 2.26-year lag between quality improvementefforts and sales improvements. Foster (1996) studiedthe speed of quality improvement in five different pro-duction facilities and found that plants that improvedmore slowly actually had better financial results thanplants that attempted to improve rapidly. Given theseprior research results, it was thought that a four-yearinterval would provide necessary time to see significantimprovement in results if it was present.

RESULTSThe results are organized according to the outcomevariables studied. For each dependent variable theauthor discusses data transformation (when needed).Next, analysis of covariance (ANCOVA) results for maineffects are presented. ANCOVA was used to test where sig-nificant differences exist between outcomes for each ofthe treatments. ANCOVA is a general linear model withone explanatory variable and multiple factors. It wasapplied where a potentially strong correlation existsbetween independent and dependent variables. Then,the author explains interactions between various qualityprograms (Six Sigma, Baldrige, TQM, and ISO 9000)and no quality program relative to each financial orperformance outcome. The interaction tests usedANCOVA to determine if there are significant interactioneffects between quality management programs (QMP)and outcomes. Model-based estimates are presented for

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companies in the 10th percentile, median, and 90thpercentile. The percentiles were determined according tofirm size, as prior research has suggested that firm sizehas a moderating effect on QMP effects (Hendricks andSinghal 2001a; York and Miree 2004). Finally, hypothe-sis tests were performed to study the author’s researchpropositions. The results are presented according tofinancial or performance outcome in order to enhanceclarity of presentation.

Free Cash Flow Per Share Summary statistics for free cash flow per share (FCF) in1998 and 2002 are presented in Table 1. This summaryshows that 138 companies were evaluated for thisresearch. These companies are rank ordered, with the34th company in the 25th percentile, company 69 at themedian, and the 104th company at the 75th percentile.These statistical tests were performed using SAS.

The model for FCF in 2002 was fit with all datapresent and no transformations. Table 2 shows theANCOVA results for FCF (as well as the other measures).The author has combined tables here for efficient pres-entation. Table 2 shows that the main effect for FCFwas not significant. However, as is shown in Table 3,the interaction term for FCF by QMP was significant (p < 0.0002). With a low R2 of 0.22, this suggests thatother variables are having an effect on an otherwisenull model. The first five rows in Table 4 present

model-based estimates of FCF in 2002 at key levels ofFCF in 1998. For reference, Table 5 shows the countsof companies in various percentiles of FCF in 1998 toprovide an indication of the range of data behind theestimates shown in previous tables and figures.

Companies without a QMP appear to have a differ-ent relationship between FCF in 2002 and 1998 thanall other companies. While FCF in 2002 appears to bemore or less positively related to FCF in 1998, these

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Table 2 ANCOVA results main effects.

Term F value Pr>F

Free cash flow .94 0.3300

Log cost per dollar sales 36.07 <.0001

EBITDA 9.57 0.0027

Log of sales 579.24 <.0001

Sales per employee 306.97 <.0001

Asset turnover 3.01 0.0800

ROA 1.12 0.2910

ROI 1.81 0.1806

Total assets 1.66 0.2006

Log number of employees 860.94 <.0001 ©20

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Table 1 Summary statistics for FCF in 1998and 2002.

1998 2002

Number of companies 138 138

Minimum -14.05 -22.18

25th percentile -0.54 0.33

Median 0.25 1.05

75th percentile 0.85 2.38

Maximum 17.58 12.40

Mean 0.19 1.19

Standard deviation 2.82 3.63 ©20

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Table 3 ANCOVA results interaction terms.

Term F Value Pr>f

Free cash flow by QMP 6.08 0.0002

Log cost per dollar sales by QMP 16.99 <.0001

EBITDA by QMP 8.56 <.0001

Log sales by QMP .83 0.5101

Sales per employee by QMP 0.46 0.7600

Asset turnover by QMP 14.94 <.0001

ROA by QMP 2.45 0.0490

ROI by QMP 2.42 0.0517

Total assets by QMP 17.4 <.0001

Log employees by QMP 1.14 0.3400 ©20

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companies have a negative relation-ship between FCF in 2002 and 1998.Figure 1 plots this interaction.

