process strategy and service performance? An empirical study

Int. J. Production Economics 137 (2012) 250–262

Contents lists available at SciVerse ScienceDirect

Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe

Does supply chain integration mediate the relationships between product/process strategy and service performance? An empirical study Cornelia Droge a,1, Shawnee K. Vickery b,2, Mark A. Jacobs c,n a

Department of Marketing, The Eli Broad Graduate School of Management, N370 North Business Complex, East Lansing, MI 48824, United States Department of Supply Chain Management, The Eli Broad Graduate School of Management, N370 North Business Complex, East Lansing, MI 48824, United States c Department of Management Information Systems, Operations Management, & Decision Sciences, School of Business, University of Dayton, 300 College Park, Dayton, OH 45469, United States b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 April 2011 Accepted 6 February 2012 Available online 13 February 2012

Determining the antecedents and performance consequences of supply chain integration is a key focus of recent supply chain management research. This study investigates the role of supply chain integration in mediating the effects of product and process modularity strategies on service performance. Adaptive Structuration Theory (AST) provides the theoretical context. The study provides empirical support for the importance of considering product and process strategies in understanding the impact of integration on performance. Canonical correlation analysis and effects decomposition are used to test the research model. The results demonstrate that customer integration mediates the linkages from product modularity and process modularity to delivery performance, as well as mediating the relationship between process modularity and support performance. In contrast, supplier integration mediates the relationship between process modularity and delivery performance only. The overall lack of support for a direct relationship between process modularity and service performance suggests that modular processes (1) lack intrinsic interfaces such as those found in modular product architectures and (2) rely upon integration to fill the role of interface. Product modularity and process modularity are both shown to be related to supplier integration and customer integration, suggesting that they engender integration across a supply chain. & 2012 Elsevier B.V. All rights reserved.

Keywords: Supply chain integration Product modularity Process modularity Logistics performance

1. Introduction The size of product portfolios is increasing rapidly and seemingly without limit as firms pursue narrower niches with more targeted products (Desai et al., 2001; Hoole, 2006). For every product removed from the portfolio, marketers add 1.8 new products (Hoole, 2006). As portfolios become more complex, firms encounter difficulties in achieving and maintaining excellent service performance (Closs et al., 2008). Several approaches to managing this complexity have been explored. Supply chain integration is one approach firms have embraced because integration facilitates a firm’s ability to respond to customers and to global market conditions. The efficient and effective linking of trading partners brought about through integration holds the

n

Corresponding author. Tel.: þ1 937 229 2204; fax: þ1 937 229 1030. E-mail addresses: [email protected] (C. Droge), [email protected] (S.K. Vickery), [email protected] (M.A. Jacobs). 1 Tel.: þ1 517 432 6405; fax: þ1 517 432 1112. 2 Tel.: þ1 517 432 6441; fax: þ1 517 432 1112. 0925-5273/$ - see front matter & 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2012.02.005

promise of significant improvements in service performance (Goffin, 2000; Kim, 2006). While the literature suggests that supply chain integration should impact performance (Frohlich and Westbrook, 2001; Iyer et al., 2004), the findings in the literature are inconsistent. Researchers have found no effects (Rodrigues et al., 2004), direct effects (Droge et al., 2004; Kim, 2009; Prajogo and Olhager, 2012), and fully mediated effects (Iyer et al., 2004; Stank et al., 2001; Vickery et al., 2003). The limited number of studies and their contradictory findings (see, e.g., Swink et al., 2007) have led some scholars to conclude that the verdict is still out as to whether supply chain integration actually has a positive impact on firm performance (Fabbe-Costes and Jahre, 2007, 2008). The apparent inconsistency of the findings and doubt about the integration–performance relationship suggests a missing variable. It is possible that the performance effects of supply chain integration must be evaluated in light of a firm’s product and/or market strategy (Narasimhan and Kim, 2002). This study seeks to empirically test this contention as well as extend the reasoning of Narasimhan and Kim (2002) to include process strategies as reasonable contexts for examining the performance

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

effects of supply chain integration. Our interest lies in determining whether upstream (supplier) integration or downstream (customer) integration fully or partially mediates the effects of product and process strategies on service performance. The specific product and process strategies selected for this study are product modularity and process modularity, respectively. Product and process modularity strategies are considered since both potentially affect service performance directly and indirectly (Fernandez and Kek¨ale, 2005; Gunasekarana and Ngai, 2005; Spring and Araujo, 2009). While no empirical studies have examined the effects of modularity on service performance (Gunasekarana and Ngai, 2005), there is significant anecdotal evidence suggesting a positive connection. For example, IBM leveraged its modular product architectures to add serviceability through ‘hot swapping’ and improved delivery performance using ‘vanilla boxes’ (Swaminathan and Tayur, 1998). JLA dominated the feed testing business in California’s central valley using modular processes to provide faster and more reliable delivery than competitors (McConneghey, 1999). Additionally, as product variety grows (Desai et al., 2001; Hoole, 2006), modularity may help mitigate the potentially negative effects on fill rate and timely availability because modularity implies component standardization and the ability to offer a large number of end products from a small number of modules (Worren et al., 2002). To investigate this topic, relevant literature pertaining to integration, product and process modularity strategies, and service performance is examined briefly. The research model and related hypotheses are presented. The model is then tested using canonical correlation to reveal macro-relationships among integration, product/process strategies, and service performance. This is followed by an effects decomposition as per Baron and Kenny (1986), which reveals the role of supply chain integration in the context of the product and process modularity strategies. Finally, the results are discussed with a view toward their theoretical, practical and managerial implications, and directions for future research are identified.

2. The research model: definition of key concepts The research model in Fig. 1 portrays that changes in performance can best be understood in the context of integration and product/process strategies. While motivated in part by an extension of Narasimhan and Kim (2002), the current research is also informed by Adaptive Structuration Theory (AST) (DeSanctis and Poole, 1994). AST suggests that structure influences socialization, which in turn influences outcomes. In the context of this research, product and process strategies are modeled to spur socialization

Indirect effects

+ Product/Process Strategy (1) Product Modularity (2) Process Modularity

External Integration (1) Supplier Integration (2) Customer Integration

+

Service Performance (1) Delivery Performance (2) Support Performance

+ Direct Effects Fig. 1. Research model of the relationships of modularity strategy, external integration, and service performance.

251

(external integration), which in turn influences service performance. Specifically, supply chain integration constitutes supplier and customer integration, product and process strategies are represented by product and process modularity, and service performance is comprised of support and delivery performance. We begin by providing definitions and supporting literature for each of the model constructs.

2.1. External integration with suppliers and customers 2.1.1. Supply chain integration upstream: supplier integration Supply chain integration is defined overall as a process of redefining and connecting entities through coordinating or sharing information and resources (Katunzi, 2011). Supplier integration suggests that suppliers are providing information and directly participating in decision making (Petersen et al., 2005). It is characterized by a cooperative relationship between the buyer and the upstream supplier. Often these relationships incorporate initiatives and programs that foster and strengthen the linkages between buyer and supplier (Vijayasarathy, 2010). Three key initiatives often touted in the literature are supplier development, just-in-time (JIT) purchasing, and supplier partnering (Dyer and Ouchi, 1993; Jaehne et al., 2009). Supplier development encompasses the policies, procedures, and practices for assessing and improving supplier capability and performance in multiple areas such as quality, design support, and delivery (Sytch and Gulati, 2008; Wagner and Krause, 2009). JIT purchasing facilitates integration through the use of highly coordinated deliveries from suppliers in support of an overall JIT strategy. Supplier partnering initiatives bring all the participants in the product life cycle into the product development process early so that suppliers and customers can provide input to each others’ processes (Petersen et al., 2003). Each of the three initiatives embodies the information exchange critically highlighted by Petersen et al. (2005).

