Supply Chain Integration and the SCOR ModelHonggeng Zhou1, W. C. Benton, Jr.2, David A. Schilling2, and Glenn W. Milligan2

1University of New Hampshire2The Ohio State University

The Supply Chain Operations Reference (SCOR) model has been widely adopted in many companies. Anecdotal evidence and tradejournals have reported significant improvements after firms have adopted the SCOR model. Although practitioners have been

enthusiastic about implementing and using the SCOR model in their operations, the SCOR model has not been empirically validated.The purpose of this study is to empirically validate the SCOR model (i.e., test the structure of the SCOR model). Data from 125 NorthAmerican manufacturing firms were collected. The results show that the relationships among the supply chain processes in the SCORmodel are generally supported. The Plan process has significant positive influence on the Source, Make, and Deliver processes. TheSource process has significant positive influence on the Make process and the Make process has significant positive influence on theDeliver process. The Source process mediates the impact of the Plan process on the Make process and the Make process mediates theimpact of the Plan process on the Deliver process. The findings provide managers with empirical evidence that the SCOR model is infact valid.

Keywords: Supply Chain Operations Reference (SCOR) model; supply chain management; business strategy

INTRODUCTION

The Supply Chain Operations Reference (SCOR) model wasdeveloped by the Supply Chain Council in 1996. The SCORmodel focuses on the supply chain management functionfrom an operational process perspective and includes cus-tomer interactions, physical transactions, and market interac-tions. In the past decade, the SCOR model has been widelyadopted by many companies including Intel, General Electric(GE), Airbus, DuPont, and IBM. According to the SupplyChain Council’s (2010) website, ‘‘While remarkably simple, it[the SCOR model] has proven to be a powerful and robusttool set for describing, analyzing, and improving the supplychain.’’ In the literature, several recent studies have reviewedthe SCOR model (Huang et al. 2004, 2005). Many otherstudies (McCormack 1998; Lockamy and McCormack 2004;Supply Chain Council 2010) have attempted to measure theSCOR model’s impact on business performance. Trade jour-nals have also reported the benefits of using SCOR model(Davies 2004; Malin 2006).

To date, the SCOR model has been used by companiesthroughout the world. Intel is one of the first major U.S.corporations to adopt the SCOR model (Supply ChainCouncil 2010). In 1999, Intel started its first SCOR projectfor its Resellers Product Division. Later, they expanded theSCOR model implementation to the Systems ManufacturingDivision. Several other SCOR projects were conductedafterward. The benefits of implementing the SCOR modelincluded faster cycle times, less inventories, improved visibil-ity of the supply chain, and access to important customerinformation in a timely fashion. GE implemented the SCORmodel in its Transportation Systems unit, which reported

sales of $2.6 billion in year 2001. The use of the SCORmodel streamlined the purchasing process with its suppliers,which led to shorter purchasing cycle time and lower cost.Davies (2004) report that since 1999 Philips Lighting hasused the SCOR model in its overall business framework,which directly resulted in improved customer service andreduced inventories. In Europe, Degussa (a German chemicalcompany) used the SCOR model to streamline its newlymerged businesses. Specifically, Degussa set up a team ofcross-functional employees to implement the SCOR project.After a three-week pilot project, the SCOR team foundopportunities in the existing supply chain processes. It wasreported that the SCOR project was expected to save thefirm millions of euros.

The SCOR model is used not only in manufacturing oper-ations, but also in service operations. As Malin (2006)reports, a New York hospital used the SCOR model todefine, measure, and improve supply chains. The first phaseof the project led to 2% reduction in overall drug inventorythe first year. The hospital reported an 8–10% reduction inexcess and obsolete inventory during the next two years.Meanwhile, the improved visibility and planning generated21% capacity increase and an 8% increase in demand. Theprep times for key procedures were reduced by as much as40%, which resulted in reduced labor costs.

Although the SCOR model has been widely practiced bymany companies in different processes of supply chains andanecdotal evidences have shown the value of adopting theSCOR model, no large-scale empirical research has beenconducted to systematically examine the relationships amongthe supply chain processes as suggested by the SCOR model.Thus, the purpose of this study is to empirically validate theSCOR model (i.e., to confirm the structure of the SCORmodel).

The results of this study show that the relationshipsamong the supply chain processes in the SCOR model aregenerally supported. The Plan process has significant positive

Corresponding author:W. C. Benton, Jr., Department of Management Sciences, FisherCollege of Business, The Ohio State University, 2100 Neil Ave-nue, Columbus, OH 43210, USA; E-mail: benton.1@osu.edu

Journal of Business Logistics, 2011, 32(4): 332–344� Council of Supply Chain Management Professionals

influence on the Source, Make, and Deliver processes. TheSource process has significant positive influence on the Makeprocess and the Make process has significant positive influ-ence on the Deliver process. Among the four supply chainprocesses, the Plan process has received the least attentionfrom the implementation firms. The findings from this studyprovide practitioners statistical confidence in the implementa-tion and use of the SCOR model.

In the next section, literature review and research hypothe-ses will be presented. The theoretical underpinnings for theresearch hypotheses are also discussed in the second section.In the third section, the research methodology and measure-ment scale development are presented. In the fourth section,the analysis results are given. The research findings and man-agerial implications are discussed in the fifth section. Finally,concluding comments and future research directions are pre-sented in the concluding section.

LITERATURE REVIEW AND RESEARCH

HYPOTHESES

In this section, we review the literature of the SCOR model.Based on the literature review, the research hypotheses areproposed. The literature review provides the theoretical foun-dation for this research. The theoretical foundation isreflected in the literature taxonomy given in Table 1.