The author evaluates H1a in Table 6.Among companies with high FCF in1998, those with no QMP have an esti-mated FCF for 2002 that is substantiallylower than all other companies, whereascompanies using Six Sigma have an esti-mated 2002 FCF that is substantiallyhigher than all other companies. Thus,they see Six Sigma companies and theaverage of all companies with a QMP ashaving significantly higher FCF in 2002than those companies without a QMP.At low FCF in 1998, however, thosecompanies without a QMP had signifi-cantly higher FCF in 2002, leading tothe patterns of significance displayedin Table 6.

Cost Per Dollar SalesIt was evident from the review of the1998 data that cost per dollar sales valuewas extremely skewed, and to betteraccommodate model fitting, the authorfitted the model to the natural log ofcost per dollar sales. The 2002 values,however, were evenly distributed.

Table 2 shows the ANCOVA resultsfor the model fit to logged cost per dol-lar sales. As shown in Table 3, becauseof the significant interaction between1998 cost per dollar sales and QMP, theeffect of QMP is different depending onthe level of cost per dollar sales in1998. Table 4 gives the model-basedestimates for mean (logged) cost perdollar sales in 2002 by QMP and levelof 1998 logged cost per dollar sales.

As can be seen in the plots ofmeans in Figure 1, the interactionappears to result from different char-acteristics with respect to cost per

Table 4 Model-based mean estimates (std. error of estimates)of effects in year 5, based on levels of year 1.

Free Cash Flow 10th Median 90th

Six Sigma 1.59 (0.75) 1.64 (0.68) 1.7 (0.83)Baldrige 1.27 (0.76) 1.94 (0.73) 2.65 (1.04)TQM -0.29 (1.35) 1.61 (0.69) 3.61 (1.38)ISO 0.79 (1.03) 1.21 (0.77) 1.65 (1.43)No QMP 2.59 (0.67) 1.13 (0.55) -0.4 (0.56)

Cost Per Dollar Sales

Six Sigma -0.18 (0.06) -0.13 (0.02) -0.11 (0.04)Baldrige -0.34 (0.04) -0.13 (0.02) -0.02 (0.03)TQM -0.1 (0.02) -0.1 (0.02) -0.098 (0.02)ISO -0.16 (0.03) -0.07 (0.02) -0.02 (0.03)No QMP -0.35 (0.03) -0.16 (0.02) -0.07 (0.02)

EBITDA

Six Sigma 2.76 (3.56) 11.88 (2.97) 23.99 (3.14)Baldrige 15.11 (4.96) 8.73 (3.49) 0.263 (3.79)TQM 2.63 (4.8) 7.58 (3.07) 14.15 (7.04)ISO 10.98 (4.27) 13.68 (3.29) 17.26 (3.79)No QMP 5.69 (3.17) 13.8 (2.19) 24.57 (3.22)

Asset Turnover

Six Sigma 15,272 (2,550) 10,116 (2,083) 812 (2,725)Baldrige -2,352 (3,392) -627 (2,685) 2,487 (2,826)TQM 11,426 (5,604) 4,765 (2,519) -7,255 (6,004)ISO 9,435 (8,878) 4,937 (2,614) -3,182 (12,084)No QMP 6,266 (1,957) 6,011 (1,579) 5,551 (2,252)

ROA

Six Sigma 2.18 (2.25) 2.28 (1.75) 2.37 (2.34)Baldrige -1.17 (2.77) -1.12 (1.77) -1.13 (2.42)TQM 3.74 (3.76) 3.08 (1.84) 2.55 (2.54)ISO -1.52 (1.96) 2.65 (1.88) 5.96 (2.08)No QMP 1.98 (1.71) 2.87 (1.29) 3.58 (1.8)

