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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, , Volume 113, Issue 2, June 2008, Pages 575-586
This paper aims to help managers of a supply chain to determine an appropriate level of collaboration according to their specific business conditions. For this purpose, a comprehensive simulation model representing two popular supply chain initiatives, that are collaborative planning, forecasting and replenishment (CPFR) and vendor-managed inventory (VMI), is constructed. In addition, a traditionally managed supply chain (TSS) is also included in the model as a benchmark. The results indicate that benefits of CPFR are always higher than VMI. However, we also realize that under certain conditions, the gap between the performances of CPFR and VMI does not rationalize the additional resources required for CPFR. Especially, when the lead time is short and/or when available manufacturing capacity is tight, a careful consideration has to be given on the selection of an appropriate collaboration mode.
A supply chain, consisting of several organizations with different and sometimes conflicting objectives, is a complex network of facilities designed to produce and distribute products according to customers’ demands. By coordinating different enterprises along the logistics network or establishing business partnerships, supply chain management (SCM) is concerned with finding the best strategy for the whole supply chain (Simchi-Levi et al., 2003, p. 2). Nevertheless, finding the best strategy in this complex network of facilities is not an easy task. It requires intensive communication and coordination among trading partners so that material flow along the supply chain is optimized as well as information flow. Fortunately, with the emergence of new management paradigms at the beginning of 1980s, e.g. Lean Thinking, Total Quality Management and Partnership Sourcing Programme, much progress has been made in the coordination of material flow (Mason-Jones and Towill, 2000; Simchi-Levi et al., 2003, p. 5). However, an equal attention has not been paid to the optimization of information flow. This ignorance of the information flow has contributed to one important problem in supply chain literature, which is called “bullwhip effect” (Lee et al., 1997a and Lee et al., 1997b). The bullwhip effect represents the phenomenon where orders to supplier tend to have a larger variance than sales to the buyer (Lee et al., 1997a and Lee et al., 1997b). In return, high inventory levels and poor customer service rates are typical symptoms of the bullwhip effect (Metters, 1997; Chopra and Meindl, 2001, p. 1363). Today, SCM researchers indicate that elimination the bullwhip effect plays a vital role for supply chain enterprises to gain competitive advantage. Most of the researchers focusing on remedies for coping with the bullwhip effect dictate that sharing retail-level information (i.e. point of sales (pos) data) between supply chain members is a prerequisite for elimination of the bullwhip effect, see e.g. Lee et al. (1997a), Chen et al., 2000a and Chen et al., 2000b, McCullen and Towill (2002), Dejonckheere et al. (2004), Ouyang (2006) and Li et al. (2006). Nevertheless, retailers, most of the time, do not desire to engage in information sharing because it provides ignorable levels of benefits for them, see e.g. Lee et al. (2000), Yu et al., 2001 and Yu et al., 2002, Zhao et al., 2002a and Zhao et al., 2002b. Therefore, this requires upstream members (e.g. suppliers or manufacturers) to offer incentives for retailers in return for information sharing. Vendor-managed inventory (VMI) and collaborative planning, forecasting and replenishment (CPFR) are the partnership programs primarily developed to encourage retailers to share information, see e.g. Lee et al. (1997b) and Disney and Towill, 2003a and Disney and Towill, 2003b. VMI, also known as continuous replenishment or supplier-managed inventory, is one of the most widely discussed partnering initiatives for encouraging collaboration and information sharing among trading partners (Angulo et al., 2004). Popularized in the late 1980s by Wal-Mart and Procter & Gamble (Waller et al., 1999), it was subsequently implemented by many other leading companies from different industries, such as Glaxosmithkline (Danese, 2004), Electrolux Italia (De Toni and Zamolo, 2005), Nestle and Tesco (Watson, 2005), Boeing and Alcoa (Micheau, 2005), etc. It is a supply chain initiative where the vendor decides on the appropriate inventory levels of each of the products and the appropriate inventory policies to maintain those levels. The retailer provides the vendor with access to its real-time inventory level. In this partnership program, the retailer may set certain service level and/or self-space requirements, which are then taken into consideration by the vendor. That is, in a VMI system, the retailer's role shifts from managing inventory to simply renting retailing space (Simchi-Levi et al., 2003, p. 154; Mishra and Raghunathan, 2004). VMI offers a competitive advantage for retailers because it results in higher product availability and service level as well as lower inventory monitoring and ordering cost (Waller et al., 1999; Achabal et al., 2000). For vendors, on the other hand, it results in reduced bullwhip effect (Lee et al., 1997b; Disney and Towill, 2003a and Disney and Towill, 2003b) and better utilization of manufacturing capacity (Waller et al., 1999), as well as better synchronization of replenishment planning (Waller et al., 1999; Çetinkaya and Lee, 2000). While many benefits have been identified in the literature, there are also a number of challenges that may exist in practice and that can potentially reduce the benefits obtained from VMI or lead to failures in VMI programs. For instance, Spartan Stores, a grocery chain, shut down its VMI effort about 1 year after due in part VMI vendors’ inability to deal with product promotions (Simchi-Levi et al., 2003, p. 161). Similarly, Kmart cut a substantial amount of VMI contracts because Kmart is not satisfied with the forecasting ability of VMI vendors (Fiddis, 1997). Consequently, many studies have been carried out to investigate the effectiveness of VMI programs under different conditions. For instance, Kuk (2004) empirically tested the acclaimed benefits of VMI programs in electronics industry. Similarly, Sari (2007) used a simulation model to evaluate the benefits of VMI under different market conditions. Dong and Xu (2002), on the other hand, evaluated the value of VMI programs both for suppliers and buyers. Most of these studies show that ineffective usage of retail-level information is one major limitation of VMI programs (see e.g. Aviv, 2002; Ovalle and Marquez, 2003; Angulo et al., 2004; Yao et al., 2007). That is, since retailers are closer to the marketplace, they may have better knowledge about customer behaviors, products and marketplace. However, in most, if not all, VMI programs, this unique knowledge of the retailers cannot be joined into inventory decisions. This is because in a typical VMI program, retailers are excluded from demand forecasting process. Indeed, in a VMI system, the responsibilities of the retailers are noting more than sharing sales and inventory data. CPFR, on the other hand, can solve majority of the problems that are encountered in adaptation of VMI because it requires all members of a supply chain to jointly develop demand forecasts, production and purchasing plans, and inventory replenishments (Aviv, 2002). It is a business practice that combines the intelligence of multiple trading partners in the planning and fulfilment of customer demand (CPFR Workgroup, 2002). CPFR adds value to the supply chain in the form of reduced inventory and increased customer service level by achieving better match of demand and supply (Foote and Krishnamurthi, 2001; Aghazadeh, 2003; Aichlmayr, 2003; Fliedner, 2003). Nonetheless, successful implementation of CPFR is not an easy task. It requires more intensive organizational resources than VMI as well as mutual trust of multiple trading partners (Barratt and Oliveria, 2001; Fliedner, 2003). Furthermore, dramatic changes are also required in usual ways of doing business for CPFR implementation. Consequently, examination of the previous literature reveals the fact that adaptation of higher levels of collaboration among members of a supply chain creates greater benefits for the supply chain. On the other hand, we also see that development and operational costs of a highly integrated collaboration is also higher. That is, while CPFR eliminates most of the problems encountered in VMI programs; investment and operation costs of CPFR are substantially higher along with greater implementation difficulties. Indeed, these difficulties might explain why many of the CPFR programs have not moved beyond a limited number of product categories or a small set of trading partners (see e.g. Baird, 2003; Program may build CPFR momentum, 2005). Therefore, this trade-off between benefits and costs of supply chain collaborations creates an urgent need for SCM practitioners to determine the right collaboration level for their supply chains. Today, many SCM practitioners try to determine the appropriate level of collaboration for their supply chains. Here, the following two questions play a critical role in determining the right collaboration level: • Does it is required to invest in CPFR if an earlier supply chain initiative such as VMI, had already been adopted? In other words, does the gap between the performances of CPFR and VMI compensate the cost of investing in CPFR? • Which factors are influential in answering the question described above? Do capacity of the manufacturing facility, lead times, or uncertainty in customer demand influence the desire for CPFR? To the best of our knowledge, there have been very few research studies aiming to explore these questions. That is, a few research studies e.g. Raghunathan (1999), Aviv, 2001, Aviv, 2002 and Aviv, 2007, Ovalle and Marquez (2003) and Disney et al. (2004) represent most of the developments in this area. Our paper is different from these previous studies in three ways. First, most of the models have tended to mainly analytical with some very restrictive assumptions (e.g. two-stage supply chain, normally distributed or correlated market demands) for the sake of mathematical tractability (e.g. Raghunathan, 1999; Aviv, 2001, Aviv, 2002 and Aviv, 2007; Disney et al., 2004). Second, some of the models have been developed so far are only concentrated on forecasting part of CPFR (e.g. Aviv, 2001 and Aviv, 2007). Third, as far as