2.1.2. Supply chain integration downstream: customer integration Customer integration encompasses the forward flow of goods and services and the backward flow of information from customer to supplier (Frohlich and Westbrook, 2001; Narasimhan and Carter, 1998). Customer integration involves directing attention and resources toward understanding how products and processes interact with the customer’s business and helping the customer become more competitive (Wisner et al., 2008). Thus customer integration entails engaging the customer in decisions about products sold by the firm (Pagh and Cooper, 1998; van Hoek et al., 1998) and encompasses methods and strategies that improve coordination between the firm and the customer (Frohlich and Westbrook, 2001). There are many ways to foster closer collaboration with customers, including closer customer relationships, lead time reduction, and product traceability. Closer customer relationships lead to improved information sharing about how a firm can precisely determine and satisfy customer requirements. Just in time (JIT) manufacturing systems exemplify sharing by providing a forum for communicating dynamic customer demand information. Furthermore, the small batch sizes associated with JIT require frequent communication and coordination (Schonberger, 1982). Another strategy fostering integration is lead-time reduction initiatives. These by their very nature enhance integration because they drive greater levels of responsiveness (Chin et al., 2004; Ismail et al., 2006; Talib et al., 2010). Lastly, product traceability facilitates operational integration across firm boundaries through tying source information about components to the

252

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

final product (Li and Warfield, 2011; Tse and Tan, 2011; Wang et al., 2009). 2.2. The product/process strategy: product versus process modularity strategies 2.2.1. Product modularity Modularity is a popular product strategy. Although little consensus on the definition of modularity has emerged (Gershenson et al., 2003; Ro et al., 2007; Salvador et al., 2002), there is agreement that modularity represents a hierarchically nested system (Sanchez and Mahoney, 1996; Simon, 1962). Product modularity has been characterized by decomposable systems (Sanchez and Mahoney, 1996; Simon, 1962), constructed of function oriented (Chang and Ward, 1995; Ulrich and Eppinger, 1995), standardized units (Ulrich, 1995; Walz, 1980). Standardization extends to both the use of common components, which are integral to modular architectures (Evans, 1963; Meyer and Lehnerd, 1997; Meyer et al., 1997), and to interfaces that create a ‘loose coupling’ enabling the production of a large number of end items from comparatively fewer inputs (Baldwin and Clark, 1997, 2000; Garud and Kumaraswamy, 1995; Sanchez, 1995; Schilling, 2000). Consistent with the literature (Jacobs et al., 2007), this research defines product modularity as the use of standardized and interchangeable architectural elements that enable the configuration of a wide variety of end products. This definition presupposes loose coupling, ease of disaggregation, heterogeneous outputs, and a oneto-one matching of function to module. 2.2.2. Process modularity The literature addressing process modularity is less well developed than that of product modularity but the systems perspective has been applied in this context too (Crawford and Rosenau, 1994; Erlicher and Massone, 2005; Lohse et al., 2004; Ulrich and Tung, 1991). Similar to product modules, process modules are characterized by standardized groups (Hoogeweegen et al., 1999) that have few strong organizational ties (Fine et al., 2005). This enables the decoupling and re-sequencing of processes and tooling with little loss in functionality because each process module functions as a relatively autonomous unit (Pine, 1993; Pine et al., 1993; Schilling and Steensma, 2001). To the degree that each production operation is independent of others, process modularity will increase (Feitzinger and Lee, 1997; Voordijk et al., 2006). A significant attribute of modular production systems is rapidly reconfigurable tooling. If each process module possesses a discreet set of functions (e.g., turning, milling, drilling), then the process module can be activated or de-activated quickly to keep pace with changes in demand (Erlicher and Massone, 2005). Flexible manufacturing systems, group technology, cellular manufacturing, and general purpose equipment facilitate rapid changeovers by engendering ease of reconfigurability in processes. Flexible manufacturing systems are those that can be changed readily in response to changes in product demand. This reconfigurability facilitates independence from prior or subsequent manufacturing processes and yields a wide range of items within a family grouping. At the system level, group technology facilitates production of component families that can be easily ‘plugged in’ (for a new product coming on line, for example) or just as easily ‘unplugged’. At the machine level, general purpose equipment (e.g., lathes) constitutes a processing function (or module) that can be readily incorporated into the sequence of operations for any given product. Consistent with these principles and prior literature (Jacobs et al., 2011) the following definition of process modularity is adopted: the incorporation of adaptable and reconfigurable tooling and routings into production operations to effectively meet heterogeneous demand.

2.3. Service performance: support and delivery Competitive performance is a multidimensional construct (Cleveland et al., 1989; Hayes and Wheelwright, 1984; Skinner, 1974; Swamidass and Newell, 1987; Vickery et al., 1994). Service is one dimension of overall performance, and this research focuses on two key aspects of service performance: support and delivery performance. Support performance is itself multifaceted, including aspects of before and after sale interactions that are related to the human factor, products, and/or processes (Aaltonen et al., 2010; Kanovska, 2010; Kotler, 1986). To tap this multidimensional concept, we study responsiveness, after sales product support, and before sales assistance. Delivery performance in our study is captured by speed, flexibility, and dependability; these three facets capture, at least in part, the multidimensionality of delivery performance (Guiffrida, 2009; Iyer et al., 2004; Kallio et al., 2000).

3. The research model: hypotheses The research model in Fig. 1 portrays external supply chain integration as a full or partial mediator of the strategy– performance relationship. In the following sections, we develop research hypotheses consistent with this perspective. 3.1. Product strategy (product modularity) and service performance The literature associates product modularity strategies with service performance, but the evidence to support this claim does not come from large scale empirical studies (Gunasekarana and Ngai, 2005). Nevertheless, the logical arguments for a positive relationship between product modularity and service performance are persuasive and are presented below. The standardization associated with modular product architectures leads to the ability to pool components and subassemblies, which in turn leads to improved inventory fill rates (Baker et al., 1986; Collier, 1981, 1982; Gerchak et al., 1988; McClain et al., 1984). For example, the flexibility of work-in-process inventory is increased (Lee and Tang, 1997) when a common pool of strategically distributed stock replaces multiple instances of safety stock (Evans, 1963; Meyer and Lehnerd, 1997; Meyer et al., 1997). The use of standardized and common components allows various demand streams to be pooled, resulting in improved forecast accuracy and higher levels of stock relative to demand variance; in turn, this results in improved inventory fill rates system wide (Lee and Tang, 1997). Modular product architectures enable postponement strategies since modules can be produced in parallel (Lorenzi and Lello, 2001; Novak and Eppinger, 2001; van Hoek et al., 1998; van Hoek and Weken, 1998); this facilitates capturing potential benefits (Worren et al., 2002). For example, differentiation can be deferred by moving modules to a variety of geographic regions and then assembling them to order; the result is a wide variety of products with very responsive delivery times (Cholette, 2009; Fine et al., 2002; Lorenzi and Lello, 2001; Novak and Eppinger, 2001; van Hoek et al., 1998; van Hoek and Weken, 1998). In addition to physical inventory implications, modular architectures impact operations. Since modular architectures facilitate the creation of a wide range of products from a limited number of inputs, modular architectures enable a more effective servicing of seasonal demand by consolidating demand for various configurations into a standard set of modules. This has a variety of implications for the manufacturing infrastructure (Ulrich, 1995), including a more balanced and stable workforce and production volume (with less variability).

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

A key element of support performance is the interaction with the customer prior to purchasing a product. Modular product architectures maximize the value of this interaction by allowing the customer to precisely tailor the product to his/her needs. Offering the customer the ability to tailor the product is a form of responsiveness in that the company provides an exactly tailored product. This ability to customize on a mass scale – i.e., mass customization – has been associated with a variety of positive outcomes (Carey, 1997; Danese and Romano, 2004; Feitzinger and Lee, 1997; Jacobs et al., 2011; Pine et al., 1993; Worren et al., 2002). Another element of support performance is the ability to service products after the customer has taken delivery. The standardized interfaces and accompanying ease of assembly/ disassembly of modular architectures enable firms to support products with high levels of post sale service. For example, repair time is minimized by the use of plug and play modules. In the case of computers, drives can be removed and replaced without powering down (hot swapping). The redundancy of drives coupled with hot swapping enhances the reliability and total up time of the system. The plug and play aspect of the architecture allows customers with limited expertise to easily service their machines. This results in maximized product availability with minimal cost to the customer. Furthermore, modular architectures may facilitate the reverse logistics associated with repair ¨ (Fernnandez and Kekale, 2005), since only the offending portion of the product needs to be returned. The formal structure of a firm will tend to align with the structure of the products produced (Baldwin and Clark, 2000; Chesbrough and Teece, 1996; Henderson and Clark, 1990; Sanchez and Mahoney, 1996; von Hippel, 1990). This is an application of contingency theory, Conway’s law, and sociotechnical theory (Avgerou et al., 2004; Cherns, 1987; Cherns, 1976; Conway, 1968; Drazin and van de Ven, 1985; Lawrence and Lorsch, 1967) and is consistent with AST. Given the substitutability of modules in a modular product, this would tend to push a firm toward a horizontal integration strategy (Garud and Kumaraswamy, 1995). Hence this theoretical perspective suggests that the production of modules will be outsourced. This has direct implications for service performance in that horizontally integrated firms can make changes more quickly than vertically integrated firms (Mac Neil, 1980). As such, volume changes may be more readily accommodated, or additional features/ technologies may be integrated into modules more rapidly. Subcontracting may also capture scale economies for the module that lead to improved pricing and quality (Wright, 1936). Collectively these benefits represent improved responsiveness to customers’ product support and delivery requirements. Taken together, modular product architectures enable the product delivery system to address the specific needs of customers. The result is a smoothing of flow through the production system and improved utilization, both of which should lead to better performance (Gorman and Brannon, 2000; Schmenner and Swink, 1998). Thus the literature suggests that product modularity impacts the speed of delivery, changes in delivery requirements, and the ability to consistently support customer requirements. This leads to the following direct effects hypothesis: H1. Product modularity is positively associated with (a) support performance and (b) delivery performance.