ROI

Six Sigma 3.91 (3.8) 4.25 (2.98) 4.47 (3.67)Baldrige -6.05 (4.76) -0.82 (3.02) 2.63 (3.68)TQM 7.26 (7.96) 6.88 (3.06) 6.63 (5.32)ISO -2.08 (3.77) 6.81 (3.45) 12.67 (4.26)None 8.2 (3.3) 6.46 (2.19) 5.3 (2.93)

Total Assets

Six Sigma -705.3 (1659.4) 1894 (1 j610.9) 6140.6 (1562.7)Baldrige 7682.9 (1937.1) 7523.7 (1804.5) 7263.5 (1741.2)TQM -28.3 (3916.3) 1784.6 (1638.3) 4746.2 (6746.5)ISO 1502.8 (1846.6) 1950.3 (1718.7) 2681.4 (2220.2)None 4135.2 (1193.8) 4545.1 (1167.6) 5214.9 (1396.8) ©

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dollar sales among compa-nies using TQM and thoseusing either no QMP or oneof the other three understudy. All companies otherthan those with TQM appearto have a direct relationshipbetween cost per dollar salesin 2002 and 1998, whileTQM companies appear tohave no such relationship.

Evaluation of the hypotheses of interest is given inTable 6. Null hypotheses regarding Six Sigma compa-nies cannot be rejected. Among companies with low costper dollar sales in 1998, companies using the BaldrigeAward criteria have a significantly lower predicted costper dollar sales than companies using any of the otherthree QMPs. Therefore, H1b is not supported.

EBITDAA review of the data showed that the distribution ofEBITDA in 1998 was skewed because of the presence ofoutlying companies. In 2002 the distribution of EBITDA

was fairly symmetric, but it was peaked. Therefore, themodel for EBITDA in 2002 was fit with all data presentand no transformations.

Table 2 shows significant ANCOVA results (p < .0027).Table 3 shows the model-based estimates for EBITDA in2002. Because of the significant interaction betweenEBITDA 1998 and QMP, the effect of QMP is differentdepending on the level of EBITDA in 1998. Table 4shows model-based estimates of average EBITDA in2002 at levels of 1998 EBITDA.

Inspection of Figure 1 suggests the following as aninterpretation of the interaction: Among companies withthe highest EBITDA in 1998, those using the Baldrige

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Table 5 Count of companies in each percentile category and QMP.

Six Sigma Baldrige TQM ISO None Total

0–10th percentile 3 6 1 4 14

10th–50th percentile 7 10 14 16 7 54

50th–90th percentile 12 5 11 6 22 56

90th percentile and higher 2 3 1 8 14

Total 24 24 26 23 41 138 ©20

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FCF 1998 by QMP interaction

FCF

2002

FCF 1998

Six SigmaBaldrigeTQMISONone

Six SigmaBaldrigeTQMISONone

Six SigmaBaldrigeTQMISONone

Six SigmaBaldrigeTQMISONone

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Award criteria had the low-est EBITDA in 2002, andthose using Six Sigma or noQMP had the highest. Thatresult is somewhat reversedamong companies with thelowest EBITDA in 1998.

Evaluation of thehypotheses of interest isgiven in Table 6. TheEBITDA in 2002 is statisti-cally equivalent betweenSix Sigma companies andcompanies with no QMP,regardless of EBITDA in1998. Among companieswith a QMP, Six Sigmacompanies have a higherEBITDA in 2002. However,this only holds amongcompanies with a higherEBITDA in 1998; for allothers, performance ofEBITDA in 2002 is equiva-lent. This provides partialsupport for H1c.

SalesDespite a large range ofsales data, the raw datawere left in their originalscales. The author exam-ined the summary of salesdata and found they wereextremely skewed. To bet-ter accommodate modelfitting, he modeled the natural log of sales.

Table 2 gives the ANCOVA table for the model forlogged sales. While the overall R2 for the model is 0.93,no terms other than sales in 1998 have any explanatorypower for sales in 2002 (see Table 3). For this reason, theauthor concludes that the null hypotheses of interestcannot be rejected and that Six Sigma has no influenceon sales. Therefore, H1d is not supported.

