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Supply Chain Integration and the SCOR Model Honggeng Zhou

1 , W. C. Benton, Jr.

2 , David A. Schilling

2 , and Glenn W. Milligan

1 University of New Hampshire

2 The 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 North American manufacturing firms were collected. The results show that the relationships among the supply chain processes in the SCOR model are generally supported. The Plan process has significant positive influence on the Source, Make, and Deliver processes. The Source process has significant positive influence on the Make process and the Make process has significant positive influence on the Deliver process. The Source process mediates the impact of the Plan process on the Make process and the Make process mediates the impact of the Plan process on the Deliver process. The findings provide managers with empirical evidence that the SCOR model is in fact valid.

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

INTRODUCTION

The Supply Chain Operations Reference (SCOR) model was developed by the Supply Chain Council in 1996. The SCOR model focuses on the supply chain management function from an operational process perspective and includes cus- tomer interactions, physical transactions, and market interac- tions. In the past decade, the SCOR model has been widely adopted by many companies including Intel, General Electric (GE), Airbus, DuPont, and IBM. According to the Supply Chain Council’s (2010) website, ‘‘While remarkably simple, it [the SCOR model] has proven to be a powerful and robust tool set for describing, analyzing, and improving the supply chain.’’ In the literature, several recent studies have reviewed the SCOR model (Huang et al. 2004, 2005). Many other studies (McCormack 1998; Lockamy and McCormack 2004; Supply Chain Council 2010) have attempted to measure the SCOR 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 companies throughout the world. Intel is one of the first major U.S. corporations to adopt the SCOR model (Supply Chain Council 2010). In 1999, Intel started its first SCOR project for its Resellers Product Division. Later, they expanded the SCOR model implementation to the Systems Manufacturing Division. Several other SCOR projects were conducted afterward. The benefits of implementing the SCOR model included faster cycle times, less inventories, improved visibil- ity of the supply chain, and access to important customer information in a timely fashion. GE implemented the SCOR model in its Transportation Systems unit, which reported

sales of $2.6 billion in year 2001. The use of the SCOR model 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 has used the SCOR model in its overall business framework, which directly resulted in improved customer service and reduced inventories. In Europe, Degussa (a German chemical company) used the SCOR model to streamline its newly merged businesses. Specifically, Degussa set up a team of cross-functional employees to implement the SCOR project. After a three-week pilot project, the SCOR team found opportunities in the existing supply chain processes. It was reported that the SCOR project was expected to save the firm 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 to define, measure, and improve supply chains. The first phase of the project led to 2% reduction in overall drug inventory the first year. The hospital reported an 8–10% reduction in excess and obsolete inventory during the next two years. Meanwhile, the improved visibility and planning generated 21% capacity increase and an 8% increase in demand. The prep times for key procedures were reduced by as much as 40%, which resulted in reduced labor costs.

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

The results of this study show that the relationships among the supply chain processes in the SCOR model are generally supported. The Plan process has significant positive

Corresponding author: W. C. Benton, Jr., Department of Management Sciences, Fisher College of Business, The Ohio State University, 2100 Neil Ave- nue, Columbus, OH 43210, USA; E-mail: [email protected]

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

influence on the Source, Make, and Deliver processes. The Source process has significant positive influence on the Make process and the Make process has significant positive influ- ence on the Deliver process. Among the four supply chain processes, the Plan process has received the least attention from the implementation firms. The findings from this study provide 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 the research 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 are proposed. The literature review provides the theoretical foun- dation for this research. The theoretical foundation is reflected 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. The SCOR model diagram is given in Figure 1. Level 1 consists of five supply chain processes: Plan, Source, Make, Deliver, and Return. As the Return process was not in the first four versions of the SCOR model and is not as mature as the other four processes, this study focuses on the other four processes (Plan, Source, Make, and Deliver), which have been widely adopted by practitioners. Level 2 of the SCOR model describes core processes. Level 3 of the SCOR model specifies the best practices of each process. According to the definition in the SCOR model, Plan includes the processes that balance aggregate demand and supply to develop a course of action which best meets sourcing, production, and delivery 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 product to a finished state to meet planned or actual demand. Deliv- ery includes all processes which provide finished goods and services to meet planned or actual demand (Supply Chain Council 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 demand and supply. The SCOR model suggests that the capability to run ‘‘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 is to get real-time information and rebalance supply chains using updated information. Information sharing in supply chains can lead to improved performance (Fawcett et al. 2011). According to Narasimhan and Kim (2001), the use of information 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) find that one primary reason that Japanese automobile firms have an advantage over traditional U.S. automobile firms is that they used designated planning teams to coordinate different functions. Furthermore, the literature suggests that interfunc- tional coordination within a firm is critical for supply chain planning because the alignment between the functions is nec- essary to achieve a firm’s strategic goals (Supply Chain Council 2010). For example, many studies (Hill 1994; Haus- man et al. 2002) have found the importance of aligning mar- keting and manufacturing operations to improve performance.