3.2. Process strategy (process modularity) and service performance Process modularity enhances responsiveness through cycle time reduction (Lorenzi and Lello, 2001), which is a component

253

of delivery performance (Holweg and Pil, 2005). Shafer and Charnes (1993) model cycle time reductions for modular processes through simulation. Cycle time reductions result from manufacturing portions of products in parallel and then assembling them quickly based on order requirements (Novak and Eppinger, 2001; van Hoek and Weken, 1998). Additionally, modular process designs have been shown to engender agility (Narasimhan et al., 2006). Agility is the ability to rapidly change operating states in response to uncertain customer demand (Fliedner and Vokurka, 1997; Kasarda and Rondinelli, 1998; Nagel and Bhargava, 1994); this includes the capability to respond to unforeseen changes to demand (Brown and Bessant, 2003; Prince and Kay, 2003; Sharifi and Zhang, 2001). The literature has associated agility with customer responsiveness, minimization of manufacturing lead time, and delivery speed (Jacobs et al., 2011); these outcomes are consistent with the measures of delivery performance used in this study. Process modularity facilitates offering wide ranges in production volume and product mix through reconfigurable equipment (Ward, 1994). Rather than simply anticipating a defined range of requirements, the ability to deconstruct and reconstruct the system as needed is what leads to agility (Noaker, 1994). Hence the use of modular manufacturing processes (based upon a cellular design incorporating reconfigurable tooling) is a strategy to meet the range of required customer responses in a timely manner (Sharifi and Zhang, 2001; Worren et al., 2002). Modular processes may also yield greater flexibility by isolating volume changes to certain portions of the process (Lorenzi and Lello, 2001). Lohse et al. (2005) state that the crucial factor for the success of modular production systems is the availability of rapidly reconfigurable tooling since the performance of the system depends on tooling selection and the allocation of manufacturing tasks to workstations. Since each process module possesses a discrete function (e.g., turning, milling, drilling), the process module can be activated or de-activated quickly to keep pace with changes in demand (Erlicher and Massone, 2005). Modular processes may also facilitate before sales activities. For example, modular processes may enable more rapid vendor qualifications since the buyer can focus attention on only a portion of the supplier’s production processes. Additionally, since modular processes reduce cycle time, sample products for product testing or sales support can be produced more quickly. The result is a faster response and better support for customers throughout the purchase decision process. Thus, the second direct effects hypothesis is: H2. Process modularity is positively associated with (a) support performance and (b) delivery performance. 3.3. External integration as a mediator of the effects of modularity on service performance AST suggests that structure influences socialization, which in turn influences outcomes. Hence there is theoretical rationale for our hypotheses that product and process strategies act upon supply chain integration, which in turn influences service performance. The literature suggests that both product and process modularity strategies directly affect performance, as in H1 and H2 (Gentry and Elms, 2009; Susarla et al., 2009); but modularity’s effects may actually be indirect through supply chain integration, or perhaps both direct and indirect. The indirect effects could be attributable to improved communication since modularity helps break down barriers between functions (Lorenzi and Lello, 2001; Sanchez and Mahoney, 1996). Communication is facilitated by the standardized interface, which becomes the coordinating

254

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

mechanism within and beyond firm boundaries (Galvin and Morkel, 2001; Sanchez and Mahoney, 1996). Given that communication and coordination are hallmarks of integration and that Frohlich and Westbrook (2001) found increased integration to be associated with improved performance, the literature suggests that modularity may lead to improved service performance indirectly through supply chain integration.

3.3.1. Supplier integration as mediator Product modularity positively influences upstream integration with suppliers in a number of ways. The first is in building a cooperative relationship by increasing the level of trust (Kim and Chhajed, 2000; van Hoek and Weken, 1998), possibly through the improved forecasts facilitated by pooling effects (Lee and Tang, 1997). Product modularity also enhances supplier integration by reducing communication barriers (Lorenzi and Lello, 2001; Sanchez and Mahoney, 1996), resulting in the ability to communicate more frequently, clearly, and with less effort (Galvin and Morkel, 2001). An example of this concerns intellectual property (IP) rights. Protecting IP is a growing concern among manufacturers that risk losing competitive advantage through the inadvertent release of confidential information. This concern works to keep buyers and suppliers at arms length. However, product modularity helps buyers and suppliers collaborate by managing these risks during the product development stage (van Hoek and Weken, 1998). Information sharing can be contained to that pertinent to the interface, thus sharing signals and geometries without completely revealing the final product into which the supplier’s module will be incorporated. Process modularity encourages supplier integration through focused collaboration. For example, when the partners discuss process improvements, the feedback pertains to a discreet portion of the production process, thus affording more targeted and easily understood feedback. Furthermore, because of the loose coupling between process steps, process changes can be made with fewer adverse impacts on other parts of the production process (Mikkola, 2006). This is in part a function of using tooling that is adaptable and reconfigurable. Several studies have shown that integration has a positive impact on performance in a variety of settings and using a variety of research methods (Lin et al., 2010; Paiva, 2010). For example, Sohal et al. (2001) report that, although a variety of other factors contribute, manufacturing integration techniques resulted in overall improvements for the study firm. In a case study of a small manufacturer, integration techniques were found to reduce costs (Collett and Spicer, 1995) and analysis of survey data reveals that information integration and relationship building are associated with improved delivery and firm performance (Jayaram and Tan, 2010). Additionally, upstream integration was shown to have a positive effect on performance in the product development context (Petersen et al., 2005). In a larger context, Carr and Pearson (1999) demonstrated the positive effect of supplier partnering on firm performance, and Landeros and Monczka (1989) found that supplier relationship management, which is an important aspect of a cooperative relationship, influences performance with respect to cost, quality, and cycle time. Furthermore, anecdotal evidence supporting a relationship between supplier integration and performance has been provided by several firms (Ragatz and Sandor, 2009). Examples include (Lewis, 1995): (1) Motorola employing supplier integration strategies to achieve supply cost reductions twice that of its competition and (2) Marks and Spencer teaming with suppliers to increase innovation and decrease cost and cycle time. Thus the literature suggests that modularity strategies influence supplier integration, which in turn impacts supplier integration. Thus we

propose the following hypotheses related to demonstrating indirect effects: H3a,b. (a) Product modularity and (b) process modularity are positively associated with supplier integration. H3c,d. Supplier integration is positively associated with (c) support performance and (d) delivery performance. H3e–h. Supplier integration will mediate the relationship between (e) product modularity and support performance, (f) product modularity and delivery performance, (g) process modularity and support performance, (h) process modularity and delivery performance. 3.3.2. Customer integration as a mediator Modular product architectures positively influence customer integration through the loose coupling property that facilitates lead time reductions (Novak and Eppinger, 2001). Loose coupling and standardization enable postponement strategies that both shorten lead times and facilitate pull-based production (Pine et al., 1993; Worren et al., 2002). Note that shorter cycle times and pullbased production require greater levels of communication and coordination between firms and their customers (Schonberger, 1982). Modular product architectures facilitate increased communication and interaction with customers (Sanchez and Mahoney, 1996). Since product modularity enables a firm to offer highly configurable products (Novak and Eppinger, 2001), it encourages closer customer relationships and facilitates a better understanding of the customer’s needs. Hence modular product architectures promote a customer focus, which is indicative of a partnership mentality (Koufteros et al., 2005). Process modularity facilitates the pooling of customer demand since a wide range of end items reduces to a smaller set of production modules. The result is improved mix and volume flexibility in conjunction with reduced manufacturing/delivery lead time (Worren et al., 2002). This occurs through aggregating demand variability to the modules, which mutes production variability (Jacobs and Swink, 2011). However, the coordination and information exchanges characteristic of customer integration must be ongoing. Fortunately, modularity facilitates information exchanges thereby making coordination easier (Sanchez and Mahoney, 1996). Lastly, there is evidence that customer integration has a positive effect on delivery performance. For example, Koufteros et al. (2010) find that customer integration reduces glitches, and by inference improves on-time delivery. Flynn et al. (2010) found that customer integration relates to improved responsiveness to customer needs and Chang (2009), investigating the services sector, found that customer integration positively correlates with delivery performance and reductions in lead time. Hence the literature and logical arguments suggest that modularity impacts customer integration and that customer integration impacts service performance. Thus our next set of hypotheses, concerning indirect effects, is: H4a,b. (a) Product modularity and (b) process modularity are positively associated with customer integration. H4c,d. Customer integration is positively associated (c) support performance and (d) delivery performance.

with

H4e–h. Customer integration will mediate the relationship between (e) product modularity and support performance, (f) product modularity and delivery performance, (g) process modularity and support performance, (h) process modularity and delivery performance.