Sales Per EmployeeThe raw data were divided by 1,000 to drop the scale to amore useable level. It was evident from the summarydata that this value was extremely skewed, and to betteraccommodate model fitting, the author used the naturallog of sales per employee (in thousands). Table 2 givesthe ANCOVA table for the model fit. While the overall

Table 6 Hypothesis results.

Free cash flow 10th1 Median2 90th3

Six Sigma vs. No QMP -1 (0.99) 0.5 (0.87) 2.1 (1.01)*

Six Sigma vs. Other QMP 1 (0.99) 0.05 (0.81) -0.94 (1.12)*

Any QMP vs. No QMP 1.75 (0.85)* -0.46 (0.67) -2.8 (0.82)*

Cost per dollar sales

Six Sigma vs. No QMP 0.17 (0.08) 0.03 (0.03) -0.04 (0.05)

Six Sigma vs. Other QMP 0.01 (0.07) -0.03 (0.03) -0.06 (0.05)

Any QMP vs. No QMP -0.16 (0.04)* -0.06 (0.02)* -0.01 (0.03)

EBITDA

Six Sigma vs. No QMP -2.93 (4.71) -1.92 (3.66) -0.57 (4.54)

Six Sigma vs. Other QMP -6.81 (4.44) 1.88 (3.52) 13.43 (4.3)*

Any QMP vs. No QMP -2.17 (3.84) 3.33 (2.76) 10.65 (4.13)*

Asset turnover

Six Sigma vs. No QMP 9005.74 (2813.7)* 4105.52 (2592.55) -4738.12 (2696.38)

Six Sigma vs. Other QMP 9101.92 (4355.24)* 7091.12 (2571.04)* 3462.13 (5065.76)

Any QMP vs. No QMP -2179.08 (3458.55) 1213.03 (2110.05) 7334.95 (3709.69)

ROA

Six Sigma vs. No QMP 0.20 (2.82) -0.58 (2.17) -1.21 (2.97)

Six Sigma vs. Other QMP 1.81 (2.85) 0.76 (2.06) -.09 (2.70)

Any QMP vs. No QMP 1.16 (2.22) 1.15 (1.63) 1.15 (2.21)

Total assets

Six Sigma vs. No QMP -4,840.5 (2,029.3) -2,651.1 (1,975.6) 925.7 (2,087.3)

Six Sigma vs. Other QMP -3,757.9 (2,293.2) -1,858.9 (1,894.2) 1,243.5 (2,895.8)

Any QMP vs. No QMP 2,022.1 (1,754) 1,257 (1,476.2) 7 (2,362.3)

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R2 for the model is a high 0.77, no terms other thansales per employee in 1998 have any explanatory powerfor sales per employee in 2002 (see Table 3). For thisreason, they concluded that the null hypotheses H1ecannot be rejected and that QMP has no influence onsales per employee.

Asset TurnoverOnce again, Table 2 shows the ANCOVA table for themodel fit, and Table 4 shows the model-based esti-mates for asset turnover in 2002. These estimatesindicate that companies with high asset turnover in1998 had a lower 2002 asset turnover than companieswith a low asset turnover in 1998. This is generallytrue for companies using Six Sigma, TQM, and ISO9000. The 2002 asset turnover for Baldrige companiesshows the opposite pattern (companies with high assetturnover in 1998 had higher asset turnover in 2002than those with lower 1998 asset turnover). Thosecompanies with no QMP showed no real difference in2002 asset turnover, regardless of their 1998 assetturnover. Figure 1 plots these estimates from Table 4.

Evaluation of the hypotheses of interest is given inTable 6. Among companies with low and median 1998asset turnover, companies using Six Sigma QMP hadsignificantly higher asset turnover in 2002 than com-panies with no QMP; the asset turnover of these SixSigma companies was also higher than the average ofcompanies with the other three programs. Amongcompanies with high asset turnover in 1998, there wasno significant difference among any of the hypothesesof interest. Therefore, H2a is partially supported.