As the SCOR model is the main framework in this study,a brief introduction of the SCOR model is necessary. TheSCOR model diagram is given in Figure 1. Level 1 consistsof five supply chain processes: Plan, Source, Make, Deliver,and Return. As the Return process was not in the first fourversions of the SCOR model and is not as mature as theother four processes, this study focuses on the other fourprocesses (Plan, Source, Make, and Deliver), which havebeen widely adopted by practitioners. Level 2 of the SCORmodel describes core processes. Level 3 of the SCOR modelspecifies the best practices of each process. According to thedefinition in the SCOR model, Plan includes the processesthat balance aggregate demand and supply to develop acourse of action which best meets sourcing, production, anddelivery requirements. Source includes the processes that pro-cure goods and services to meet planned or actual demand.Make is comprised of the processes that transform productto a finished state to meet planned or actual demand. Deliv-ery includes all processes which provide finished goods andservices to meet planned or actual demand (Supply ChainCouncil 2010). The following subsections review the litera-ture of the four processes and develop the research hypothe-ses.

Plan (planning)

Supply chain planning process uses information from exter-nal and internal operations to balance aggregate demandand supply. The SCOR model suggests that the capability torun ‘‘simulated’’ full stream supply ⁄demand balancing for‘‘what–if’’ scenarios is important for supply chain planning.‘‘What–if’’ analysis helps firms to perform sensitivity analysis

for various possible scenarios. Another important ability isto get real-time information and rebalance supply chainsusing updated information. Information sharing in supplychains can lead to improved performance (Fawcett et al.2011). According to Narasimhan and Kim (2001), the use ofinformation systems can improve supply chain integration.From the process perspective, it is important to have a desig-

Table 1: Literature review taxonomy

Authors

Supply chain practice

Plan Source Make Deliver

Ahmad and Schroeder (2001) *Benton and Shin (1998) *Blackburn (1991) *Chen and Paulraj (2004) *Carr and Pearson (1999) *Choi and Hartley (1996) *Cua et al. (2001) *Dong and Xu (2002) * *Ferrari (2001) * *Flynn et al. (1999) *Fullerton and McWatters (2001) *Fullerton et al. (2003) *Garcia et al. (2004) * *Giffi et al. (1990) * *Goldsby and Stank (2000) *Gurin (2000) *Ha et al. (2003) *Hahn et al. (1983) *Hausman et al. (2002) * *Hayes and Wheelwright (1984) * *Henig and Levin (1992) * *Hill (1994) * * *Hines (1996) * *Kaynak and Hartley (2008) *Lee et al. (1997) * *Li et al. (2005) *Lockamy and McCormack (2004) * * *MacDuffie et al. (1996) *Makatsoris and Chang (2004) * *McKone and Schroeder (2001) *Nakajima (1988) *Nair (2006) *Pande et al. (2000) *Powell (1995) *Prahinski and Benton (2004) *Rungtusanatham et al. (1997) *Samson and Terziovski (1999) *Schonberger (1990) *Shah and Ward (2003) *Shah and Ward (2007) *Stalk et al. (1992) * *Supply Chain Council (2010) * * *Wemmerlov and Hyer (1989) *Womack et al. (1990) *

Supply Chain Integration and SCOR Model 333

nated supply chain planning team. Womack et al. (1990) findthat one primary reason that Japanese automobile firms havean advantage over traditional U.S. automobile firms is thatthey used designated planning teams to coordinate differentfunctions. Furthermore, the literature suggests that interfunc-tional coordination within a firm is critical for supply chainplanning because the alignment between the functions is nec-essary to achieve a firm’s strategic goals (Supply ChainCouncil 2010). For example, many studies (Hill 1994; Haus-man et al. 2002) have found the importance of aligning mar-keting and manufacturing operations to improveperformance.

Source (buyer–supplier relationship)

Sourcing practice connects manufacturers with suppliers andis critical for manufacturing firms. The academic literatureand the SCOR model have identified several sourcingpractices as best practices (Carr and Pearson 1999; Chen andPaulraj 2004; Prahinski and Benton 2004; Li et al. 2005;Benton 2010). Establishing long-term supplier–buyer rela-tionship and reducing the supplier base are good sourcingpractices. The role of key suppliers in a supply chain should

be assured through long-term relationship (Treleven 1987;Benton 2010). Hahn et al. (1983) show that companies’ bene-fits gained by giving larger volume of business to fewer sup-pliers using long-term contracts outweigh the costs. Just-in-time (JIT) delivery from suppliers is also considered a goodsourcing practice. The benefits of JIT delivery have beenwidely documented (Benton and Shin 1998; Ahmad and Sch-roeder 2001; Dong et al., 2001). Furthermore, providingfeedback about suppliers’ performance evaluations is a goodsourcing practice. According to Carr and Pearson (1999),supplier evaluation systems have a direct positive impact onbuyer–supplier relationship, and an indirect impact on firmfinancial performance. More recently, Prahinski and Benton(2004) studied the role of communication in supply chainmanagement. They found that executives at buying firmsneed to incorporate indirect influence strategy, formality,and feedback into supplier development programs.

Make (transformation process)

The Make process includes the practices that efficientlytransform raw materials into finished goods to meet supplychain demand in a timely manner. Both academic literature

Return

Level

Descrip on Schematic Comments

Top Level (Process Types)

Level 1 defines the scope and content for the Supply Chain Operations Reference-model. Here basis of competition performance targets are set.