Source (buyer–supplier relationship)

Sourcing practice connects manufacturers with suppliers and is critical for manufacturing firms. The academic literature and the SCOR model have identified several sourcing practices as best practices (Carr and Pearson 1999; Chen and Paulraj 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 sourcing practices. 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 good sourcing practice. The benefits of JIT delivery have been widely documented (Benton and Shin 1998; Ahmad and Sch- roeder 2001; Dong et al., 2001). Furthermore, providing feedback about suppliers’ performance evaluations is a good sourcing practice. According to Carr and Pearson (1999), supplier evaluation systems have a direct positive impact on buyer–supplier relationship, and an indirect impact on firm financial performance. More recently, Prahinski and Benton (2004) studied the role of communication in supply chain management. They found that executives at buying firms need to incorporate indirect influence strategy, formality, and feedback into supplier development programs.

Make (transformation process)

The Make process includes the practices that efficiently transform raw materials into finished goods to meet supply chain 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

Plan 1

Configuration Level (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.

Process Element Level (Decompose Processes)

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

P1.1 Identify, Prioritize, and

Aggregate Supply-Chain Requirements

P1.2 Identify, Assess, and

Aggregate Supply-Chain Requirements

P1.3 Balance Production

Resources with Supply- Chain Requirements

P1.4 Establish 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.

Implementation Level (Decompose Process Elements)

Not in

Scope

Su p p ly -C h ai n O p er a

o n s R ef er en

ce -m

o d el

Return

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

334 H. Zhou et al.

(Shah and Ward 2007; Benton 2011b) and the SCOR model include four groups of practices for the Make process: JIT production, 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 (Wemmerlov and Hyer 1989; Blackburn 1991; Powell 1995; MacDuffie et al. 1996; Benton and Shin 1998; Flynn et al. 1999; Fuller- ton and McWatters 2001; Fullerton et al. 2003; Benton 2011a). The review of quality management literature has led to 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; Cua et al. 2001; Nair 2006; Kaynak and Hartley 2008). TPM is a manufacturing program that primarily maximizes equipment effectiveness throughout its entire life (Nakajima 1988; Cua et al. 2001). Several studies have explored the good practices of TPM and their positive relationship with business perfor- mance (Cua et al. 2001). The literature review led to the identification of the following effective TPM practices: pre- ventive maintenance; safety improvement program; planning and scheduling strategies; and maintenance optimization. The HRM practices emphasize employee team work and workforce capabilities. Employee team work is important for improving production, because frontline employees working as a team can leverage the experience of all employees and greatly contribute to process and product improvement (Hayes and Wheelwright 1984). Workforce capability is another 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 supply chain partners, which increases the real-time visibility of order tracking. Agility is also an important competence of world class logistics. Gurin (2000) describes how Ford part- nered with the United Parcel Service to develop and imple- ment an Internet-based delivery process, significantly improving Ford’s delivery performance. An Internet-based delivery system can significantly enhance the real-time order tracking capability. Other best delivery practices identified by the SCOR model include a single contact point for all order inquiries, order consolidation, and the use of auto- matic identification. The bar code technology significantly improves the relationship between suppliers and buyers and allows some emerging inventory management programs such as vendor-managed inventory program. Ahmad and Schroe- der (2001) identify several factors that affect delivery perfor- mance. The factors include JIT management, quality management, production instability, and so on. However, Ahmad and Schroeder (2001) do not use a scale to measure the 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 in Figure 2. First, effective supply chain planning practices are expected to influence the implementation of effective sourc- ing, production, and delivery practices (Lockamy and Mc- Cormack 2004). The planning process is expected to balance the aggregate supply chain demand and supply. The ability to balance demand and supply in real time can enhance a long-term relationship with suppliers who can better respond to 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, and HRM. For example, without a good planning process, a JIT production 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 delivery process. To respond to customer demand changes quickly, firms need the ability to track the order delivery status in real time (Makatsoris and Chang 2004). Based on the SCOR model 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 of Make process (St. John and Young 1991; Hines 1996; Ben- ton 2010). A good long-term relationship with suppliers can help firms implement JIT production. Without a good JIT delivery from suppliers, a JIT production system would be