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

4. Sampling and measurement 4.1. Sampling This study focused on tier one suppliers to automotive manufacturers in North America. The population frame consisted of the top 150 tier one suppliers in terms of annual sales as identified by industry experts from the Automotive Industry Action Group (AIAG), a professional association with over 1000 members. The research questionnaire, accompanied by an informational letter, was mailed to CEOs. The letter stated the purpose of the project and that a member of the research team would be calling soon. CEOs of strategic business units (SBUs) or individual firms were instructed to complete the survey for their SBU or firm. CEOs of multiple business units were instructed to select one of their SBUs to participate in the study and to forward the research questionnaire to the CEO of that unit. Telephone calls were made to obtain definitive responses from CEOs regarding their intent to participate. Respondents provided their titles and contact addresses for the purpose of receiving the summary results; most were members of the top management team (CEO, VP, Director, etc.). Respondents were contacted to complete missing values or illegible responses. The final sample consisted of 57 firms, making the response rate approximately 39 percent. The mean annual sales volume was $501 million (s ¼$637 million). The mean number of employees was 2810 (s ¼3431). Using Dun & Bradstreet data, these means were compared using a standard t-test to 71 non-responding companies and no statistically significant difference was detected. To assess the inter-rater reliability, a second questionnaire went to the original respondents; they were asked to have another knowledgeable individual complete the abbreviated questionnaire. Subsequent analysis found concordance across respondents. Finally, common method bias is a concern when surveys are used because respondents may be predisposed to answer questions in a thematic manner (Campbell and Fiske, 1959), resulting in confounding of variables. To evaluate whether this threat to validity, we followed the standard method (Podsakoff et al., 2003). Upon employing an exploratory factor analysis (EFA), multiple factors emerge. Furthermore the maximum variance explained by any one factor of that EFA is 18%. Hence common method bias is not a concern. 4.2. Measurement A panel of experts from AIAG assisted in ensuring completeness and clarity of all items. To engender a common understanding, the survey instrument included definitions of all items. Finally, the expert panel assisted in the pilot testing of the instrument. The constructs, items and their definitions, construct reliability, and the item-to-total correlations are in Table 1. All measurement items are conceptually grounded in the literature as discussed previously and are drawn from published research projects (Jacobs et al., 2011; Jacobs et al., 2007). The measures for product modularity, process modularity, supplier integration, and customer integration are ‘‘extent of use’’ scales where respondents rate ‘‘the degree to which the following initiatives were utilized by your SBU’’ on 7-point scales with endpoints ‘‘Extremely Low’’ (¼1) and ‘‘Extremely High Use of Item’’ (¼7). For service performance, respondents were asked to rate various aspects of performance relative to major competitors on 7-point scales, where 1¼ ‘‘Poor’’ and 7¼‘‘Excellent.’’ Although these measures are subjective, similar measures of performance are widely used and show high convergence with objective measures (Hart and Banbury, 1994; Powell and Dent-Micallef, 1997; Venkatraman and Ramanujam, 1987). Subjective measures can improve response rates

255

since often they alleviate responder reluctance to reveal performance data due to confidentiality policies; for this reason, subjective measures can be preferable to objective measures. The scales for each construct were internally consistent and reliable, with construct reliabilities ranging from 0.60 to 0.85 (Table 1). Item-to-total correlations were all significant at 0.01 and with two exceptions (product traceability 0.58 and general purpose equipment 0.46) were greater than 0.69 and thus no item was eliminated (Churchill, 1979). To further validate the factors, we performed a confirmatory factor analysis (CFA) using EQS 6.1. The CFA revealed a comparative fit index of 0.916 and a root mean square error of approximation of 0.058. Item loadings on each factor were all significant at 0.05 or better, with the exception of product traceability (a measure of customer integration) which was significant at 0.06. No significant cross loadings were identified.

5. Results 5.1. Canonical correlation and macro-level regression results for the model The global relationships among the sets of variables comprising our research model (product/process modularity strategies, supply chain integration, and service performance) were tested first using canonical correlation. Canonical correlation creates orthogonal linear composites of variables called canonical functions (Montabon et al., 2007). These composites represent ‘‘many to many’’ relationships between variables, each of which has a canonical loading (or weight). The results, found in Table 2, show that the independent variables (product modularity, process modularity, supplier integration, and customer integration) explain a significant amount of variation in support and delivery performance (average R2 ¼23.6%). The first canonical function is significant (p o0.01), with canonical correlation of 0.519. The second function is unlikely to have much practical significance since its canonical correlation is non-significant at p ¼0.167, with a low redundancy index of 0.190 (Hair et al., 2009). Since canonical functions are optimized for prediction, not interpretation, the meaning of relationships can be difficult to interpret (Hair et al., 2009). This is attributable to canonical correlation’s property of relating linear composites rather than variables (Israels, 1986; Stewart and Love, 1968). While the canonical correlation analysis revealed that the variable sets were significantly related and hence indicative that product/process strategies and integration are predictive of service performance, we proceeded with additional analyses to determine the exact nature of these relationships. We employed a two-stage decomposition procedure which focused first on four macro-linear regression analyses (reported in Table 3) and then on a microlinear regression analyses (reported in Fig. 2). The results of the macro-level regression analyses in Table 3 show the role of product modularity as a significant, positive predictor of both dimensions of service performance in all four models. Process modularity was negatively related to support performance and unrelated to delivery performance. Next, supplier integration had a marginally significant (po0.10) positive impact on delivery performance only, while customer integration positively affected both support and delivery performance (po0.05). The additional series of micro-linear regression analyses (as presented in Fig. 2) follow the recommendations for effects decomposition as per Baron and Kenny (1986). These sets of analyses demonstrate the decomposition of linear composites to reveal the relationships between specific criterion and predictor variables. The results are discussed in the next sections.

256

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

Table 1 Measurement: constructs and their measures. Construct/measures

rn

Definition

Delivery performance (CR¼ 0.88) Delivery speed

0.84

The ability to reduce the time between the receipt of a customer order and final delivery to as close to zero as possible The ability to effectively respond to changes in planned delivery dates The ability to consistently deliver on the promised due date

Delivery flexibility Delivery dependability

0.84 0.85

Support performance (CR ¼0.85)nn Product support

0.80

Responsive to customers

0.86

Pre-Sale customer service

0.78

Supplier integration (CR ¼0.71) Supplier development

0.74

JIT purchasing Supplier partnering

0.79 0.84

Customer integration (CR¼ 0.66) Closer customer relationships JIT manufacturing

0.69 0.69

Manufacturing lead time reduction Product traceability

0.78

Product modularity (CR ¼ 0.60) Modularity Standardization

Determining customer’s requirements, then meeting those requirements no matter what it takes A philosophy of eliminating wastes, characterized by small lots and reduced set-up times, in which components and products are pulled, as required, by the manufacturing system A conscious effort to reduce total manufacturing lead time Maintaining and using accurate records for lot tracking and material control, including finished goods, work in process, purchased parts and raw materials

0.86

The process of developing interchangeable parts across products that can be configured into a wide variety of end products The use of standard procedures, materials, parts, and or processes for designing and manufacturing a product

0.70

Group technology

0.83

Flexible mfg systems

0.72

General purpose equipment

0.46

n

Policies, procedures, and practices for assessing and improving supplier capability and performance in multiple areas such as quality, design support, and delivery Requiring JIT deliveries from your suppliers to support your overall JIT strategy Bringing all of the participants in the product life cycle into the process early on so even suppliers and customers can provide input to each others’ processes

0.58

0.89

Process modularity (CR ¼0.61) Cellular manufacturing

The ability to service the customer in providing product support after the sale of the product to ensure continuing customer satisfaction The ability to respond in a timely manner to the needs and wants of the company’s customers including potential customers The ability to service the customer during the purchase decision process

A manufacturing process that produces families of parts within a single line or cell of machines operated by machinists who work only within that line or cell The grouping of products that have similar design properties or manufacturing characteristics into product families to simplify design and manufacturing A computer controlled manufacturing system using semi-independent NC/CNC machines linked together by means of an automated material handling network The use of general purpose machines, tools, or transporting devices in the manufacturing operation

r ¼Item to total correlation, all correlations significant at 0.01. CR ¼Composite reliability.

nn

Table 2 Results of canonical correlation analysis.

Independent variables Supplier integration Customer integration Product modularity Process modularity Redundancy indices Dependent variables Support performance Delivery performance Redundancy indices Canonical correlation Average variance explained

Canonical function 1

Canonical function 2

0.083 0.555 0.794  0.527 0.393

0.329 0.030  0.503 1.030 0.332

0.525 0.586 0.810 0.519 (p ¼0.007)

 1.163 1.133 0.190 0.306 (p¼ 0.167)

23.6% for the canonical model

5.2. Results for product modularity, supply chain integration, and service performance To examine the relationship between product modularity and support performance (path C Fig. 2), support performance was regressed on product modularity and a significant relationship

(0.36, po0.01) was found. Integration strategy was then regressed on product modularity (path A Fig. 2) and significant relationships were found for both supplier and customer integration (0.415 and 0.516 respectively, p o0.01). Thus, product modularity has direct impacts on integration strategy and on support performance. Support performance was next regressed on both integration strategy constructs and product modularity (paths B and D, respectively, in Fig. 2) to test for the presence of indirect relationships. Path B was not significant for either integration strategy, indicating the impact of product modularity on support performance is strictly direct. To investigate the relationship between product modularity and delivery performance, delivery performance was regressed on product modularity, revealing a significant relationship (0.42, po0.01). Together with the results from regressing the integration strategies on modularity, these findings indicate that product modularity directly affects supplier integration, customer integration, and delivery performance. To ascertain the presence of an indirect relationship, delivery performance was next regressed on each integration strategy and on product modularity. For customer integration, path B was significant (0.261, po0.05), but path B for supplier integration was not significant. Thus, customer integration partially mediates the effects of product modularity on delivery performance.