Source MakeDeliver

Plan1

#

ConfigurationLevel (Process

Categories)

A company’s supply chain can be “configured-to-order” at Level 2 from core “process categories.” Companies implement their operations strategy through the configuration they choose for their supply chain.

2

ProcessElement Level (DecomposeProcesses)

Level 3 defines a company’s ability to compete successfully in its chosen markets, and consists of:

• Process element definitions • Process element information

inputs, and outputs • Process performance metrics • Best practices, where

applicable

3

P1.1Identify, Prioritize, and

Aggregate Supply-Chain Requirements

P1.2Identify, Assess, and

Aggregate Supply-Chain Requirements

P1.3Balance Production

Resources with Supply-Chain Requirements

P1.4Establish and Communicate

Supply-Chain Plans

Companies implement specific supply-chain management practices at this level. Level 4 defines practices to achieve competitive advantage and to adapt to changing business conditions.

ImplementationLevel (DecomposeProcess Elements)

4

Not in

Scope

Supp

ly-Cha

inOpe

raon

sRe

ference-mod

el

Return

Figure 1: Supply Chain Operations Reference (SCOR) model.

334 H. Zhou et al.

(Shah and Ward 2007; Benton 2011b) and the SCOR modelinclude four groups of practices for the Make process: JITproduction, total preventive maintenance (TPM), total qual-ity management (TQM), and human resource management(HRM). JIT production includes several practices: pull sys-tem, cellular manufacturing, cycle time reduction, agile man-ufacturing strategy, and bottleneck removal (Wemmerlovand Hyer 1989; Blackburn 1991; Powell 1995; MacDuffieet al. 1996; Benton and Shin 1998; Flynn et al. 1999; Fuller-ton and McWatters 2001; Fullerton et al. 2003; Benton2011a). The review of quality management literature has ledto the identification of good quality management practices:TQM, statistical process control (SPC), continuous improve-ment program, and six-sigma techniques (Benton 1991; Pow-ell 1995; Rungtusanatham et al. 1997; Pande et al. 2000; Cuaet al. 2001; Nair 2006; Kaynak and Hartley 2008). TPM is amanufacturing program that primarily maximizes equipmenteffectiveness throughout its entire life (Nakajima 1988; Cuaet al. 2001). Several studies have explored the good practicesof TPM and their positive relationship with business perfor-mance (Cua et al. 2001). The literature review led to theidentification of the following effective TPM practices: pre-ventive maintenance; safety improvement program; planningand scheduling strategies; and maintenance optimization.The HRM practices emphasize employee team work andworkforce capabilities. Employee team work is important forimproving production, because frontline employees workingas a team can leverage the experience of all employees andgreatly contribute to process and product improvement(Hayes and Wheelwright 1984). Workforce capability isanother important measurement for workforce management(Giffi et al. 1990; Schonberger 1990).

Deliver (outbound logistics)

The extant literature and anecdotal evidence show that deliv-ery has become a critical link in supply chain management(Gurin 2000; Ha et al. 2003). Goldsby and Stank (2000)review the world class logistics competencies and capabilities.One capability is sharing real-time information with supplychain partners, which increases the real-time visibility oforder tracking. Agility is also an important competence ofworld class logistics. Gurin (2000) describes how Ford part-nered with the United Parcel Service to develop and imple-ment an Internet-based delivery process, significantlyimproving Ford’s delivery performance. An Internet-baseddelivery system can significantly enhance the real-time ordertracking capability. Other best delivery practices identifiedby the SCOR model include a single contact point for allorder inquiries, order consolidation, and the use of auto-matic identification. The bar code technology significantlyimproves the relationship between suppliers and buyers andallows some emerging inventory management programs suchas vendor-managed inventory program. Ahmad and Schroe-der (2001) identify several factors that affect delivery perfor-mance. The factors include JIT management, qualitymanagement, production instability, and so on. However,Ahmad and Schroeder (2001) do not use a scale to measurethe good practices in delivery process.

Relationships of the four supply chain processes in the SCOR

model

Both the SCOR model and the literature suggest the relation-ship among the four supply chain processes as illustrated inFigure 2. First, effective supply chain planning practices areexpected to influence the implementation of effective sourc-ing, production, and delivery practices (Lockamy and Mc-Cormack 2004). The planning process is expected to balancethe aggregate supply chain demand and supply. The abilityto balance demand and supply in real time can enhance along-term relationship with suppliers who can better respondto the demand ⁄ supply changes (Ferrari 2001). It also sup-ports the implementation of an effective production system,which includes practices such as JIT, TPM, TQM, andHRM. For example, without a good planning process, a JITproduction would be impossible. The interfunctional coordi-nation such as the alignment between marketing and manu-facturing is important for an effective JIT production.Effective supply chain planning also drives effective deliveryprocess. To respond to customer demand changes quickly,firms need the ability to track the order delivery status in realtime (Makatsoris and Chang 2004). Based on the SCORmodel and the literature, the hypotheses are proposed as fol-lows.

H1: Plan process positively influences Source process.H2: Plan process positively influences Make process.H3: Plan process positively influences Deliver process.

Second, sourcing process positively influences the use ofMake process (St. John and Young 1991; Hines 1996; Ben-ton 2010). A good long-term relationship with suppliers canhelp firms implement JIT production. Without a good JITdelivery from suppliers, a JIT production system would be

Plan

Source

Make

Deliver

H1

H2

H3

H4

H5

Figure 2: Supply Chain Operations Reference (SCOR)model.

Source: Supply Chain Operations Reference Model, SupplyChain Council (2010).