we know, none of the models has explored CPFR and VMI comparatively in capacitated multi-stage supply chains under both stationary and non-stationary customer demands (e.g. Ovalle and Marquez, 2003) as we have done in this paper. Therefore, this paper contributes to the current literature by extending the results of previous research studies in a way that managers in a supply chain enterprise can determine an appropriate level of collaboration for their supply chains. Unlike many prior analytical studies which have very restrictive assumptions for the sake of mathematical tractability (e.g. Mishra and Raghunathan, 2004; Lee and Chu, 2005; Yao et al., 2007), we have used a simulation model in this study to investigate the benefits of CPFR and VMI under more realistic circumstances. The simulation approach has been used extensively in the literature for analyzing supply chain systems (e.g. Waller et al., 1999; Zhao et al., 2002a and Zhao et al., 2002b; Angulo et al., 2004; Lau et al., 2004; Sari, 2007; Zhang and Zhang, 2007). In this study, we considered a four-stage supply chain, which consists of four echelons: a manufacturing plant, a warehouse, a distributor and a retailer. The plant has limited manufacturing capacity and produces a single product. Each enterprise replenishes its inventory from its immediate upstream enterprise. The remainder of this study is organized as follows. Section 2 clarifies the methodology and development of the simulation model. Setting of experimental design is identified in Section 3, followed by simulation output analysis in Section 4. Conclusions are presented in Section 5.
نتیجه گیری انگلیسی
This paper comparatively investigates the performance increase obtained from VMI and CPFR in a four-stage supply chain under both stationary and non-stationary customer demands viva a comprehensive simulation study. Through comprehensive simulation experiments and subsequent statistical analysis of the simulation outputs, we make the following three important observations. First, we observe that the benefits gained from CPFR are always higher than that of VMI under all conditions considered in this study. That is, compared with VMI, CPFR produces lower total supply chain cost as well as higher customer service levels. Therefore, from this study, we may sure and clear about the fact that the managers of a supply chain enterprise better off investing in CPFR. Second, through simulation output analysis, we observe that the performance increase gained from CPFR and VMI significantly depends on three factors. These are capacity tightness of the plant, replenishment lead times and uncertainty in market demand. As these factors get different levels, the benefits obtained from both initiatives also change substantially. Moreover, the gap between the performance improvements produced by CPFR and VMI also changes significantly. For example, we observe that when the lead times are short and/or where available manufacturing capacity is tight, the benefits of switching from VMI to CPFR are at its lowest value. That is, contribution of switching to CPFR is almost ignorable when we consider the additional resources required for CPFR adaptation. The managerial implication of this finding is great because nowadays, adaptation of every business practice, which is in fashion, is popular without analyzing the suitability of it for specific business conditions. Therefore, this research indicates that it is of highly importance to make careful benefit/cost analysis to invest in CPFR under the conditions where lead time is short and/or where available manufacturing capacity is very tight. Finally, we recognize that there are substantial decreases in the performance of VMI as the uncertainty in customer demand increases. On the other hand, we also recognize that there is only a slight decrease in the performance of CPFR under higher variable customer demands. Thus, indicating that highly variable customer demand results in widening the gap between the performances of CPFR and VMI. This is because of the fact that supply chain members may better manage the uncertainties through joint forecasting and inventory planning under CPFR. Therefore, one other managerial implication that can be drawn from this finding is that the practitioners have to invest in CPFR as soon as possible in the industries in which demand uncertainty is highly variable. The computer industry, for instance, with its very short product life cycle and highly variable customer demand, is a good example to industries where the adaptation of CPFR is an urgent need. Although this study provides important insights into CPFR and its relationship with VMI, we have to state that there are some limitations of this study. First, we consider a serial supply chain structure with one member at each echelon. This supply chain structure is only a simplified case and in future research studies, modeling more realistic supply chain structures may better explain and extend the results obtained from this research. Second, we assume that the members in the supply chain apply order-up to policies to make their production/inventory decisions; however, there are other types of inventory/production policies that can be included in the model. Third, the cost structure used in the simulation model only represents one special case.