The Effect of Collaborative Demand Planning on Tier 1 Supplier Responsiveness

Abstract 020-0213

The Effect of Collaborative Demand Planning on Tier 1 Supplier Responsiveness

Sang Ho Chae

Yonsei School of Business, Yonsei University

A204, Parkhouse, 140-1, Daeshin-dong, Seodaemun-gu,Seoul, 120-160, South Korea

+82 (0)10 9861 4252

POMS 22nd Annual Conference

Reno, Nevada, U.S.A.

April 29 to May 2, 2011

1. Introduction

1.1 Research Context

Collaboration and information sharing between companies in supply chains have been one of the major topics for supply chain management professionals and researchers. Since the adoption of electronic data interchange (EDI), information systemsfor inter-firm communication have enabled firms to implement new collaboration processes to increase supply chain performance. In 1992, efficient consumer response (ECR) was initiated by the grocery retail industry to reduce costs in supermarket distribution chain and respond to consumer demand quickly (Lee, 2002). Collaborative Planning, Forecasting, and Replenishment (CPFR) is a second-generation ECRsuggested by the Voluntary Interindustry Commerce Standard (VICS) (VICS, 2000; Seifert, 2003).Recently, consumer electronics manufacturers and their customers, including retailers and mobile network operators, have been implementing CPFRto cope with fast changing consumer demand. As consumers require lower price and higher availability of various electronic goods, leading consumer electronics retailersare increasing collaboration with consumer electronics manufacturers to meet consumer demand efficiently.However, Frohlich and Westbrook (2001) argue that supply chain integration between only customers (e.g. electronics retailers) and manufacturers (e.g. electronics manufacturers) without integration with manufacturers’ suppliers brings little benefit.

Considering this indication of Frohlich and Westbrook(2001), this study assumes that a successful implementation of a collaborative demand planning between a manufacturer and a retailer should be also supported by integration with the manufacturer’s suppliers.Adopting a single case study of Samsung Electronics—a multinational semiconductor and consumer electronics manufacturer, this study investigatesthe relationships between collaborative demand planningsuch as CPFR and the manufacturer’s integration with suppliers.

1.2 Research Question

A recently developed manufacturing paradigm of agile supply chain pursues highly responsive manufacturing and supply chain processes to constantly changing market situations. Therefore, a manufacturer may consider the following underlying problems when it implements collaboration processes with customers and suppliers:

1. Responding efficiently to customer demand

2. Increasing responsiveness of suppliers

Within the context of CPFR implementation in the consumer electronics industry, this study aims at understanding the approaches of a consumer electronics manufacturer to solve above problems. These problems are narrowed down to the following research question.

How does CPFR implementation affect responsiveness of manufacturer’s suppliers?

In relation to the research context and research question, the rest of this study is organized into the following sections. The second sectionreviews the literature to build a theoretical foundation of this study. Based on the discussion in Section 2, the conceptual model to explain the effect of CPFR is developed in the third section. Section 4 justifies the application of the case study method to this research project and explains the methods for data collection and analysis.In Section 5, the conceptual model developed in Section 3 is tested by analyzing the case of Samsung Electronics. Finally, the conclusion in Section 6 provides implications for managers and suggestions for further research.

2. Theoretical Background

2.1 The Resource Based View

The resource based view of the firm recognizes the firm as a bundle of productive resources (Penrose, 1959). In the strategic management literature, the interchangeably used terms of firm resources and capabilities are defined as tangible and intangible assets that enable firms to develop and implement strategies (Porter, 1981; Daft, 1983). Firm resources can be classified into three categories of physical capital resources (Williamson, 1975), human capital resources (Becker, 1964), and organizational capital resources (Tomer, 1987). The resource based view explains the firm’s sustained competitive advantage under two main assumptions. First, this model assumes that resources within firms are heterogeneous and have different levels of efficiency (Peteraf, 1993). Resources with higher level of efficiency enable firms to create higher value to customers or produce at lower cost.Second, the model assumes that these heterogeneous resources may not be easily transferable between firms.Under the assumptions of heterogeneity and immobility of firm resources, Barney (1991) suggests four conditions that a firm resource must have in order to create sustained competitive advantage over rival firms. These conditions are: value, rareness, imperfect imitability, and imperfect substitutability. Firm resources meeting above conditions are often termed strategic resources.