Supply Chain Integration and SCOR Model 335

impossible. A good relationship with suppliers also helpscontrol the quality of the inputs, which helps the use ofTQM program. For example, a major automobile manufac-turer does not examine the quality of some incoming compo-nents, because it has a good relationship with its suppliersand has enough confidence on its supplier’s quality. Finally,a good delivery from suppliers allows manufacturers to sche-dule preventive maintenance in an effective way. Therefore,the following hypothesis is proposed.

H4: Source process positively influences Make process.

Third, the Make process positively influences the deliveryprocess (Henig and Levin 1992; Garcia et al. 2004). A goodJIT production system produces products in a timely manneraccording to customer needs, which is essential to theimplantation of JIT delivery. A good TQM program andknowledgeable employees are also necessary to facilitate theuse of JIT delivery. In addition, an effective production sys-tem can help increase the visibility of order trackingthroughout the whole supply chain system. Therefore, thefollowing hypothesis is proposed.

H5: Make process positively influences Deliver process.

Although H1–H5 are directly from the SCOR model, theempirical validation of the SCOR model contributes to theacademic literature and provides value to the practitioners.Taken together, H1, H2, and H4 suggest that Source processmediates the influence of Plan process on the Make process.The mediation effect suggests that the Plan process drivesbetter Make process at least partially because good supplychain planning practices have positive influence on sourcingpractices. Similarly, H2, H3, and H5 together suggest thatMake process mediates the influence of Plan process on theDeliver process. Thus, this study will use Sobel tests todirectly examine these two mediation effects.

H6: The influence of Plan process on Make process is medi-ated by Source process.

H7: The influence of Plan process on Deliver process ismediated by Make process.

RESEARCH METHOD

Sample

The research objectives were achieved by obtaining responsesfrom manufacturing professionals holding senior-level posi-tions. Contact information for qualified informants was iden-tified with the assistance of the Supply Chain Council (2010).The surveyed firms include Xerox Corp., Dow CorningCorp., Owens Corning, Nachi Robotic Systems, WindsorMold Inc., and Minntech Corporation. The respondentswere senior executives and held titles such as CEO, Presi-dent, Vice President, and Director. The average number ofemployees in the respondents’ firms was about 5,000. Eight

companies had more than 10,000 employees. The medianannual sales value, as reported by the respondents, wasbetween $100 million and $500 million. Five companies hadannual sales of more than $5 billion. Four academic expertsand three industry experts were asked to review the surveyinstrument (questionnaire) to ensure the relevance and clarityof the survey instrument. The industry experts who reviewedthe questionnaire also provided insights as to the type of jobtitles that may reflect probable knowledge of the SCORmodel. Utilizing this guidance, the sample was selected basedupon job titles and job descriptions available. Employing themultiple contact strategy as suggested by Dillman (2007), atotal of 745 manufacturing professionals were invited to par-ticipate in the study.

Four contacts were made with the selected informants.The purpose of the initial postcard contact was to verify theaccuracy of the mailing address and make the selectedrespondents aware of the forthcoming questionnaire. Twoweeks after the initial postcard was mailed, the first roundsurvey packages were mailed. According to Dillman (2007),at least two weeks are needed between contacts to allowenough time for the postcards with wrong addresses to bereturned to us. The survey packages contained three items:the personalized letter of introduction about the importanceof the study, an eight-page booklet of the survey question-naire, and a prepaid business reply envelope. The third con-tact, mailed one week after the first round survey packages,were reminder postcards. The postcards were used to thankthose who had returned the questionnaire and remind thosewho had not returned the questionnaire. Two weeks aftersending the reminder postcards, the second round question-naires were mailed to the informants who had not replied.As before, the survey package included: a personalized letter,the questionnaire, and the prepaid business reply envelope.Two weeks after the second round questionnaires weremailed, those companies who had not replied were contactedby telephone. Several insights were gained from the success-ful telephone conversation. First, respondents in many of thecompanies, the informant forwarded the questionnaires toothers within the company to complete. However, if therespondent who received the questionnaire could not respondto certain questions, the respondent would most likely for-ward the questionnaire to another person who can answerthe questionnaire. It is expected that if the questionnaire wasforwarded, the return rate is greatly reduced. This processalso resulted in significantly longer cycle times (Dillman2007). Second, many respondents who were interested in thestudy could not locate the questionnaire that was sent tothem. Thus, a replacement survey package was sent to them.Third, we found that it is important to have direct contactwith the executives who had the authority to decide whetherto participate in the study. Finally, many companies couldnot participate in the study because of company policies.

Measurement scales

The survey questions and the descriptive statistics for eachmeasurement scale are in Table 2. The Make process hasfour indicators (JIT, TQM, TPM, and HRM). This section

336 H. Zhou et al.

first describes the multiple criteria that are used to validatethe measurement scales. Then, the final results of the scaleanalysis are presented.

