معیارهای جامع و قابل تنظیم برای انتخاب تامین کننده
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|19128||2007||14 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 105, Issue 2, February 2007, Pages 510–523
As firms are increasingly becoming outsourcing oriented, supplier selection has become a major strategic decision for original equipment manufacturers (OEMs). Hundreds of publications can be found in the literature that deal with supplier selection. Researchers from business school often emphasize philosophical issues and focus on developing qualitative principles to guide management decision making. On the other hand, engineering researchers mostly treat supplier selection as an optimization problem. While strategic thinking cannot provide quantitative solutions, a mathematically optimal solution has no meaning if it does not match a firm's business strategy. Therefore, there is a need to integrate strategic thinking with quantitative optimization in order to make sound and effective decisions on supplier selection. This paper presents an integration mechanism in terms of a set of comprehensive and configurable metrics arranged hierarchically that takes into account product type, supplier type, and OEM/supplier integration level. Based on a firm's business strategy, the management configures an appropriate set of metrics used to measure supplier performance. An optimal supplier selection decision is then made based on this chosen set of metrics, achieving a strategic fit between the firm's business model and its supply chain strategy.
Due to global competition, original equipment manufacturers (OEMs) are increasingly becoming outsourcing-oriented in order to lower manufacturing costs. According to Krajewski and Ritzman (2001), the percentage of sales revenues spent on purchased materials varies from more than 80 percent in the petroleum refining industry to 25 percent in the pharmaceutical industry. Therefore, the selection of appropriate suppliers has become an important decision for OEMs. OEMs must choose those suppliers that can deliver required raw materials and components at a high-quality level with low cost to satisfy customer demand. In addition, because of shortened product life cycle, OEMs and suppliers need to develop strategic partnerships so they can quickly adapt to a rapidly changing market. Furthermore, with rising consumerism and the concern about the environment, more and more OEMs are consciously building a consumer and environment friendly image. Partnering with the right suppliers has become a key factor to the success of an OEM (Ellram et al., 2002). As such, many researchers devoted their efforts to developing supplier selection methodologies. Researchers form business school often emphasize philosophical issues and focus on developing qualitative principles to guide management decision making. This is typified by the philosophy of matching business strategy with supply chain strategy, first articulated by Fisher (1997) and later formalized by Chopra and Meindl (2003). On the other hand, engineering researchers mostly treat supplier selection as an optimization problem and attempt to develop mathematical models to generate optimal solutions. We believe these two paradigms are complementary rather than competitive. While strategic thinking cannot provide quantitative solutions, a mathematically optimal solution has no meaning if it does not match a firm's business strategy. The missing link is a set of comprehensive metrics that can be configured based on a firm's business strategy to serve as a basis for formulating an objective function to be optimized quantitatively. Although some metrics have been proposed in the literature to measure supplier performance, they are not developed specifically to integrate strategic decision making with quantitative optimization. The number of metrics also varies, ranging from 13 to 60 in different publications. The issue of configurability is often ignored. In this paper, we present a comprehensive set of metrics that are configurable based on a firm's business strategy. These metrics are arranged hierarchically to take into account product type (i.e., make to stock, make to order, or engineer to order), supplier type (i.e., local or global), and OEM/supplier integration level (i.e., no integration, operational integration, or strategic partnership). After briefly reviewing relevant literature in Section 2, the metrics development methodology is presented in Section 3. Section 4 discusses how the metrics can be configured for supplier selection. This is followed by an illustrative example in Section 5. Section 6 concludes the paper.
نتیجه گیری انگلیسی
The high correlation between supply chain strategy and business performance has been empirically demonstrated (Carter and Narasimhan, 1996). Firms now realize that their supply chain strategy must match their business model in order to be competitive and profitable. A sound business model must be based on market environment and customer demand, which are strongly influenced by product characteristics and its life cycle stage. For example, staples have a nearly constant design and their demand pattern is highly predictable. There is no technical barrier to entry to the staple production business. Therefore, profitability can only be achieved by minimizing cost and employing a level schedule across the entire supply chain. On the other hand, personal computers have a short product life cycle. At the introduction stage of a new model (e.g., IBM PCs in the 1980s), customer demand cannot be accurately forecasted. To maximize profitability, a responsive supply chain is needed that can quickly scale up and down production depending on customer acceptance. At the mature stage, competitive firms allow customers to configure their own computers over the internet and deliver the customized computers within days (e.g., present day DELL PCs). This requires an agile supply chain that emphasizes low volume high variety production and short lead time. It is obvious that no suppliers can be universally superior to the others under all circumstances. Rather, selection of the best suppliers must be driven by a firm's supply chain strategy, which is a high-level management decision. Researchers in Engineering schools (including Operations Research) overly emphasized the need of quantitative optimization and overlooked the importance of integration with business strategic thinking when it comes to supplier selection. The result is a large body of literature on different methodologies for supplier selection without a clear rationale for choosing an appropriate objective function to be optimized. It is our view that the large amount of decision-making methodologies presented in the literature is basically variations of optimization methods, AHP-based methods, MAUT-based methods, or outranking methods. Each of these methods has its pros and cons and the effort for improving them is certainly worthwhile. However, the more important issue is how to make sure that these methods are used effectively so decisions made indeed lead to the improvement of a firm's profitability. We believe the answer is a set of comprehensive metrics that can be selectively configured by management based on a firm's business model to guide quantitative optimization, as presented in this paper. The metrics we collected are by no means exhaustive, especially in today's rapidly changing world with continually evolving new business models. To meet their needs, firms may choose to add new metrics to the existing categories or even create new categories if necessary. The key is to configure a set of metrics that truly reflect a firm's business strategy. Firms should then critically evaluate their suppliers along these metrics and remain engaged with high performers to build competitive advantage (Bharadwaj and Matsuno, 2006). This will enable a firm to optimize its order management cycle, leading to improved customer satisfaction, receded interdepartmental problems, and improved financial performance (Shapiro et al., 1992).