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

257

Table 3 Results for macro-level analyses of four regression models. Product modularity,

Process modularity,

Supplier integration,

Customer integration,

b

b

b

b

Support performance (dep. var.)

0.489nnn 0.436nn 0.367nnn 0.326nn

 0.312nn  0.388nnn  0.040  0.119

0.084 0.248nn

X X

0.178

n

Delivery performance (dep. var.)

X X

0.304nn

R2

0.199 0.233 0.202 0.236

n

Significant at 0.10. Significant at 0.05. nnn Significant at 0.01. nn

Indirect effects: H3a-h & H4a-f

External Integration • Supplier • Customer

A

B Service Performance

Product / Process Strategy

C/D

• Product Modularity • Process Modularity

• Support • Delivery

Direct Effects: H1a-b & H2a-b

Fig. 2. Results of mediation analyses.

5.3. Results for process modularity, supply chain integration, and service performance First, support performance was regressed on process modularity. This relationship was not significant, indicating that there is no direct relationship between process modularity and support performance. Next, supplier integration, and in turn, customer integration were regressed on process modularity, revealing positive and significant betas (0.345 and 0.541, respectively, p o0.01). The subsequent regression of support performance on each integration strategy and on process modularity produced a positive beta for customer integration (0.394, p o0.01) and a nonsignificant result for supplier integration. Thus customer integration fully mediates the relationship between process modularity and support performance. Finally, delivery performance was regressed on process modularity, revealing a significant relationship (0.212, p o0.10).

Regressing delivery performance on integration strategy and process modularity yielded a significant beta for path B (p o0.01) and non-significance for path D. Thus, the impacts of process modularity on delivery performance are indirect through customer and supplier integration; i.e., both supplier and customer integration fully mediate the effects of process modularity on delivery performance. 5.4. Summary of the results by research hypothesis A summary of the results, reorganized in the context of the research hypotheses, is presented in Table 4. H1a–b and H2a–b concern the direct effects of product modularity and process modularity on support performance. H3a–b and H4a–b focus on the relationships of product modularity and process modularity, respectively, to supplier integration and customer integration. H3c–d and H4c–d delineate the linkages between supplier

258

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

Table 4 Conclusions of the hypothesis testing. Hypothesis Product modularity and service performance H1a Product modularity will have a positive effect on support performance H1b Product modularity will have a positive effect on delivery performance Process modularity and service performance H2a Process modularity will have a positive effect on support performance H2b Process modularity will have a positive effect on delivery performance Direct/indirect effects of supplier integration H3a Product modularity will have a positive effect on supplier integration H3b Process modularity will have a positive effect on supplier integration H3c Supplier integration will have a positive effect on support performance H3d Supplier integration will have a positive effect on delivery performance H3e Supplier integration will mediate the relationship between product modularity and support performance H3f Supplier integration will mediate the relationship between product modularity and delivery performance H3g Supplier integration will mediate the relationship between process modularity and support performance H3h Supplier integration will mediate the relationship between process modularity and delivery performance Direct/indirect effects of customer integration H4a Product modularity will have a positive effect on customer integration H4b Process modularity will have a positive effect on customer integration H4c Customer integration will have a positive effect on support performance H4d Customer integration will have a positive effect on delivery performance H4e Customer integration will mediate the relationship between product modularity and support performance H4f Customer integration will mediate the relationship between product modularity and delivery performance H4g Customer integration will mediate the relationship between process modularity and support performance H4h Customer integration will mediate the relationship between process modularity and delivery performance

Result

Supported

delivery performance (see Fig. 2). The results also showed that customer integration fully mediates the effects of process modularity on both support and delivery performance, and partially mediates the effects of product modularity on delivery performance. However, customer integration does not mediate the effects of product modularity on service performance.

Supported

6. Discussion Not supported Supported (microanalysis only) Supported Supported Not supported Supported Not supported Not supported Not supported Supported

Supported Supported Supported Supported Not supported Supported Supported Supported

integration and customer integration, respectively, with service performance. Finally, H3e–h and H4e–h concern the potential roles of supplier integration and customer integration, respectively, as mediators. H1a–b were strongly supported by the results of the canonical correlation and both the macro- and micro-linear regression analyses (see Tables 2 and 3, and Fig. 2). Product modularity was shown to be a highly significant, positive predictor of service performance. H2b was supported by the pertinent micro-linear regression (see Fig. 2). H3a–b and H4a–b were strongly supported by the only analyses relevant to these hypotheses – the microlinear regressions (see Path A in Fig. 2). H3c–d and H4c–d were concerned with the effects of supplier integration and customer integration, respectively, on support and delivery performance. There was no support for H3c and limited support for H3d concerning supplier integration. In contrast, there was strong support for positive effects of customer integration on both support and delivery performance (H4c–d). Finally, H3e–f and H4e–f concerned mediation by supplier integration and customer integration, respectively. Supplier integration fully mediated the effects of process modularity on

6.1. The direct antecedents of service performance Two service performance constructs were considered: support and delivery performance. The first major finding is that modularity impacts service performance, since the following direct effects were positive: (1) product modularity-support performance; (2) product modularity-delivery performance; and (3) process modularity-delivery performance. Of these three, the effect of process modularity on delivery was the weakest; process modularity had no direct effect on support performance. This set of results indicates conclusively that product modularity has a pervasive direct impact on both aspects of service performance, while the impact of process modularity is more limited. The results also show that improvements to service performance accrue from integration strategies, since the following were positive: (1) supplier integration-delivery performance; (2) customer integration-support performance; and (3) customer integration-delivery performance. The results for customer integration were particularly robust, which is consistent with prior studies examining the effects of supplier versus customer integration on performance (see, e.g., Stank et al., 2001; Gimenez and Ventura, 2005). However, managers and researchers should be cautious about assuming that supply chain integration is axiomatically beneficial since the effects of integration and the effects of modularity are interrelated in complex ways. For example, both product modularity and process modularity are positively related to customer integration and supplier integration. In this regard, the findings support the contention set forth at the outset of this article that the effects of supply chain integration must be evaluated in light of a firm’s product and/or market strategies; in particular, our extension into process strategies is supported. This insight may be the key to understanding the contradictory findings characterizing recent research investigating the performance effects of supply chain integration. The contradictions could be due to the omission of model constructs capturing the firm’s product, market, and/or process strategies. The linkages from product and process modularity to customer and supplier integration were highly significant and hence provide empirical support to arguments that modular architectures can reduce barriers to collaboration (Galvin and Morkel, 2001; Sanchez and Mahoney, 1996). The results of the analyses imply that modularity facilitates boundary spanning initiatives (such as customer and supplier integration).

6.2. Effects decomposition For the four possible effects of product/process strategy on service performance, the results show that customer integration functions as a mediator, either fully or partially, in three cases. In contrast, supplier integration mediates in only one of the four cases. Thus, it appears that customer integration is more important than supplier integration, at least in the context of product and market strategies concerned with modularity. The findings suggest that with modular products and processes, each node of the supply chain should focus integration efforts on downstream members to realize service performance benefits.

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

Customer integration partially mediates the effect of product modularity on delivery performance. This means that product modularity has a two part impact: direct on delivery performance, as well as indirect through customer integration on delivery performance. Thus while part of the impact of product modularity depends on and is enhanced by customer integration, it is still possible to receive benefit from product modularity absent customer integration. In contrast, customer integration fully mediates the relationship between process modularity and both dimensions of service performance. This means that the impact of process modularity operates indirectly through customer integration: absent customer integration, process modularity will have little or no impact on service performance. Supplier integration fully mediates the effect of process modularity on delivery performance (and has no effect on support performance). This means that process modularity’s impact on delivery performance is indirect and cannot be realized absent some degree of supplier integration. Hence firms embracing production designs relying upon flexible manufacturing systems, group technology, and general purpose equipment must integrate with suppliers to recognize delivery improvements. Taken together with the finding presented above for process modularity and customer integration, supply chain integration is critical to realizing the benefits from process modularity strategies. 6.3. Managerial implications The results suggest managers without the flexibility or financial resources to invest in supply chain integration can reap service performance improvements from investments in modular product architectures. There are two reasons for this. First, product modularity stimulates integration. Second, since product modularity has a significant total effect on support and delivery performance, investments in modular product architectures will have some service performance benefit—either direct, indirect, or both. Of the two supply chain integration types investigated in this research, the analyses reveal greater benefit from downstream integration with customers. Hence before supplier integration initiatives are pursued, managers should invest in customer integration. Integration technology that captures point of sale data, inventory levels, and shipping status are all examples. Capturing and sharing customer centric data allows managers to respond proactively to demand swings, and sharing shipping/ inventory status enables more effective business management by customers. This research also provides some insight to capital allocations. In particular, when managers invest in modular products and processes, they should accompany those investments with investments in downstream integration. This is particularly true in the case of process modularity investments since downstream integration fully mediates the relationship with the dimensions of service performance. Investments in upstream integration will generate lesser benefit, but are requisite in the context of process modularity. In particular, only the delivery performance dimension of service will be improved when investments in flexible manufacturing systems, group technology, and general purpose equipment are coupled with upstream integration efforts. Note that since upstream integration fully mediates the link between process modularity and delivery performance, managers must invest in upstream integration to recognize any delivery performance benefit from process modularity. 6.4. Limitations The research is not without limitations. One such limitation is the sample size. Smaller sample sizes can mean relatively large