Plan

Source

Make

Deliver

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

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

Supply Chain Integration and SCOR Model 335

impossible. A good relationship with suppliers also helps control the quality of the inputs, which helps the use of TQM 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 suppliers and 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 delivery process (Henig and Levin 1992; Garcia et al. 2004). A good JIT production system produces products in a timely manner according to customer needs, which is essential to the implantation of JIT delivery. A good TQM program and knowledgeable employees are also necessary to facilitate the use of JIT delivery. In addition, an effective production sys- tem can help increase the visibility of order tracking throughout the whole supply chain system. Therefore, the following hypothesis is proposed.

H5: Make process positively influences Deliver process.

Although H1–H5 are directly from the SCOR model, the empirical validation of the SCOR model contributes to the academic literature and provides value to the practitioners. Taken together, H1, H2, and H4 suggest that Source process mediates the influence of Plan process on the Make process. The mediation effect suggests that the Plan process drives better Make process at least partially because good supply chain planning practices have positive influence on sourcing practices. Similarly, H2, H3, and H5 together suggest that Make process mediates the influence of Plan process on the Deliver process. Thus, this study will use Sobel tests to directly 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 is mediated by Make process.

RESEARCH METHOD

Sample

The research objectives were achieved by obtaining responses from 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 Corning Corp., Owens Corning, Nachi Robotic Systems, Windsor Mold Inc., and Minntech Corporation. The respondents were senior executives and held titles such as CEO, Presi- dent, Vice President, and Director. The average number of employees in the respondents’ firms was about 5,000. Eight

companies had more than 10,000 employees. The median annual sales value, as reported by the respondents, was between $100 million and $500 million. Five companies had annual sales of more than $5 billion. Four academic experts and three industry experts were asked to review the survey instrument (questionnaire) to ensure the relevance and clarity of the survey instrument. The industry experts who reviewed the questionnaire also provided insights as to the type of job titles that may reflect probable knowledge of the SCOR model. Utilizing this guidance, the sample was selected based upon job titles and job descriptions available. Employing the multiple contact strategy as suggested by Dillman (2007), a total 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 the accuracy of the mailing address and make the selected respondents aware of the forthcoming questionnaire. Two weeks after the initial postcard was mailed, the first round survey packages were mailed. According to Dillman (2007), at least two weeks are needed between contacts to allow enough time for the postcards with wrong addresses to be returned to us. The survey packages contained three items: the personalized letter of introduction about the importance of 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 thank those who had returned the questionnaire and remind those who had not returned the questionnaire. Two weeks after sending 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 were mailed, those companies who had not replied were contacted by telephone. Several insights were gained from the success- ful telephone conversation. First, respondents in many of the companies, the informant forwarded the questionnaires to others within the company to complete. However, if the respondent who received the questionnaire could not respond to certain questions, the respondent would most likely for- ward the questionnaire to another person who can answer the questionnaire. It is expected that if the questionnaire was forwarded, the return rate is greatly reduced. This process also resulted in significantly longer cycle times (Dillman 2007). Second, many respondents who were interested in the study could not locate the questionnaire that was sent to them. Thus, a replacement survey package was sent to them. Third, we found that it is important to have direct contact with the executives who had the authority to decide whether to participate in the study. Finally, many companies could not participate in the study because of company policies.

Measurement scales

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

336 H. Zhou et al.

first describes the multiple criteria that are used to validate the measurement scales. Then, the final results of the scale analysis are presented.

