یک روش یکپارچه برای یافتن تامین کنندگان کلیدی در SCM
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|19171||2009||5 صفحه PDF||سفارش دهید||3450 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 36, Issue 3, Part 2, April 2009, Pages 6461–6465
Association rule is a widely used data mining technique that searches through an entire data set for rules revealing the nature and frequency of relationships or associations between data entities. Supplier selection is a significant work in supply chain management. Often, there will be thousands of potential suppliers and identifying a subset of these suppliers can be a complex process of determining a satisfactory subset based on a number of factors. In this paper, the supplier selection can be viewed as the problem of mining a large database of shipment. The proposed method incorporates the extended association rule algorithm of data mining with that of set theory to find key suppliers. This research has employed a numerical example for the integrated method to develop suitable supplier clusters. The results show that the method is effective and applicable.
In today’s ever-changing markets, many businesses move human resources, materials and information from some place to some other place throughout the world. Businesses around the world are attempting to position themselves to operate in a highly competitive marketplace. Gunasekaran (1999) indicates no single organization can respond quickly enough to the changing markets in a competitive environment of this type. Thus, competitive pressures over the past few years have promoted supply chain management (SCM) as one key strategy by which enterprises can make improvements to their business strategies (Fisher, Hammond, Obermeyer, & Raman, 1994). According to Simchi-Levi, Kaminski, and Simchi-Levi (2000), supply chain management is a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses and stores, so that merchandise is produced and distributed at the right quantities, to the right locations, at the right time, in order to minimize system-wide costs while satisfying service level requirements. The performance of the chain depends on every single organization involved. To attain a good performance for remaining competitive, the selection of an appropriate supplier can be important for a firm’s competitive advantage. Selecting the right suppliers can significantly improve corporate competitiveness. Developing a suitable approach for supplier selection is however a challenging research task. Often, there will be thousands of potential suppliers and identifying a subset of these suppliers can be a complex process of determining a satisfactory subset. AR can identify a subset of key suppliers. However, AR is a computationally and I/O intensive task. An exhaustive search over this exponential space is infeasible for anything except small values. Set theory can simplify complex processes of AR. In this research, the approach combining association rule (AR) with set theory is centered on developing suitable supplier clusters to find primary suppliers and secondary suppliers. The remainder of this paper is organized as follows. The supplier selection model and association rule relating with brief literatures are reviewed and discussed in Sections 2 and 3. An integrated method is derived and proposed in Section 4. A numerical example is used to demonstrate the proposed method in Section 5. Discussions are presented in the Section 6 and conclusions are in the last section.
نتیجه گیری انگلیسی
Developing a suitable approach for the supplier selection is however a challenging research task, although there will be thousands of potential suppliers. As explained earlier, the proposed model can be used in any specific supplier sorting problems. In this paper, the supplier selection can be viewed as the problem of mining a large database of shipment. The association rules are often applied MBA for establishing “aggregate” customer behaviors to optimize stores. However, ARM is a computationally and I/O intensive task. With given m items, there are 2m subsets that might potentially be identified. An exhaustive search over this exponential space is infeasible for anything, only if the value of m is small. The proposed method incorporates the extended association rule algorithm of data mining with the set theory to find key suppliers. The set theory can overcome an exhaustive search. The method proposed in this paper also establishes an available function which is capable of reflecting the different degrees of complementary to find potential supplier(s) to meet demands from other suppliers.