259

standard errors, which can skew test statistics and cause rejection of valid hypotheses. However, finding significant relationships in small samples is an indication that the finding is particularly robust (Cohen et al., 2003). Another limitation is that the data are from a single industry. Single industry studies have both advantages and disadvantages: confounding due to industry differences is reduced, but generalizability of the results may also be reduced. In this instance, the tier one suppliers are all in the automotive industry. However, they are highly diverse, providing electronic, machined metal, and other types of components requiring varying degrees of assembly, and thus the results may be more generalizable than those from most single industry studies. Nevertheless, assuming that the results hold in other contexts should be avoided. 6.5. Contributions This research makes several contributions. First it provides empirical validation of Narasimhan and Kim’s (2002) contention that supply chain integration must be evaluated in the context of a firm’s product strategy. We furthered this line of thinking to incorporate process strategy and establish that the process dimension must also be considered. This study helps to establish the usefulness of AST as a theoretical perspective that can inform supply chain research. By establishing a positive relationship between integration and performance, we have empirically corroborated other research (such as Droge et al., 2004; Frohlich and Westbrook, 2001; Iyer et al., 2004; Jacobs et al., 2007; Stank et al., 2001; Vickery et al., 2003). However, we have added to the literature in considering the specific dimensions of service performance. As a result of the measures of product and process strategy, we developed additional insights relating to modularity. In particular, this research established a positive link between product and process modularity and delivery performance, as well as between product modularity and service performance. The research also reveals the strong role of modularity strategies in engendering integration up and down the supply chain. 6.6. Summary and conclusion Product modularity’s impacts on service performance are primarily direct since three out of four potential mediation routes through integration constructs were non-significant while the direct paths were significant; but process modularity’s impacts are primarily indirect since three out of four potential mediation routes were significant. Hence product modularity does not appear to require integration strategies to the same extent as does process modularity. Since the coordination benefits of modular product architectures are associated with the interface, this sparks the question as to whether modular processes lack intrinsic interfaces. It is possible that integration potentially fulfills the role of interface in modular processes. If integration serves as the interface, then a logical question is whether there are standard types of interfaces that parallel those identified in products (e.g., slot, bus, sectional, and plug; Ulrich, 1995). Furthermore, if there are standard types of integration-interfaces, does the setting in which they are applied matter? In other words, does using integration type 1 in situation A lead to better performance than integration type 2 in the same setting? These research possibilities are rich for exploration. In regards to integration, it seems that downstream (customer) integration is far more important than upstream integration. This is particularly true in the case of process modularity as antecedent, or delivery performance as consequence. Future research should seek to determine why this is the case. Does each node’s

260

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

focus on the customer supersede the need to focus on suppliers? In other words, does customer integration make supplier integration redundant? The effects of modular product/process strategies and supply chain integration more extensively impact delivery performance than support performance since there are five ways (or paths) to improve delivery performance but only two to improve support performance. Hence the range of managerial options for improving delivery performance is greater. Note that these may not be the only routes to enhanced service performance. We examined only support and delivery performance (there are certainly many other dimensions), and we did not study internal integration or other organizational dimensions. We leave these topics to future research. More generally, this research provided a limited test of AST. There are aspects where the mediating relationship discovered is consistent with AST, but others (e.g., direct effects and partial mediation) which are inconsistent with the current presentation of AST. This research thus suggests a refinement of AST incorporating indirect effects may be worth considering. Lastly, this study confirms and extends the work of Narasimhan and Kim (2002), and by doing so demonstrates the usefulness of AST for supply chain research. Hence opportunities for additional research into indirect effects are available, as is the opportunity to more tightly link the information systems and supply chain literatures through the use of AST. References Aaltonen, P.G., Markowski, E.P., Kirchner, T.A., 2010. Ingredients of financial services customer satisfaction: the case of credit card services. Journal of the Academy of Business and Economics 10 (3), 149–156. Avgerou, C., Ciborra, C., Land, F., 2004. The Social Study of Information and Communication Technology: innovation, Actors and Contexts. Oxford University Press, Oxford ; New York. Baker, K.R., Magazine, M.J., Nuttle, H.L.W., 1986. The effect of commonality on safety stock in a simple inventory model. Management Science 32 (8), 982–988. Baldwin, C.Y., Clark, K.B., 1997. Managing in an age of modularity. Harvard Business Review 75 (5), 84–94. Baldwin, C.Y., Clark, K.B., 2000. Design Rules. The MIT Press, Cambridge, Mass. Baron, R.M., Kenny, D.A., 1986. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51, 1173–1182. Brown, S., Bessant, J., 2003. The manufacturing strategy-capabilities links in mass customisation and agile manufacturing–an exploratory study. International Journal of Operations and Production Management 23 (7), 707–730. Campbell, D.T., Fiske, D.W., 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin 56, 81–105. Carey, M., 1997. Modularity times three. Sea Power 40 (4), 81–84. Carr, A.S., Pearson, J.N., 1999. Strategically managed buyer–supplier relationships and performance outcomes. Journal of Operations Management 17, 497–519. Chang, T.S., Ward, A.C., 1995. Design-in-modularity with conceptual robustness. Research in Engineering Design 7, 67–85. Chang, H.H., 2009. An empirical study of evaluating supply chain management integration using the balanced scorecard in Taiwan. Service Industries Journal 29, 185–202. Cherns, A.B., 1976. The principles of sociotechnical design. Human Relations 29 (8), 783–792. Cherns, A., 1987. Principles of sociotechnical design revisited. Human Relations 40 (3), 153–161. Chesbrough, H., Teece, D.J., 1996. When is virtual virtuous? Organizing for innovation. Harvard Business review 74 (1), 65–74. Chin, K.-S., Tummala, V.M.R., Leung, J.P.F., Tang, X., 2004. A study on supply chain management practices: the Hong Kong manufacturing perspective. International Journal of Physical Distribution and Logistics Management 34 (6), 505–524. Cholette, S., 2009. Mitigating demand uncertainty across a winery’s sales channels through postponement. International Journal of Production Research 47, 3587–3609. Churchill Jr., G.A., 1979. A paradigm for developing better measures of marketing constructs. Journal of Marketing Research 16, 64–73. Cleveland, G., Schroeder, R.G., Anderson, J.C., 1989. A theory of production competence. Decision Sciences 20 (4), 655–668. Closs, D.J., Jacobs, M.A., Swink, M., Webb, G.S., 2008. Toward a theory of competencies for the management of product complexity: Six case studies. Journal of Operations Management 26, 590–610.