Scale validity and reliabilityThe measurement scale development process supports thevalidity and reliability of the measurement scales. First,exploratory factor analysis was performed. Then, confirma-tory factor analysis (CFA) was performed. The contentvalidity of the scales was established by the literature. Inaddition, both academicians and practicing managersassessed the survey questionnaire content validity before the

surveys were distributed. Construct validity ensures that theconceptual constructs are operationalized in the appropriateway. To ensure construct validity, exploratory factor analysiswith principal component method is used. According to Hairet al. (1998) and Carmines and Zeller (1979), the factor load-ings need to be at least .3. Only one factor in each constructcan have an eigenvalue that is larger than 1.00 and the vari-ance explained by the first factor in each construct is at least40%. Reliability is defined as the extent to which the mea-sures can yield same results on other replication studies. Theinternal consistency measured by Cronbach’s alpha is usedto measure the construct reliability in this study. The lower

Table 2: Survey questions and descriptive statistics

Survey question Mean SD

To what extent have the following planning practices been implemented in your company[1 = not implemented, 7 = extensively implemented]Plan1. ‘‘What–if’’ analysis has been implemented for supply ⁄demand balancing 3.41 1.98Plan2. A change in the demand information instantaneously ‘‘reconfigures’’ theproduction and supply plans

3.21 2.18

Plan3. Online visibility of supply chain demand requirements 3.35 2.05Plan4. The designation of a supply chain planning team 3.65 2.15Plan5. Both marketing and manufacturing functions are involved in supply chainplanning process

3.70 2.08

To what extent have the following sourcing practices been implemented in your company[1 = not implemented, 7 = extensively implemented]Source1. Long-term relationships with strategic suppliers 5.51 1.52Source2. Reduction in the number of suppliers 4.69 1.87Source3. Just-in-time delivery from suppliers 4.29 1.92Source4. Frequent measurement of suppliers’ performance 4.75 1.83Source5. Frequent performance feedback to suppliers 4.44 1.94

To what extent have the following production practices been implemented in your company[1 = not implemented, 7 = extensively implemented]JIT1. Pull system 3.97 2.11JIT2. Cellular manufacturing 3.42 2.25JIT3. Cycle time reduction 4.40 1.96JIT4. Agile manufacturing strategy 3.10 2.04JIT5. Bottleneck ⁄ constraint removal 4.02 1.83TPM1. Preventive maintenance 4.98 1.75TPM2. Maintenance optimization 4.08 2.00TPM3. Safety improvement programs 5.57 1.65TPM4. Planning and scheduling strategies 5.02 1.50TQM1. Total quality management 4.88 1.84TQM2. Statistical process control 4.19 2.16TQM3. Formal continuous improvement program 4.75 2.06TQM4. Six-sigma techniques 3.36 2.20HRM1. Self-directed work teams 3.69 1.93HRM2. We use knowledge, skill, and capabilities as criteria to select employees 5.14 1.60HRM3. Direct labor technical capabilities are acknowledged 4.67 1.72HRM4. Employee cross-training program 4.76 1.51

To what extent have the following delivery practices been practiced in your company[1 = not practiced, 7 = extensively practiced]Deliver1. We have a single point of contact for all order inquiries 5.12 1.82Deliver2. We have real-time visibilities of order tracking 4.41 2.17Deliver3. We consolidate orders by customers, sources, carriers, etc. 4.59 2.03Deliver4. We use automatic identification during the delivery process to track order status 3.26 2.19

Supply Chain Integration and SCOR Model 337

limit of .7 is considered acceptable (Nunnally and Bernstein1994; Hair et al. 1998). The results in Table 3 show that allfactor loadings meet the criterion of larger than .3. The fac-

tor analysis results from Table 3 also show that all con-structs satisfy the unidimensionality requirement. For allscales except Deliver process, only one eigenvalue is larger

Table 3: Final results of measurement validation

Scale name Variable name Factor loading Scale statistics

Plan Plan1 .75 Cronbach’s alpha: .80Largest eigenvalue (variance explained): 2.80 (56%)Second largest eigenvalue (variance explained): .77 (15%)Average variance extracted: .46Reliability, q: .81Average variance shared, c2: .34

Plan2 .72Plan3 .74Plan4 .80Plan5 .75

Source Source1 .59 Cronbach’s alpha: .76Largest eigenvalue (variance explained): 2.62 (52%)Second largest eigenvalue (variance explained): .82 (16%)Average variance extracted: .44Reliability, q: .78Average variance shared, c2: .39

Source2 .58Source3 .66Source4 .87Source5 .87

MakeJIT JIT1 .57 Cronbach’s alpha: .82

Largest eigenvalue (variance explained): 2.99 (60%)Second largest eigenvalue (variance explained): .87 (17%)

JIT2 .79JIT3 .86JIT4 .77JIT5 .84

TPM TPM1 .90 Cronbach’s alpha: .89Largest eigenvalue (variance explained): 2.70 (68%)Second largest eigenvalue (variance explained): .67 (17%)

TPM2 .79TPM3 .83TPM4 .77

TQM TQM1 .79 Cronbach’s alpha: .86Largest eigenvalue (variance explained): 2.83 (71%)Second largest eigenvalue (variance explained): .50 (12%)

TQM2 .85TQM3 .89TQM4 .84

HRM HRM1 .68 Cronbach’s alpha: .77Largest eigenvalue (variance explained): 2.40 (60%)Second largest eigenvalue (variance explained): .70 (18%)

HRM2 .78HRM3 .88HRM4 .75

Deliver Deliver1 .68 Cronbach’s alpha: .73Largest eigenvalue (variance explained): 2.22 (56%)Second largest eigenvalue (variance explained): 1.01 (25%)Average variance extracted: .61Reliability, q: .86Average variance shared, c2: .45

Deliver2 .83Deliver3 .78Deliver4 .68

Make JIT .79 Cronbach’s alpha: .86Largest eigenvalue (variance explained): 2.81 (70%)Second largest eigenvalue (variance explained): .52 (13%)Average variance extracted: .42Reliability, q: .74Average variance shared, c2: .40

TPM .87TQM .87HRM .82

Degree of freedom 130Chi-squared statistics 267Normed chi-square 2.06Nonnormed fit index (NNFI) .91Comparative fit index (CFI) .93Incremental fit index (IFI) .93Root mean square error of approximation (RMSEA) .09All loadings significant at p < .05

338 H. Zhou et al.

than 1.00 and the variance explained by the largest eigen-value is larger than 40%. For the Deliver process, the secondlargest eigenvalue is slightly larger than 1.00. The scree testsuggests that one factor is the most appropriate for this setof items. Thus, the Deliver process is determined to be unidi-mensional. For the reliability, Table 3 shows that all scaleshave Cronbach’s alpha values of .7 or higher. Thus, it is con-cluded that all measurement scales are reliable.