Traditionally, strategic resources have been thought to be restricted to the boundaries of the firm and non-tradable (Dierickx and Cool, 1989). It is claimed that if the resources are tradable, they are not inimitable or immobile anymore, thus cannot create sustained competitive advantage. However, recent studies suggest a broader view of strategic resources that they may exist beyond the boundaries of the firm (Das and Teng, 2000), or within the network of inter-firm relationships (Afuah, 2001). These recent approach to strategic resources termed the ‘extended resource based view of the firm,’ suggests the attainability of strategic assets outside of the firm boundaries (Mathews, 2003).

Based on the resource based view, Ireland et al. (2002) especially emphasize that effectively managed strategic alliances can be a source of valuable firm resources and create sustained competitive advantage. They suggest that strategic alliances provide access to previously unavailable resources and opportunities to develop new resources by collaboration. Moreover, strategic alliances allow firms to access complementary resources, which can develop new competitive advantage (Ireland et al., 2001) through economies of scope, synergies, and development of new resources and subsequent skills (Hitt et al., 2001).

2.2 Social Capital Theory

Social capital is “a valuable asset that stems from access to resources made available through social relationships” (Granovetter, 1992). Nahapiet and Ghoshal (1998) suggest that there are three highly interrelated dimensions of social capital that can be developed between partner firms: structural dimension, relational dimension, and cognitive dimension. The structural dimension of social capital refers to “the overall pattern of connections between actors” (Nahapiet and Ghoshal, 1998). The relational dimension of social capital refers to the strength of network ties between actors (Krause et al., 2007), or assets embedded in personal relationships, such as trust, obligation and reciprocity (Nahapiet and Ghoshal, 1998). Nahapiet and Ghoshal (1998) identify the cognitive dimension of social capital as the resources providing shared representations, interpretations, and system of meaning between actors.

Recently, the supply chain management literature has applied social capital theory in the context of buyer-supplier relationships. Krause et al. (2007) suggest that relational capital (buyer and supplier dependence) accumulation has significant positive effects on buyer cost and total cost performance improvements, while structural capital (supplier development activities) accumulation has more impacts on quality, delivery, and flexibility. Cognitive capital (shared goals and values) accumulation has important effects on buyer performance improvements in cost and total cost, as well as quality, delivery, and flexibility. Lawson et al. (2008) argue that relational capital (personal interaction, mutual respect, and mutual trust) accumulation and structural capital (managerial communication and technical exchange) accumulation have positive relationships with buyer performance improvement in product design, process design, lead time, and product quality.Cousins et al. (2006) put more focus on relational capital and investigate how formal and informal socialization mechanisms facilitate relational capital accumulation and impact supplier relationship outcomes. Gupta and Govindarajan (2000: 483) refer socialization mechanisms to “those organizational mechanisms which build interpersonal familiarity, personal affinity, and convergence in cognitive maps among personnel from different subsidiaries.” Applying the extended resource based view, Cousins and Menguc (2006) suggest that relational capital created by inter-firm socialization mechanisms may be seen as a strategic resource of the firm, since it is valuable, rare, and difficult to imitate or substitute. Drawing on these arguments, it can be suggested that inter-firm socialization plays an important role in sustaining competitive advantage of the firm.

2.3 Supply Chain Management

2.3.1 Supply Chain Integration

Supply chain integration is defined as “linking their internal processes to external suppliers and customers in unique supply chains” (Frohlich and Westbrook, 2001: 185). Ragatz et al. (2002) point out that supply chain integration is critical for the delivery of superior consumer value and improves cost performance, delivery, quality, and cycle time.