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

surveys were distributed. Construct validity ensures that the conceptual constructs are operationalized in the appropriate way. To ensure construct validity, exploratory factor analysis with principal component method is used. According to Hair et al. (1998) and Carmines and Zeller (1979), the factor load- ings need to be at least .3. Only one factor in each construct can have an eigenvalue that is larger than 1.00 and the vari- ance explained by the first factor in each construct is at least 40%. Reliability is defined as the extent to which the mea- sures can yield same results on other replication studies. The internal consistency measured by Cronbach’s alpha is used to 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 1.98 Plan2. A change in the demand information instantaneously ‘‘reconfigures’’ the production and supply plans

3.21 2.18

Plan3. Online visibility of supply chain demand requirements 3.35 2.05 Plan4. The designation of a supply chain planning team 3.65 2.15 Plan5. Both marketing and manufacturing functions are involved in supply chain planning 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 1.52 Source2. Reduction in the number of suppliers 4.69 1.87 Source3. Just-in-time delivery from suppliers 4.29 1.92 Source4. Frequent measurement of suppliers’ performance 4.75 1.83 Source5. 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 2.11 JIT2. Cellular manufacturing 3.42 2.25 JIT3. Cycle time reduction 4.40 1.96 JIT4. Agile manufacturing strategy 3.10 2.04 JIT5. Bottleneck ⁄ constraint removal 4.02 1.83 TPM1. Preventive maintenance 4.98 1.75 TPM2. Maintenance optimization 4.08 2.00 TPM3. Safety improvement programs 5.57 1.65 TPM4. Planning and scheduling strategies 5.02 1.50 TQM1. Total quality management 4.88 1.84 TQM2. Statistical process control 4.19 2.16 TQM3. Formal continuous improvement program 4.75 2.06 TQM4. Six-sigma techniques 3.36 2.20 HRM1. Self-directed work teams 3.69 1.93 HRM2. We use knowledge, skill, and capabilities as criteria to select employees 5.14 1.60 HRM3. Direct labor technical capabilities are acknowledged 4.67 1.72 HRM4. 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 1.82 Deliver2. We have real-time visibilities of order tracking 4.41 2.17 Deliver3. We consolidate orders by customers, sources, carriers, etc. 4.59 2.03 Deliver4. 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 Bernstein 1994; Hair et al. 1998). The results in Table 3 show that all factor 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 all scales 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: .80 Largest eigenvalue (variance explained): 2.80 (56%) Second largest eigenvalue (variance explained): .77 (15%) Average variance extracted: .46 Reliability, q: .81 Average variance shared, c2: .34

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

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

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

Make JIT JIT1 .57 Cronbach’s alpha: .82

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

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

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

TPM2 .79 TPM3 .83 TPM4 .77

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

TQM2 .85 TQM3 .89 TQM4 .84

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

HRM2 .78 HRM3 .88 HRM4 .75

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

Deliver2 .83 Deliver3 .78 Deliver4 .68

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

TPM .87 TQM .87 HRM .82

Degree of freedom 130 Chi-squared statistics 267 Normed chi-square 2.06 Nonnormed fit index (NNFI) .91 Comparative fit index (CFI) .93 Incremental fit index (IFI) .93 Root mean square error of approximation (RMSEA) .09 All 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 second largest eigenvalue is slightly larger than 1.00. The scree test suggests that one factor is the most appropriate for this set of items. Thus, the Deliver process is determined to be unidi- mensional. For the reliability, Table 3 shows that all scales have 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, CFA was performed to confirm the measurement model of the structural equation model. As Table 3 shows, reliability rho scores for all constructs exceed the threshold of .7 (Fornell and Larcker 1981). For each construct, the average shared variance is smaller than the average variance extracted. Moreover, the overall CFA model statistics (comparative fit index [CFI] = .93, incremental fit index [IFI] = .93, non- normed fit index [NNFI] = .91, and root mean square error of approximation [RMSEA] = .09) suggest that the pro- posed construct structure has a reasonably good fit. It is to be noted that JIT, TPM, TQM, and HRM do not have the three CFA-related measures (i.e., average variance extracted, shared variance, and reliability rho) because they are the measurement items for the latent variable Make in the CFA model. For example, JIT value in the CFA model is the average 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. The respondents are familiar with the constructs. Harman’s one- factor test of common method bias (Podsakoff and Organ 1986; Podsakoff et al. 2003; Hochwarter et al. 2004) found several distinct factors for the variables, which suggested that common method variance bias was not a problem.