After performing the exploratory factor analysis, CFAwas performed to confirm the measurement model of thestructural equation model. As Table 3 shows, reliability rhoscores for all constructs exceed the threshold of .7 (Fornelland Larcker 1981). For each construct, the average sharedvariance is smaller than the average variance extracted.Moreover, the overall CFA model statistics (comparative fitindex [CFI] = .93, incremental fit index [IFI] = .93, non-normed fit index [NNFI] = .91, and root mean square errorof approximation [RMSEA] = .09) suggest that the pro-posed construct structure has a reasonably good fit. It is tobe noted that JIT, TPM, TQM, and HRM do not have thethree CFA-related measures (i.e., average variance extracted,shared variance, and reliability rho) because they are themeasurement items for the latent variable Make in the CFAmodel. For example, JIT value in the CFA model is theaverage of the five JIT items (i.e., JIT1, JIT2, JIT3, JIT4,and JIT5) in Table 3.

As we used a single informant to answer all questions,potential common method bias is checked. The items com-prising the scales of planning, sourcing, JIT, TPM, TQM,HRM, and delivery were not highly similar in content. Therespondents are familiar with the constructs. Harman’s one-factor test of common method bias (Podsakoff and Organ1986; Podsakoff et al. 2003; Hochwarter et al. 2004) foundseveral distinct factors for the variables, which suggested thatcommon method variance bias was not a problem.

Summary of research methodology

This study used a survey research method. The analysis wasbased on 125 useable responses from U.S. manufacturingfirms. The survey followed the standard process suggested byDillman (2007) to ensure that a good and representativesample was obtained. After the sample was obtained, the sta-tistical analysis has been performed to ensure that the mea-surement scales are valid and reliable before themeasurement scales have been used in further statistical anal-ysis such as structural equation model. Other measurementconcerns such as common method bias have been addressedin this research methodology stage.

ANALYSIS RESULTS

Descriptive statistics

The descriptive statistics in Table 2 show that the mean ofthe supply chain planning and JIT practices are relativelylow compared with the practices of the Source, TPM, TQM,HRM, and Deliver processes. The means of the planning

and JIT practices are 3.46 and 3.78, respectively, while themeans of the Source, TPM, TQM, HRM, and Deliver prac-tices are 4.74, 4.91, 4.30, 4.57, and 4.34, respectively. For thefive planning practices, all of them are below 4.00. In con-trast to that, all five sourcing practices have scores above4.00. In the Make process, it is quite surprising to see thatthe mean of the pull system, cellular manufacturing, agilemanufacturing strategy, six-sigma techniques, and self-direc-ted work teams are below 4.00, since the lean manufacturinghas been introduced to North America for more than20 years and many studies have reported extensive imple-mentation of lean practices in North American firms (Powell1995; Flynn et al. 1999; Shah and Ward 2003). It seems thatthe firms are doing well in the TPM area and most aspectsof TQM and HRM. The factor analysis for the four indica-tors (JIT, TPM, TQM, and HRM) of the Make process sup-ports the idea of lean manufacturing bundles in Shah andWard (2003). Regarding the delivery process, the firms aredoing well on all practices except automatic identification. Insum, the descriptive statistics suggest that firms are doingwell overall in sourcing, delivery, TPM, TQM, and HRM,the means of which are above 4.00. But the firms are notdoing as well on supply chain planning and JIT production,the means of which are below 4.00.

Structural equation model

We use the structural equation model method to test thehypotheses H1–H5 about the relationships among the foursupply chain processes and the results are shown in Figure 3.The results are summarized in Tables 3 and 4. Then we useSobel tests to test the two mediation effects hypothesized inH6 and H7. The results are shown in Table 5.

Before running the structural equation model, the scorefor JIT, TPQ, TQM, and HRM were calculated according tothe average of the items with related factor. Therefore, JIT,TQM, TPM, and HRM are considered as indicators forMake construct. A number of fit statistics were used to eval-uate the models because no single measure was adequate(Bollen and Long 1993). A normed chi-square below oneindicates that the model is overfitted (Joreskog 1969), whilea value larger than 3.0 (Carmines and McIver 1981) to 5.0(Wheaton et al. 1977) indicates that a model does not ade-quately fit the data. The normed chi-square adjusts the sam-ple discrepancy function by the degree of freedom. Hairet al. (1998) provide guidelines for interpreting the RMSEA

Table 4: Results of hypotheses tests

Path in the structural

model

Path coefficient

estimate (t-value) Outcome

Plan fi Source (H1) .46* (3.27) SupportedPlan fi Make (H2) .31* (3.35) SupportedPlan fi Deliver (H3) .44* (3.13) SupportedSource fi Make (H4) .63* (3.71) SupportedMake fi Deliver (H5) .38* (2.80) Supported

Note: *Significant at p < .05.