Cohen, J., Cohen, P., West, S.G., Aiken, L., 2003. Applied Multiple Regression/ Correlation Analysis for the Behavioral Sciences, 3rd ed. Lawrence Erlbaum Associates, Mahwah, NJ. Collett, S., Spicer, R.J., 1995. Improving productivity through cellular manufacturing. Production and Inventory Management Journal 36, 71. Collier, D.A., 1981. The measurement and operating benefits of component part commonality. Decision Sciences 12 (1), 85–96. Collier, D.A., 1982. Aggregate safety stock levels and component part commonality. Management Science 28 (11), 1296–1303. Conway, M., 1968. How do committees invent? Datamation 14 (4), 28–31. Crawford, C.M., Rosenau Jr., M.D., 1994. Significant issues for the future of product innovation. The Journal of Product Innovation Management 11 (3), 253–259. Danese, P., Romano, P., 2004. Improving inter-functional coordination to face high product variety and frequent modifications. International Journal of Operations and Production Management 24 (9/10), 863–885. Desai, P., Kekre, S., Radhakrishnan, S., Srinivasan, K., 2001. Product differentiation and commonality in design: Balancing revenue and cost drivers. Management Science 47 (1), 37–51. DeSanctis, G., Poole, M.S., 1994. Capturing the complexity in advanced technology use: adaptive structuration theory. Organization Science 5, 121–147. Drazin, R., Van De Ven, A.H., 1985. Alternative forms of fit in contingency theory. Administrative Science Quarterly 30 (4), 514–539. Droge, C., Jayaram, J., Vickery, S.K., 2004. The effects of internal versus external integration practices on time-based performance and overall firm performance. Journal of Operations Management 22, 557–573. Dyer, J.H., Ouchi, W.G., 1993. Japanese-style partnerships: giving companies a competitive edge. Sloan Management Review 35 (1), 51–63. Erlicher, L., Massone, L., 2005. Human factors in manufacturing: new patterns of cooperation for company governance and the management of change. Human Factors and Ergonomics in Manufacturing 15, 403–419. Fabbe-Costes, N., Jahre, M., 2007. Supply chain integration improves performance: the emperor’s new suit? International Journal of Physical Distribution and Logistics Management 37 (10), 835–855. Fabbe-Costes, N., Jahre, M., 2008. Supply chain integration and performance: a review of the evidence. International Journal of Logistics Management 19 (2), 130–154. Feitzinger, E., Lee, H.L., 1997. Mass customization at Hewlett-Packard: the power of postponement. Harvard Business Review 75 (1), 116–121. ¨ Fernandez, I., Kekale, T., 2005. The influence of modularity and industry clockspeed on reverse logistics strategy: implications for the purchasing function. Journal of Purchasing and Supply Management 11 (4), 193–205. Fine, C.H., Vardan, R., Pethick, R., El-Hout, J., 2002. Rapid-response capability in value-chain design. MIT Sloan Management Review 43, 69–75. Fine, C.H., Golany, B., Naseraldin, H., 2005. Modeling tradeoffs in threedimensional concurrent engineering: a goal programming approach. Journal of Operations Management 23, 389–403. Fliedner, G., Vokurka, R.J., 1997. Agility: competitive weapon of the 1990s and beyond? Production and Inventory Management Journal 38 (3), 19–24. Flynn, B.B., Huo, B., Zhao, X., 2010. The impact of supply chain integration on performance: a contingency and configuration approach. Journal of Operations Management 28, 58–71. Frohlich, M.T., Westbrook, R., 2001. Arcs of integration: an international study of supply chain strategies. Journal of Operations Management 19, 185–200. Galvin, P., Morkel, A., 2001. The effect of product modularity on industry structure: the case of the world bicycle industry. Industry and Innovation 8, 31–47. Garud, R., Kumaraswamy, A., 1995. Technological and organizational designs for realizing economies of substitution. Strategic Management Journal 16, 93–110. Gentry, R.J., Elms, H., 2009. Firm partial modularity and performance in the electronic manufacturing services industry. Industry and Innovation 16 (6), 575–592. Gerchak, Y., Magazine, M.J., Gamble, A.B., 1988. Component commonality with service level requirements. Management Science 34 (6), 753–760. Gershenson, J.K., Prasad, G.J., Zhang, Y., 2003. Product modularity: definitions and benefits. Journal of Engineering Design 14 (3), 295–313. Gimenez, C., Ventura, E., 2005. Logistics-production, logistics-marketing and external integration: their impact on performance. International Journal of Operations and Production Management 25, 20–38. Goffin, K., 2000. Design for supportability: essential component of new product development. Research Technology Management 43 (2), 40–47. Gorman, M.F., Brannon, J.I., 2000. Seasonality and the production-smoothing model. International Journal of Production Economics 65 (2), 173–178. Guiffrida, A.L., 2009. A probability based model for evaluating delivery performance. Journal of the Academy of Business and Economics 9, 95–104. Gunasekarana, A., Ngai, E.W.T., 2005. Build-to-order supply chain management: a literature review and framework for development. Journal of Operations Management 23, 423–451. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., 2009. Multivariate Data Analysis, 7th ed. Prentice-Hall, Upper Saddle River. Hart, S., Banbury, C., 1994. How strategy-making processes can make a difference. Strategic Management Journal 15 (4), 251–269. Hayes, R.H., Wheelwright, S.C., 1984. Restoring our competitive edge : competing through manufacturing. Wiley, New York, pp. 197?228.

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

Henderson, R., Clark, K., 1990. Architectural innovation: the reconfiguration of existing product technologies and the failure of firms. Administrative Science Quarterly 35 (1), 9–30. Holweg, M., Pil, F.K., 2005. Flexibility first. Industrial Engineer 37, 46. Hoogeweegen, M.R., Teunissen, W.J.M., Vervest, P.H.M., Wagenaar, R.W., 1999. Modular network design: Using information and communication technology to allocate production tasks in a virtual organization. Decision Sciences 30, 1073–1103. Hoole, R., 2006. Drive complexity out of your supply chain. Harvard Business School Newsletter, 3–5. Ismail, S., Maling, E., Christiane, B., 2006. Quality in supply chains: an empirical analysis. Supply Chain Management 11 (6), 491–502. Israels, A., 1986. Canonical analysis. A review with applications in ecology. Psychometrica 51, 495–497. Iyer, K.N.S., Germain, R., Frankwick, G.L., 2004. Supply chain B2B e-commerce and time-based delivery performance. International Journal of Physical Distribution and Logistics Management 34, 645–661. Jacobs, M., Swink, M., 2011. Product portfolio architectural complexity and operational performance: incorporating the roles of learning and fixed assets. Journal of Operations Management 29, 677–691. Jacobs, M.A., Vickery, S., Droge, C., 2007. The effects of product modularity on competitive performance: do integration strategies mediate the relationship? International Journal of Operations and Production Management 27, 1046–1068. Jacobs, M.A., Droge, C., Vickery, S., Calantone, R., 2011. The effects of product and process modularity on agility and firm growth performance. Journal of Product Innovation Management 28, 123–137. Jaehne, D.M., Li, M., Riedel, R., Mueller, E., 2009. Configuring and operating global production networks. International Journal of Production Research 47 (8), 2013–2030. Jayaram, J., Tan, K.-C., 2010. Supply chain integration with third-party logistics providers. International Journal of Production Economics 125, 262–271. Kallio, J., Saarinen, T., Tinnila, M., Vepsalainen, A.P.J., 2000. Measuring delivery process performance. International Journal of Logistics Management 11, 75–88. Kanovska, L., 2010. Customer services: a part of market orientation. Economics and Management 15 (1), 562–565. Kasarda, J.D., Rondinelli, D.A., 1998. Innovative infrastructure for agile manufacturers. Sloan Management Review 39 (2), 73–82. Katunzi, T.M., 2011. Obstacles to process integration along the supply chain: manufacturing firm’s perspective. International Journal of Business and Management 6, 105–113. Kim, K., Chhajed, D., 2000. Commonality in product design: cost saving, valuation change and cannibalization. European Journal of Operational Research 125 (3), 602–621. Kim, S.W., 2006. The effect of supply chain integration on the alignment between corporate competitive capability and supply chain operational capability. International Journal of Operations and Production Management 26 (10), 1084–1107. Kim, S.W., 2009. An investigation on the direct and indirect effect of supply chain integration on firm performance. International Journal of Production Economics 119, 328–346. Kotler, P., 1986. Principles of Marketing, 3rd ed. Englewood Cliffs, Prentice-Hall, N.J. Koufteros, X., Vonderembse, M., Jayaram, J., 2005. Internal and external integration for product development: the contingency effects of uncertainty, equivocality, and platform strategy. Decision Sciences 36, 97–133. Koufteros, X.A., Rawski, G.E., Rupak, R., 2010. Organizational integration for product development: the effects on glitches, on-time execution of engineering change orders, and market success. Decision Sciences 41, 49–80. Landeros, R., Monczka, R.M., 1989. Cooperative buyer/seller relationships and a firm’s competitive posture. Journal of Purchasing and Materials Management 25, 9–18. Lawrence, P., Lorsch, J., 1967. Organization and environment; managing differentiation and integration Boston: division of Research, Graduate School of Business Administration. Harvard University. Lee, H.L., Tang, C.S., 1997. Modelling the costs and benefits of delayed product differentiation. Management Science 43, 40–53. Lewis, J.D., 1995. Business world: western companies improve upon the Japanese ‘keiretsu’. Wall Street Journal A21. Li, L., Warfield, J.N., 2011. Perspectives on quality coordination and assurance in global supply chains. International Journal of Production Research 49 (1), 1–4. Lin, Y., Wang, Y., Yu, C., 2010. Investigating the drivers of the innovation in channel integration and supply chain performance: a strategy orientated perspective. International Journal of Production Economics 127, 320–332. Lohse, N., Ratchev, S., Valtchanov, G., 2004. Towards web-enabled design of modular assembly systems. Assembly Automation 24 (3), 270–279. Lohse, N., Hirani, H., Ratchev, S., 2005. Equipment ontology for modular reconfigurable assembly systems. International Journal of Flexible Manufacturing Systems 17, 301. Lorenzi, S., Lello, A.D., 2001. Product modularity theory and practice: the benefits and difficulties in implementation within a company. International Journal of Automotive Technology and Management 1, 425–448. Mac Neil, I.R., 1980. The New Social Contract: An Inquiry into Modern Contractual Relationships. Yale Press, New Haven.