Return on AssetsTable 2 gives the ANCOVA table for the model fit, andTable 3 shows the model-based estimates for ROA in2002. Table 3 shows a significant interaction betweenROA and sales (p < .05). Table 4 provides the model-based estimates of average 2002 return on investmentsat levels of 1998 ROA.

Evaluations of the hypotheses of interest are givenin Table 6. While the interaction is significant, noneof the hypotheses of interest are. Table 6 provides

means and standard errors of the return on asset dataused in the model by QMP, company size, and year.Therefore, H2b is not supported.

Return on InvestmentROI for either year looks bell-like in distribution exceptfor the presence of very long tails that show outlyingobservations in both the positive and negative direction.Attempts at transformation did little to pull in thesestrong outliers. Thus, the ANCOVA model was fit on theraw data. Table 2 shows the ANCOVA table. As one cansee in Table 3, at p < .05, the interaction of QMP byROI in 1998 is technically not statistically significant.However, it is of borderline significance. Table 5 givesthe model-based estimates for ROI in 2002 by per-centiles of ROI in 1998 and QMP. Figure 1 plots thesemeans for easier interpretation. From Figure 1 itappears that Baldrige and ISO 9000 companies may beresponding differently than the companies with theother three QMPs. This is descriptive only, however,owing to the lack of significance of any of the terms inthe ANCOVA model. Therefore, H2c is not supported.

Total AssetsTo bring the data to a more manageable scale, theoriginal data were divided by 1,000. The model was fitwith all available data for total assets in 2002. Thenonsignificant ANCOVA result is presented in Table 2.As is shown in Table 3, because of the significantinteraction between total assets 1998 and QMP, theeffect of QMP is different, depending on the level oftotal assets in 1998. Table 4 shows the model-basedestimates of the average total assets (in thousands) for2002 for each QMP by levels of total assets in 1998.

Figure 1 shows a plot of the estimates to facilitategeneral interpretation of the interaction. Generally, itappears that the relationship between 1998 total assetsand 2002 total assets does not hold for companiesusing Baldrige, ISO 9000, or no QMP.

Table 6 contains the tests of the hypotheses ofinterest. Among companies at the highest level of totalassets in 1998, there is no difference in 2002 totalassets. For all others, however, those using the

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Baldrige Award criteria have higher total assets in2002 than those using any other QMP. Therefore, H2dis not supported.

Number of EmployeesBecause these data are skewed, the author used thenatural logarithm as the outcome in modeling efforts.Table 2 gives the results of the ANCOVA model. For thismodel, adding the indicator for company size was notappropriate, so it was not included here. Because noterm other than the number of employees in 1998 isstatistically significant, H2e is not supported.

DISCUSSION AND CONCLUSIONSAs has been seen, the results from Six Sigma programsare at best mixed. The author found a significant effecton FCF, EBITDA, and asset turnovers; however, SixSigma did not seem to affect sales, ROA, ROI, or firmgrowth. As a result, if firms want to improve cash,earnings, or productivity in using assets, Six Sigmamight be of use. An intervening variable in this analy-sis was firm size, which confirmed Hendricks andSinghal’s finding on the importance of firm size. Itappears that firm size mitigates the effects of programslike Six Sigma. Certainly, large firms such as GE andMotorola have resources and assets to invest in SixSigma programs. These firms also showed greatcapacity for improvement. Smaller firms may nothave the same resources to apply to such programs;however, smaller firms that invest in efforts such asSix Sigma may be impacted more significantly as atotal proportion of the firm’s business results.

Companies with high cash flows in 1998 and noQMP had lower FCF in 2002 than companies that hadadopted Six Sigma. Companies with low cash flowand no QMP did better than companies using SixSigma, suggesting that for cash-poor firms, Six Sigmamay in fact be a drain on resources. Also, these com-panies may not have the cash necessary to sustaineffective Six Sigma results over four years. Therefore,H1a was supported for firms with high cash flows, butmay have a negative effect for companies with poorcash flows.