Supply Chain Integration and SCOR Model 339

as follows: RMSEA < .05, good model fit; .05 <RMSEA < .10, reasonable model fit; RMSEA > .10, poormodel fit. Hair et al. (1998) also suggest that the model fit isgood if NNFI and CFI are above .9. Both NNFI and CFIadjust the sample discrepancy function by the degree of free-dom. The IFI is similar to NFI but it has a correction in thedenominator to decrease the sample size effect (Bollen 1989).It is desirable to have IFI no less than .9. As shown in thebottom of Table 3, the fit indices of our model were:v2 = 267 with df = 130 (i.e., the normed chi-square is 2.06),NNFI = .91, CFI = .93, IFI = .93, and RMSEA = .09.All fit statistics fell in the desirable ranges and suggested thatthe model had a reasonably good fit. Based on the structuralequation model, the results of the five hypotheses are shownin Figure 3 and Table 4. According to the t-values inTable 4, all five hypotheses were supported at the .05 signifi-cance level. In addition to a good fit of the structural model,a good structural equation model needs to have a good mea-surement model (i.e., the path coefficients of all indicators tothe related latent variables are significant at the .05 level).

According to the SEM results, all path coefficients are signif-icant at the .05 level and the t-values are larger than 2.0.

Mediation effect

To test the two mediation effects, the Sobel tests are used.For each mediation test, three regressions are required. Takethe mediation effect of Source process as an example (seeTable 5). First, Plan process must have significant influenceon Make process. Second, Plan process must have significantinfluence on Source process. Third, the influence of Plan pro-cess on Make process must change significantly when Sourceprocess is entered into the regression model. Then a Sobeltest is performed to test the significance of the mediationeffect (Venkatraman 1989).

Model 1 in Table 5 shows that the Plan process has a sig-nificant influence on Make process. The regression coefficientis .405, which is significant at the 5% level. Model 2 showsthat the Plan process has a significant influence on theSource process. The coefficient is .392, which is significant atthe 5% level. Model 3 shows that the coefficient of the Planprocess on the Make process is reduced to .212 when Sourceprocess is entered into regression together with the Plan pro-cess. To test whether this reduction is significant, a Sobel testis performed. The calculation of the Sobel test statistics isshown in Table 5. The result shows that the Sobel test statis-tic is 4.5. The p-value of this Sobel test is smaller than .05.This means that the Source process significantly mediates theinfluence of the Plan process on the Make process. Similarregression analysis is performed for the mediation effect ofthe Make process. The results are summarized in Table 5.The Sobel test statistic is 3.5. The p-value of this Sobel testis smaller than .05 as well. Thus, we conclude that the Makeprocess significantly mediates the influence of the Plan processon the Deliver process.

Summary of analysis

This analysis section first provides the descriptive statistics ofall measurement items, which gives the readers an overallpicture of the data set. Using the measurement scales vali-dated in the third section, the structural equation modelinganalysis tests the relationships among the four processes in

Plan

Source

Make

Deliver

H1: γ1=.46*

H2: γ2=.31*

H3: γ3=.44*

H4: β1=.63*

H5: β2=.38*

Note: * Indicates significance at p < .05

Figure 3: Supply Chain Operations Reference (SCOR) modelwith results.

Table 5: Mediation test for Source and Make processes

Tests for Source process Tests for Make process

Variable Plan Source Variable Plan Make

Model 1 (dependent variable: Make) .405* (.062) Model 1 (dependent variable: Deliver) .504* (.075)Model 2 (dependent variable: Source) .392* (.067) Model 2 (dependent variable: Make) .405* (.062)Model 3 (dependent variable: Make) .212* (.059) .493* (.071) Model 3 (dependent variable: Deliver) .334* (.103) .419* (.103)

Sobel test statistics is: .493 · .392 ⁄ sqrt (.4932 · .0672 + .3922 ·.0712) = 4.5

Sobel test statistics is: .419 · .405 ⁄ sqrt (.4192 · .0622 + .4052 ·.1032) = 3.5

Notes: The numbers within parentheses are the standard errors of the coefficients.

*Significant at p < .05.

340 H. Zhou et al.

the SCOR model. The statistics in Tables 3 and 4 generallysupport the relationships proposed in the SCOR model.Finally, regression analysis is used to test the mediation roleof the Make process and the Source process in the SCORmodel.

RESULTS AND DISCUSSION

This study marks the first empirical study that tests the valid-ity of the relationships among the supply chain processes inthe SCOR model. According to the results in Figure 3 andTable 4, the relationships of the supply chain processes inthe SCOR model are supported as expected (Supply ChainCouncil 2010). The Plan process has significant positive influ-ence on Source, Make, and Deliver processes. Source processhas significant positive influence on Make process whileMake process has significant positive influence on Deliverprocess. The strongest link is from the Source process to theMake process while the weakest link is from the Plan processto the Make process.

The relatively weak link from the Plan process to theMake process reveals some issues in the SCOR model. Whilethe Make process in the SCOR model does include theHRM and TPM practices, the Plan process of the SCORmodel does not cover the planning about HRM and TPM(Supply Chain Council 2010). The Plan process primarilyfocuses on sourcing, JIT production, and delivery practices.In the future, the SCOR model might need to include theplanning activities for HRM (leadership) and TPM to keepthe SCOR model consistent with itself.

The results in Table 5 support the hypotheses that (1)Source process mediates the influence of Plan process onMake process, and (2) Make process mediates the influenceof Plan process on Deliver process. The significant mediationeffect suggests that an effective Source process plays a criticalrole in the relationship between Plan process and Make pro-cess and an effective Make process plays a critical role in therelationship between Plan and Deliver processes. Accordingto Table 5, the indirect influence that Plan process has onthe Make process through the Source process is.392 · .493 = .193 (.392 from Model 2 and .493 from Model3). The direct influence that Plan process has on the Makeprocess is .212 (from Model 3). The total influence (directinfluence + indirect influence) that Plan process has on theMake process is .193 + .212 = .405. Table 5 shows thatabout 34% (1 ) .334 ⁄ .504 = .34) of the total influence thatPlan process has on the Deliver process is the indirect influ-ence through the Make process when Make process isentered into the regression.