Summary of research methodology

This study used a survey research method. The analysis was based on 125 useable responses from U.S. manufacturing firms. The survey followed the standard process suggested by Dillman (2007) to ensure that a good and representative sample 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 the measurement scales have been used in further statistical anal- ysis such as structural equation model. Other measurement concerns such as common method bias have been addressed in this research methodology stage.

ANALYSIS RESULTS

Descriptive statistics

The descriptive statistics in Table 2 show that the mean of the supply chain planning and JIT practices are relatively low 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 the means of the Source, TPM, TQM, HRM, and Deliver prac- tices are 4.74, 4.91, 4.30, 4.57, and 4.34, respectively. For the five planning practices, all of them are below 4.00. In con- trast to that, all five sourcing practices have scores above 4.00. In the Make process, it is quite surprising to see that the mean of the pull system, cellular manufacturing, agile manufacturing strategy, six-sigma techniques, and self-direc- ted work teams are below 4.00, since the lean manufacturing has been introduced to North America for more than 20 years and many studies have reported extensive imple- mentation of lean practices in North American firms (Powell 1995; Flynn et al. 1999; Shah and Ward 2003). It seems that the firms are doing well in the TPM area and most aspects of 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 and Ward (2003). Regarding the delivery process, the firms are doing well on all practices except automatic identification. In sum, the descriptive statistics suggest that firms are doing well overall in sourcing, delivery, TPM, TQM, and HRM, the means of which are above 4.00. But the firms are not doing 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 the hypotheses H1–H5 about the relationships among the four supply chain processes and the results are shown in Figure 3. The results are summarized in Tables 3 and 4. Then we use Sobel tests to test the two mediation effects hypothesized in H6 and H7. The results are shown in Table 5.

Before running the structural equation model, the score for JIT, TPQ, TQM, and HRM were calculated according to the average of the items with related factor. Therefore, JIT, TQM, TPM, and HRM are considered as indicators for Make 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 one indicates that the model is overfitted (Joreskog 1969), while a 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. Hair et 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) Supported Plan fi Make (H2) .31* (3.35) Supported Plan fi Deliver (H3) .44* (3.13) Supported Source fi Make (H4) .63* (3.71) Supported Make 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, poor model fit. Hair et al. (1998) also suggest that the model fit is good if NNFI and CFI are above .9. Both NNFI and CFI adjust the sample discrepancy function by the degree of free- dom. The IFI is similar to NFI but it has a correction in the denominator to decrease the sample size effect (Bollen 1989). It is desirable to have IFI no less than .9. As shown in the bottom 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 that the model had a reasonably good fit. Based on the structural equation model, the results of the five hypotheses are shown in Figure 3 and Table 4. According to the t-values in Table 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 to the 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. Take the mediation effect of Source process as an example (see Table 5). First, Plan process must have significant influence on Make process. Second, Plan process must have significant influence on Source process. Third, the influence of Plan pro- cess on Make process must change significantly when Source process is entered into the regression model. Then a Sobel test is performed to test the significance of the mediation effect (Venkatraman 1989).

Model 1 in Table 5 shows that the Plan process has a sig- nificant influence on Make process. The regression coefficient is .405, which is significant at the 5% level. Model 2 shows that the Plan process has a significant influence on the Source process. The coefficient is .392, which is significant at the 5% level. Model 3 shows that the coefficient of the Plan process on the Make process is reduced to .212 when Source process is entered into regression together with the Plan pro- cess. To test whether this reduction is significant, a Sobel test is performed. The calculation of the Sobel test statistics is shown 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 the influence of the Plan process on the Make process. Similar regression analysis is performed for the mediation effect of the Make process. The results are summarized in Table 5. The Sobel test statistic is 3.5. The p-value of this Sobel test is smaller than .05 as well. Thus, we conclude that the Make process significantly mediates the influence of the Plan process on the Deliver process.

Summary of analysis

This analysis section first provides the descriptive statistics of all measurement items, which gives the readers an overall picture of the data set. Using the measurement scales vali- dated in the third section, the structural equation modeling analysis 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) model with 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 · .071

2 ) = 4.5

Sobel test statistics is: .419 · .405 ⁄ sqrt (.4192 · .0622 + .4052 · .103

2 ) = 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 generally support the relationships proposed in the SCOR model. Finally, regression analysis is used to test the mediation role of the Make process and the Source process in the SCOR model.