Among companies with low and medium assetturnover, Six Sigma led to higher asset turnover.Therefore, H2a was supported for companies with lowand medium asset turnover in 1998. It could be thatthese companies with low asset turnover could benefitmore from process improvement implicit in Six Sigmathan firms with high asset turnover.

The adoption of Six Sigma seemed to not affect ROAand ROI significantly. This may not be surprisingbecause many factors affect ROA and ROI, as evidencedby the DuPont model. Further investigation showed thatlarge firms using Six Sigma performed better. That is,the improvement rates were in the correct direction. Thedifferences, however, were not significant. Reflectingon the original research question regarding financialand operational outcomes, it appears that at a macrolevel, the effects, while in some cases significant, aresomewhat modest. While the author did see benefits inboth financial and operational areas, quality profes-sionals should be careful to not oversell the benefits ofSix Sigma or any other QMP.

That Six Sigma did not affect firm growth was notsurprising. There were many exogenous macroeconomicfactors, including 9/11, that could have influencedgrowth during this period. Powell (1995) and othershave stated that there are many factors that affect firmgrowth other than quality management approaches.Indeed, many quality programs have been associatedwith reductions in number of employees. On the otherhand, that one is able to find any significant results forthese small numbers of firms demonstrates that qualityprograms such as Six Sigma can potentially provideimportant financial and operational returns. Thisresearch is very high level with much room for futurestudy. Future studies should examine specific effects onspecific projects in companies. It is expected that suchstudies could verify and clarify these findings.

LimitationsThere were several limitations to this research. As wasdiscussed previously, financial results during this periodwere influenced by 9/11 and other events. In addition,these firms’ use of Six Sigma or other quality programsin 1998 does not guarantee that they implemented these

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programs effectively or maintained their strategicsupport through 2002 and beyond. Of course, thenormal provisos concerning COMPUSTAT hold, asthese data were firm-level data, and many times SixSigma programs are implemented at the business unitlevel. While the methodology the author used isadapted from prior QMP studies, it is difficult to isolatethe effects of Six Sigma at a macro level. Future stud-ies at a more micro level may help the understandingof these phenomena.

This study included firms from a variety of indus-tries. For future research, the author suggests industry-specific studies of the effects of Six Sigma adoption. Ithas been suggested that Six Sigma is best suited forprocess industries. It is expected that in some ways suchindustries would have been leading-edge adopters.

Implications for ManagersThis research has implications for managers. Theauthor found negative cash flows for small firms, butpositive cash flows for larger firms. It is apparent thatmanagers in smaller firms should carefully considerthe cash flow impacts of quality improvement invest-ments. Managers should also carefully monitor theeffects of Six Sigma efforts and focus on “hard dollar”benefits. If not skillfully implemented, the benefits ofSix Sigma may be marginal. The “fad” element of SixSigma should also be noted. It is more likely thatcompanies that understand the benefits of Six Sigmaare able to effectively manage to achieve those bene-fits. It could be that companies that implement SixSigma to merely parrot industry practice will likelyfind modest competitive benefits from Six Sigma orany other quality improvement effort.

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BIOGRAPHY

S. Thomas Foster is area leader of global supply chain atBrigham Young University. He has published more than 50 quality-re la ted ar t ic les in journals such as Decis ion Sciences,International Journal of Production Research, the Journal ofOperations Management, the International Journal of Qualityand Reliability Management, the Quality Management Journal,and Quality Progress magazine. His book is titled, QualityManagement: Integrating the Supply Chain (Prentice Hall).Foster is founder of http://www.freequality.org, was awardedthe ASBSU 2000 Outstanding Faculty Award, and received theprestigious 2002 Decision Sciences Insti tute InstructionalInnovation Award. He is currently serving as guest editor for aspecial issue on supply chain qual i ty for the Journal ofOperations Management. He served twice as an examiner forthe Malcolm Baldrige National Quality Award and is a memberof ASQ. He can be reached by e-mail at tom_foster@byu.edu.

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