To our best knowledge, this is the first study thatempirically tests the relationships among all four supplychain processes in the SCOR model. Very few studies(Lockamy and McCormack 2004; Huang et al. 2005) con-ceptually discussed the SCOR model. To date, this is theonly study that has comprehensively addressed the rela-tionships among all four supply chain processes. Thisstudy contributes to the literature by providing a holisticview of the supply chain management from the process

perspective and offers an integrative analysis of the supplychain processes.

For practitioners, the findings provide rigorous empiricalevidence in support of the SCOR model. The finding givespractitioners statistical confidence in the implementation anduse of the SCOR model. For example, this study reveals thefirms’ insufficiency in the supply chain planning practices,although the Plan process is shown to be important for allother three processes. This study identifies the quantitativerelationships among the four supply chain processes, whichcan help firms assess their supply chain strengths andweaknesses. The descriptive statistics can also help firms tobenchmark themselves with other firms.

CONCLUSION AND FUTURE RESEARCH

This study marks the first empirical effort to examine thevalidity of the SCOR model. It has been shown that the rela-tionships among the supply chain processes in the SCORmodel are generally supported. With data from 125 NorthAmerica manufacturing companies, the Plan process has sig-nificant positive influence on the Source, Make, and Deliverprocesses. The Source process has significant positive influ-ence on the Make process and the Make process has signifi-cant positive influence on the Deliver process. The Sourceprocess mediates the impact of the Plan process on the Makeprocess and the Make process mediates the impact of thePlan process on the Deliver process. Among the four supplychain processes, it appears that the Plan process has receivedthe least attention from the firms so far, although it doeshave significant influence on all the other three processes.

This study contributes to both academic literature andpractitioners. Several recent studies have addressed the issueof supply chain integration and governance (Chen et al.2009a,b; Richey et al. 2010). As Chen et al. (2009b) men-tioned, the SCOR model is an illustration of the processapproach to supply chain integration. This study provides aholistic view of supply chain integration from an empiricalsurvey research methodology perspective. It reveals thequantitative relationships among the four components of theSCOR model. Richey et al. (2010) suggested that the supplychain governance which balances the self-interest and inter-dependency in supply chains can help improve performance.Through the Source and Deliver components of the SCORmodel, this study enhances our understanding of the impor-tance of working with suppliers and customers in supplychain management.

For practitioners, the empirical validation of the SCORmodel structure gives practitioners more confidence in apply-ing the SCOR model to the real business world. The studyalso reveals the weaknesses in using the SCOR model suchas in the planning area. The statistics in this study providespractitioners a quantitative sense of the various linkages inthe SCOR model and also help firms to benchmark them-selves with other firms. The quantified relationships amongthe four components of the SCOR model can help firms bal-ance their investments in different components of the SCORmodel and optimize their supply chain investment returns.

Supply Chain Integration and SCOR Model 341

As this study is the first empirical effort to validate theSCOR model, this study primarily focuses on the relation-ships among the four supply chain processes in Level 1 ofthe SCOR model. The measurement items are used to opera-tionalize the concepts in Level 1 of the SCOR model. Thislimits the richness of this study. Future studies can investi-gate Level 2 or below of the SCOR model with more detailssuch as information sharing and coordination.

Information sharing and coordination is an importantaspect of supply chain management (Chen and Paulraj 2004;Li et al. 2005; Sahin and Robinson 2005). Future researchcan address this topic with respect to the use of the SCORmodel. For example, Level 3 of the SCOR model does spec-ify the information inputs and outputs of process element.How this information sharing among supply chain partnerscan influence the coordination among supply chain partnersand therefore impacts the value of the SCOR model is aninteresting topic.

Last, although the SCOR model was initially developedfor manufacturing firms, more service organizations havebegun to use SCOR model as well (Malin 2006). This studyonly collected data from manufacturing firms. Future studycan extend the SCOR model to service operations and seehow the differences between manufacturing and service oper-ations influence the relationships among the supply chainprocesses.

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SHORT BIOGRAPHIES

Honggeng Zhou (PhD The Ohio State University) is anAssociate Professor in the Whittemore School of Businessand Economics at the University of New Hampshire, wherehe teaches courses to undergraduates and MBA students.His primary research interests include supply chain manage-ment and operations management. He has published in Jour-nal of Operations Management, Decision Sciences,International Journal of Production Economics, etc.

W. C. Benton, Jr. (PhD Indiana University) is the EdwinD. Dodd Professor of Management Sciences in the Max M.Fisher College of Business at The Ohio State Universitywhere he teaches courses in health care delivery, operationsmanagement, purchasing, and supply chain management toundergraduates, MBAs, and doctoral candidates. He haspublished numerous articles in the fields of health care, sup-ply chain management, and sustainability.

David A. Schilling (PhD Johns Hopkins University) is aProfessor of Management Science at the Fisher College ofBusiness, The Ohio State University. He has publishednumerous articles in the fields of transportation, locationanalysis, and multi-objective programming.

Glenn W. Milligan (PhD The Ohio State University) is anEmeritus Professor of Management Sciences at the FisherCollege of Business, The Ohio State University. He hasserved as the Chair of the Department of ManagementSciences. He has published numerous articles in the fields ofquality management classification and log-linear models.

344 H. Zhou et al.

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