Frohlich and Westbrook (2001) argue that companies with ‘seamlessly’ integrated supply chain with both suppliers and customers have higher rates of performance improvement. Based on their empirical research investigating 322 manufacturers around the world, they introduce a graphical illustration of strategic positions in supply chain integration called the ‘arcs of integration’ to suggest that firms with the ‘outward-facing supply chain strategy’ (extensive integration with both suppliers and customers) have the highest rates of performance improvement. Another empirical research of Frohlich and Westbrook (2002) on demand chain management (DCM) shows similar results. Demand chain management is based on ‘pull control’, where supply chain actions and movements of materials are triggered by the end-customers’ demand (Lummus and Vokurka, 1999). Based on the data collected from United Kingdom manufacturers and services, Frohlich and Westbrook (2002) compare the web-based demand chain management model to the web-based demand integration, web-based supply integration, and web-based low integration models. The result of their study shows that adopting the demand chain management strategy brings more benefits (faster delivery time, reduced transaction costs, greater profitability, and enhanced inventory turnover) than adopting the demand integration, supply integration, or low integration strategy.

Paulraj and Chen (2007) investigate the antecedents of supply chain integration and argue that strategic buyer-supplier relationships and information technology have positive effects on supply chain integration. Better coordination based on trust and commitment between buyers and suppliers facilitates supply chain integration (Cavinato, 2005). Information technology also enhances supply chain integration by facilitating collaborative planning (Karoway, 1997) and linking demand information to upstream supply chain activities (Min and Galle, 1999). In addition to identifying the antecedents of supply chain integration, Paulraj and Chen (2007) argue that supply chain integration and supply chain agility performance are positively related.

2.3.2 Agile Supply Chains

Supply chain agility is defined as “the supply chain’s capability to adapt or respond in a speedy manner to a changing marketplace environment” (Swafford et al., 2006: 172). Agile supply chains allow firms to have competitive advantage over competitors by enabling firms to have better market sensitivity, capability of synchronizing supply with demand, and ability to reduce cycle times (Swafford et al., 2006). Brown and Eisenhardt (1998) argue that to be competitive, firms have to adapt to unpredictable, constantly changing marketplace conditions.

Fisher (1997) proposes that the design of a supply chain should be aligned with the type of products to pursue higher performance of the supply chain. He categorizes products and supply chains into functional / innovative products and physically efficient / market responsive supply chains, respectively. Market-responsive supply chains can be alternatively termed ‘agile supply chains’. He argues that innovative products with demand variation and short life cycles should be processed by an agile supply chain. On the other hand, he suggests that functional products should be processed by a physically efficient or ‘lean’ supply chain. Selldin and Olhager (2007) empirically test Fisher’s (1997) model and show that the companies with the match between products and supply chain type perform better in cost, delivery speed, and delivery dependability.As Fisher (1997) suggests the differences between physically efficient supply chains and market responsive supply chains, Christopher (2000) distinguishes agility from ‘leanness’. He notes that leanness focuses on the elimination of waste and by itself it does not enable the ability of the organization to meet specific customer needs more rapidly.

Lee (2004) emphasizes the importance of ‘agile, adaptable, and aligned’ supply chain, which he terms the ‘triple-A supply chain’, to build a sustained competitive advantage. According to Swafford et al.’s (2006) definition of supply chain agility, Lee’s (2004) term of ‘adaptability’ can be also described as agility. Alignment, which is achieved by exchange of information and knowledge, sharing risks, costs, and benefits between buyers and suppliers (Lee, 2004), can be described as supply chain integration. Therefore, Lee’s (2004) argument of ‘agile, adaptable, and aligned’ supply chains is also highlighting the importance of supply chain integration and supply chain agility. Current business practices, such as efficient consumer response (ECR) and Collaborative Planning, Forecasting, and Replenishment (CPFR) are pursuing both supply chain integration and supply chain agility.

2.4 ECR and CPFR

Efficient Consumer Response (ECR) is a retailer-supplier collaboration process developed for the United States grocery retailers and branded manufacturers in the early 1990s. ECRis based on the pull principle pursuing synchronization of production and distribution on the basis of the real demand information from consumers (Seifert, 2003). ECRpractices consist of three major areas of joint marketing and sales activities, joint logistics and supply chain activities, and collaborative information technology and process improvement tools (Corsten and Kumar, 2005). The benefits of efficient consumer response (ECR) have been addressed by large retailers and manufacturers, such as Coca-Cola (CCRRGE, 1994) and Wal-Mart (Seifert, 2003; p.1).