261

McClain, J.O., Maxwell, W.L., Muckstadt, J.A., Thomas, L.J., Weiss, E.N., Collier, D.A., 1984. Comment on ’’Aggregate safety stock levels and component part commonality’’/Reply. Management Science 30 (6), 772–774. McConneghey, K., 1999. Environmental Laboratory Annual Performance Benchmark Study. Mc Conneghey and Co., Raleigh, NC, p. 96. Meyer, M.H., Lehnerd, A.P., 1997. The Power of Product Platforms: Building Value and Cost Leadership. Free Press, New York. Meyer, M.H., Tertzakian, P., Utterback, J.M., 1997. Metrics for managing research and development in the context of the product family. Management Science 43, 88–111. Mikkola, J.H., 2006. Capturing the degree of modularity embedded in product architectures. The Journal of Product Innovation Management 23, 128. Montabon, F., Sroufe, R., Narasimhan, R., 2007. An examination of corporate reporting, environmental management practices and firm performance. Journal of Operations Management 25, 998–1014. Nagel, R.N., Bhargava, P., 1994. Agility: the ultimate requirement for world-class manufacturing performance. National Productivity Review 13 (3), 331–340. Narasimhan, R., Carter, J.R., 1998. Linking business unit and material sourcing strategies. Journal of Business Logistics 19 (2), 155–171. Narasimhan, R., Kim, S.W., 2002. Effect of supply chain integration on the relationship between diversification and performance: evidence from Japanese and Korean firms. Journal of Operations Management 20, 303–323. Narasimhan, R., Swink, M., Kim, S.W., 2006. Disentangling leanness and agility: an empirical investigation. Journal of Operations Management 24, 440–457. Noaker, P.M., 1994. The search for agile manufacturing. Manufacturing Engineering 113, 40–43. Novak, S., Eppinger, S.D., 2001. Sourcing by design: product complexity and the supply chain. Management Science 47, 189–204. Pagh, J.D., Cooper, M.C., 1998. Supply chain postponement and speculation strategies: how to choose the right strategy. Journal of Business Logistics 19 (2), 13–33. Paiva, E.L., 2010. Manufacturing and marketing integration from a cumulative capabilities perspective. International Journal of Production Economics 126, 379–386. Petersen, K.J., Handfield, R.B., Ragatz, G.L., 2003. A model of supplier integration into new product development. Journal of Product Innovation Management 20, 284–299. Petersen, K.J., Handfield, R.B., Ragatz, G.L., 2005. Supplier integration into new product development: coordinating product, process and supply chain design. Journal of Operations Management 23, 371–388. Pine II, B.J., Victor, B., Boynton, A.C., 1993. Making mass customization work. Harvard Business Review 71 (5), 108–119. Pine, B.J., 1993. Mass customization : the new frontier in business competition. Harvard Business School Press, Boston, Mass. Podsakoff, P.M., Podsakoff, N.P., MacKenzie, S.B., Lee, J.-Y., 2003. Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology 886, 879–903. Powell, T.C., Dent-Micallef, A., 1997. Information technology as competitive advantage: the role of human, business, and technology resources. Strategic Management Journal 18 (5), 375–405. Prajogo, D., Olhager, J., 2012. Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration. International Journal of Production Economics 135, 514–522. Prince, J., Kay, J.M., 2003. Combining lean and agile characteristics: creation of virtual groups by enhanced production flow analysis. International Journal of Production Economics 85 (3), 305–318. Ragatz, G.L., Sandor, J., 2009. General session. In: 55th Annual Purchasing and Supply Chain Management Executive Seminar, Michigan State University. Ro, Y.K., Liker, J.K., Fixson, S.K., 2007. Modularity as a strategy for supply chain coordination: the case of U.S. auto. IEEE Transactions on Engineering Management 54 (1), 172–189. Rodrigues, A.M., Stank, T.P., Lynch, D.F., 2004. Linking strategy, structure, process, and performance in integrated logistics. Journal of Business Logistics 25, 65–94. Salvador, F., Forza, C., Rungtusanatham, M., 2002. Modularity, product variety, production volume, and component sourcing: theorizing beyond generic prescriptions. Journal of Operations Management 20 (5), 549. Sanchez, R., Mahoney, J.T., 1996. Modularity, flexibility, and knowledge management in product and organization design. Strategic Management Journal 17, 63–79. Sanchez, R., 1995. Strategic flexibility in product competition. Strategic Management Journa 16 (Special Issue), 135–160. Schilling, M.A., Steensma, H.K., 2001. The use of modular organizational forms: an industry-level analysis. Academy of Management Journal 44 (6), 1149–1168. Schilling, M.A., 2000. Toward a general modular systems theory and its application to interfirm product modularity. The Academy of Management Review 25 (2), 312–329. Schmenner, R.W., Swink, M.L., 1998. On theory in operations management. Journal of Operations Management 17 (1), 97–113. Schonberger, R.J., 1982. Japanese Manufacturing Techniques: Nine Hidden Lessons in Simplicity. Free Press, New York, NY. Shafer, S.M., Charnes, J.M., 1993. Cellular versus functional layouts under a variety of shop operating conditions. Decision Sciences 24, 665–681.

262

C. Droge et al. / Int. J. Production Economics 137 (2012) 250–262

Sharifi, H., Zhang, Z., 2001. Agile manufacturing in practice: application of a methodology. International Journal of Operations and Production Management 21, 772. Simon, H., 1962. The architecture of complexity. Proceedings of the American Philosophical Society 106, 467–482. Skinner, W., 1974. The focused factory. Harvard Business Review 52 (3), 113–122. Sohal, A.S., Fitzpatrick, P., Power, D., 2001. A longitudinal study of a flexible manufacturing cell operation. Integrated Manufacturing Systems 12, 236–245. Spring, M., Araujo, L., 2009. Service, services and products: rethinking operations strategy. International Journal of Operations and Production Management 29 (5), 2–12. Stank, T.P., Keller, S.B., Closs, D.J., 2001. Performance benefits of supply chain logistical integration. Transportation Journal 41, 32–46. Stewart, D., Love, W., 1968. A general canonical correlation index. Psychological Bulletin 70, 160–163. Susarla, A., Barua, A., Whinston, A.B., 2009. Multitask agency, modular architecture, and task disaggregation in SaaS. Journal of Management Information Systems 26 (4), 87–117. Swamidass, P.M., Newell, W.T., 1987. Manufacturing strategy, environmental uncertainty and performance: a path analytic model. Management Science 33 (4), 509–525. Swaminathan, J.M., Tayur, S.R., 1998. Managing broader product lines through delayed differentiation using vanilla boxes. Management Science 44, S161–S172. Swink, M., Narasimhan, R., Wang, C., 2007. Managing beyond the factory walls: effects of four types of strategic integration on manufacturing plant performance. Journal of Operations Management 25, 148–164. Sytch, M., Gulati, R., 2008. Creating value together. MIT Sloan Management Review 50 (1), 12–13. Talib, F., Rahman, Z., Qureshi, M.N., 2010. Integrating total quality management and supply chain management: similarities and benefits. Journal of Supply Chain Management 7 (4), 26–44. Tse, Y.K., Tan, K.H., 2011. Managing product quality risk in a multi-tier global supply chain. International Journal of Production Research 49 (1), 139–158. Ulrich, K.T., Eppinger, S.D., 1995. Product Design and Development. McGraw-Hill, New York. Ulrich, K., Tung, K., 1991. Fundamentals of product modularity. In: Proceedings of the 1991 ASME design engineering technical conferences - conference on design/manufacture integration, Miami, FL, 73–79. Ulrich, K., 1995. The role of product architecture in the manufacturing firm. Research Policy 24, 419–440.

van Hoek, R.I., Weken, H.A.M., 1998. The impact of modular production on the dynamics of supply chains. International Journal of Logistics Management 9, 35–50. van Hoek, R.I., Commandeur, H.R., Vos, B., 1998. Reconfiguring logistics systems through postponement strategies. Journal of Business Logistics 19 (1), 33–54. Venkatraman, N., Ramanujam, V., 1987. Measurement of business economic performance: an examination of method convergence. Journal of Management 13 (1), 109–122. Vickery, S.K., Droge, C., Markland, R., 1994. Strategic production competence: convergent, discriminant, and predictive validity. Production and Operations Management 3 (4), 308–320. Vickery, S.K., Jayaram, J., Droge, C., Calantone, R., 2003. The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships. Journal of Operations Management 21, 523–540. Vijayasarathy, L.R., 2010. Supply integration: an investigation of its multidimensionality and relational antecedents. International Journal of Production Economics 124, 489–505. von Hippel, E., 1990. Task partitioning: an innovation process variable. Research Policy 19 (5), 407–418. Voordijk, H., Meijboom, B., Haan, J.D., 2006. Modularity in supply chains: a multiple case study in the construction industry. International Journal of Operations and Production Management 26, 600–618. Wagner, S.M., Krause, D.R., 2009. Supplier development: communication approaches, activities and goals. International Journal of Production Research 47 (12), 3161–3177. Walz, G.A., 1980. Design tactics for optimal modularity. Proceedings of Autotestcon IEEE November, 281–284. Wang, X., Li, D., O’Brien, C., 2009. Optimisation of traceability and operations planning: an integrated model for perishable food production. International Journal of Production Research 47 (11), 2865–2886. Ward, C., 1994. What is agility? Industrial Engineering 26, 14. Wisner, J.D., Tan, K.-C., Leong, K., 2008. Principles of Supply Chain Management, second ed. South-Western, Mason, OH. Worren, N., Moore, K., Cardona, P., 2002. Modularity, strategic flexibility, and firm performance: a study of the home appliance industry. Strategic Management Journal 23, 1123–1140. Wright, T.P., 1936. Factors effecting the cost of airplanes. Journal of the Aeronautical Sciences 3, 122–128.