RESULTS AND DISCUSSION

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

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

The results in Table 5 support the hypotheses that (1) Source process mediates the influence of Plan process on Make process, and (2) Make process mediates the influence of Plan process on Deliver process. The significant mediation effect suggests that an effective Source process plays a critical role in the relationship between Plan process and Make pro- cess and an effective Make process plays a critical role in the relationship between Plan and Deliver processes. According to Table 5, the indirect influence that Plan process has on the Make process through the Source process is .392 · .493 = .193 (.392 from Model 2 and .493 from Model 3). The direct influence that Plan process has on the Make process is .212 (from Model 3). The total influence (direct influence + indirect influence) that Plan process has on the Make process is .193 + .212 = .405. Table 5 shows that about 34% (1 ) .334 ⁄ .504 = .34) of the total influence that Plan process has on the Deliver process is the indirect influ- ence through the Make process when Make process is entered into the regression.

To our best knowledge, this is the first study that empirically tests the relationships among all four supply chain 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 the only study that has comprehensively addressed the rela- tionships among all four supply chain processes. This study contributes to the literature by providing a holistic view of the supply chain management from the process

perspective and offers an integrative analysis of the supply chain processes.

For practitioners, the findings provide rigorous empirical evidence in support of the SCOR model. The finding gives practitioners statistical confidence in the implementation and use of the SCOR model. For example, this study reveals the firms’ insufficiency in the supply chain planning practices, although the Plan process is shown to be important for all other three processes. This study identifies the quantitative relationships among the four supply chain processes, which can help firms assess their supply chain strengths and weaknesses. The descriptive statistics can also help firms to benchmark themselves with other firms.

CONCLUSION AND FUTURE RESEARCH

This study marks the first empirical effort to examine the validity of the SCOR model. It has been shown that the rela- tionships among the supply chain processes in the SCOR model are generally supported. With data from 125 North America manufacturing companies, the Plan process has sig- nificant positive influence on the Source, Make, and Deliver processes. 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 Source process mediates the impact of the Plan process on the Make process and the Make process mediates the impact of the Plan process on the Deliver process. Among the four supply chain processes, it appears that the Plan process has received the least attention from the firms so far, although it does have significant influence on all the other three processes.

This study contributes to both academic literature and practitioners. Several recent studies have addressed the issue of 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 process approach to supply chain integration. This study provides a holistic view of supply chain integration from an empirical survey research methodology perspective. It reveals the quantitative relationships among the four components of the SCOR model. Richey et al. (2010) suggested that the supply chain governance which balances the self-interest and inter- dependency in supply chains can help improve performance. Through the Source and Deliver components of the SCOR model, this study enhances our understanding of the impor- tance of working with suppliers and customers in supply chain management.

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

Supply Chain Integration and SCOR Model 341

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

Information sharing and coordination is an important aspect of supply chain management (Chen and Paulraj 2004; Li et al. 2005; Sahin and Robinson 2005). Future research can address this topic with respect to the use of the SCOR model. 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 partners can influence the coordination among supply chain partners and therefore impacts the value of the SCOR model is an interesting topic.

Last, although the SCOR model was initially developed for manufacturing firms, more service organizations have begun to use SCOR model as well (Malin 2006). This study only collected data from manufacturing firms. Future study can extend the SCOR model to service operations and see how the differences between manufacturing and service oper- ations influence the relationships among the supply chain processes.

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SHORT BIOGRAPHIES

Honggeng Zhou (PhD The Ohio State University) is an Associate Professor in the Whittemore School of Business and Economics at the University of New Hampshire, where he 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 Edwin D. Dodd Professor of Management Sciences in the Max M. Fisher College of Business at The Ohio State University where he teaches courses in health care delivery, operations management, purchasing, and supply chain management to undergraduates, MBAs, and doctoral candidates. He has published numerous articles in the fields of health care, sup- ply chain management, and sustainability.

David A. Schilling (PhD Johns Hopkins University) is a Professor of Management Science at the Fisher College of Business, The Ohio State University. He has published numerous articles in the fields of transportation, location analysis, and multi-objective programming.

Glenn W. Milligan (PhD The Ohio State University) is an Emeritus Professor of Management Sciences at the Fisher College of Business, The Ohio State University. He has served as the Chair of the Department of Management Sciences. He has published numerous articles in the fields of quality management classification and log-linear models.

344 H. Zhou et al.

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