Collaborative Planning, Forecasting, and Replenishment (CPFR) is a second-generation ECR suggested by the Voluntary Interindustry Commerce Solutions (VICS) (VICS, 2000; Seifert, 2003). CPFRrequires more collaboration between participating partners than ECR, because it includes joint long-range forecasting rather than solely relying on inventory replenishment triggered by actual customer demand (Grant et al, 2006: 157). VICS suggests the following CPFRprocess model (see Table 2.1) as a guideline for CPFRimplementation (Seifert, 2003).

<Table 2.1> CPFR Process Model

1. Develop Collaboration Arrangement
2. Create Joint Business Plan / Planning
3. Create Sales Forecast
4. Identify Exceptions for Sales Forecast
5. Resolve/Collaborate on Exception Items
6. Create Order Forecast
7. Identify Exceptions for Order Forecast
8. Resolve/Collaborate on Exception Items / Forecasting
9. Generate Order / Replenishment

<Adapted from Seifert (2003: 35)>

CPFR process model has three stages of planning, forecasting, and replenishment. These stages are subdivided into nine steps as shown in Table 2.1. The first step of the model is to identify and agree the objectives and rules of collaboration between customer and manufacturer. The second step is to develop joint business plan, which defines “product group roles, objectives, and articles” (Seifert, 2003: 37). The third to fifth steps are to cooperate on sales forecast by creating sales forecast, agree exceptions for sales forecast, and clarifying exceptions to the sales forecast. The sixth to eighth steps are to synchronize order forecast between customer and manufacturer. The final step (step 9) is order generation.

CPFRimplementation cases in business practices support the positive effect of CPFRon supply chain performance. Motorola’s CPFRinitiative improved forecast accuracy, reduced inventory at the retailer’s distribution center, stock-out rates and transportation costs, and increased long-term shipments (Cederlund et al., 2007). A pilot CPFR-based program of West Marine reduced stock-outs and improved forecast accuracy and on-time delivery (Smith, 2006). Sears, Roebuck and Co. and the Michelin North American Tire Co.’sCPFRinitiative in 2001 helped Sears to achieve improvement in store in-stock levels and increase in distribution-centers-to-store fill rate, while reducing combined Michelin and Sears inventory levels (Steermann, 2003). Procter & Gamble in Europe implemented projects applying the CPFRconcept and experienced benefits, such as improved forecast accuracy and product availability, and reduction in inventory and rush orders (Seifert, 2003: 276).

Modeling-based researches also support the positive effect of CPFRimplementation on supply chain performance. Chen et al. (2007) use simulation modeling to investigate the effect of non-collaboration and CPFRimplementation on supply chain performance. The result of their simulation shows that CPFRimplementation scenarios perform significantly better than non-collaboration scenario in average system service level, average system fulfillment rate, average system cycle time, and total system cost (Chen et al., 2007). Raghunathan (1999) models a supply chain of two customers and a manufacturer to investigate the effect of shared forecasts and shows that forecast information sharing decreases customer and manufacturer’s costs. Aviv (2001) compares two models of joint forecasting and individual forecasting and concludes that joint forecasting is more beneficial.

3. Conceptual Model and Hypotheses

In this section, the research question —How does CPFR implementation affect responsiveness of manufacturer’s suppliers?—is hypothesized into a conceptual model.The modelis consisted of the constructs developed from the theoretical discussions in Section 2 and the relationships between the constructs are represented as four hypotheses (Hypothesis 1, Hypothesis 2a, Hypothesis 2b, and Hypothesis 3).

3.1 Proposed Conceptual Model

Figure 3.1 shows the conceptual model illustrating the relationship between CPFR implementation, manufacturer’s forecast accuracy, and supplier responsiveness. As shown in the research question in Section 1.2, the objective of thisstudy is to find out the relationship between CPFR implementation and supplier responsiveness. In addition, this research project intends to identify the driver of CPFR adoption. Applying Fisher’s (1997) proposition, the conceptual model adopts the manufacturer’s intention to build agile supply chain as an antecedent of CPFR adoption.Lastly, as a reflection of the recent research on supply chain socialization, the conceptual model hypothesizes the relationship between supply chain socialization and the effect of CPFR implementation.