ارزیابی برنامه های توسعه تامین کننده با متدولوژی مبتنی بر مجموعه خشن خاکستری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|21311||2011||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 11, October 2011, Pages 13505–13517
Supplier performance management and continuous improvement play an important role for organizational and supply chain development. Many broad-based empirical studies have provided insights into the relationships of supplier development practices to supplier performance. Yet, specific tools available for organizations to internally evaluate such relationships are limited. To help further the research and development of decision tools in this area, we introduce a multi-method approach relying on grey system theory and rough set theory that can help organizations identify the important practices and programs related to suppliers’ performance. Our contribution includes introduction of a methodological approach for evaluating supplier development programs and identification of effective programs and relationships. Implications and results interpretation of the methodology, limitations and future research directions, further expanding the methodology and its applications, conclude the paper.
Supplier management and development have been critical issues for organizational strategic and competitive advantage. As globalization, outsourcing, and core competency management philosophies become more pervasive the supplier–buyer relationship has become more central to organizational strategy. Managing the performance of suppliers and furthering their continuous improvement efforts is no longer a luxury and feel-good measure, but is a necessary investment for the long-term survival of organizations. Supplier development approaches and practices have been extensively proposed and documented in the literature. These studies incorporate and investigate a broad variety of supplier development programs. Organizations do not view these programs as trivial operational activities and may invest significant resources in them. Yet, not all supplier development programs contribute equally to the development and improvement of a particular organizational supply chain and supply partners. Aiding organizations to more easily identify effective supplier development programs can save time, money and resources. Numerous studies have sought to determine which practices and programs effectively contribute to supplier development. Many of these studies are broad-based and provide insights into the relationships of these programs to supplier performance. But, these general theoretical relationships will vary depending on idiosyncratic industry and organizational characteristics. Thus, there is significant opportunity for formal models and decision support and expert system tools that organizations can utilize to help them improve and systematize their supplier development and improvement practices and programs (Gunasekaren et al., 2000 and Simatupang and Sridharan, 2004). Interestingly, a critical aspect of strategic supplier development is the use of formal models and data driven approaches to help identify suppliers that need development support and the types of practices to include (Gunasekaren et al., 2000 and Hartley and Choi, 1996). Yet, in industry it has been found that the level of implementation of these formal modelling practices to identify and manage supplier development programs has been very limited (Krause, Handfield, & Scannell, 1998). Researchers have also lagged in development of formal supplier development models (albeit supplier selection models have proliferated over the past two decades). Even today the extant literature has not considered any prioritization to address the “locus of investments” for supplier development initiatives (Narasimhan, Mahapatra, & Arlbjørn, 2008). To help further the research and development of formal modelling and decision aid tools in this area, we introduce a multiple stage multi-method approach that can help organizations identify which organizational practices and programs relate to suppliers performance. This unique methodology integrates grey system with rough set theory. The methodologies, one used for intangible decision making (grey system), and one used for data mining (rough set) purposes, utilize previous performance results and practices that were implemented for given suppliers. With this methodology, the existence of quantitative, clearly defined data is not a necessity. Various types of captured data, qualitative, quantitative, tangible, intangible, and perceptual can be analyzed. The paper contributes to the literature by jointly linking these two techniques introducing and developing an approach for an underserved application area that is critically important to industry and supply chain management researchers. The practical outcome of this modelling approach provides organizations insights into supplier development practices and programs in which they should invest their available resources. We begin by identifying previous research within the supplier development field to help identify important practices and programs used to aid suppliers. We also briefly identify some formal models in this area and provide an overview comparison between these models and the methodology proposed here. The two basic methodologies, grey systems and rough set theory are introduced with some general principles provided as background. An illustrative case application example sets the stage for describing the methodology in detail. Evaluation of the results in a follow-up discussion provides some practical interpretation. A final summary and conclusion incorporates additional discussion and identifies limitations and future research directions.
نتیجه گیری انگلیسی
In this paper, using grey systems and rough set theories we have introduced a multi-stage methodological approach for evaluating supplier development programs in an organization. This methodology can easily become a valuable element of a decision support tool for supply chain managers and organizational performance measurement and benchmarking programs. It can also help organizations more effectively plan, design, and manage their supplier development and continuous improvement programs. It can be used to identify problem programs as well as encourage further participation in and development of successful programs. Many different types of supplier development programs exist for organizations. The identification of effective supplier development programs may be prudent for organizations that may be investing a large portion of their managerial budgets on monitoring and managing suppliers. This tool can aid in the dispersal of scarce resources for such programs. This tool and effort represents one of the first to explicitly address these issues from a formal modelling perspective. We base most of the development of this technique on rough set theory. The main advantage of rough set theory in data analysis is that it does not need any preliminary or additional information about data − like probability in statistics, or grade of membership or the value of possibility such as in fuzzy set theory (Pawlak, 1991). Even with practical and theoretical advantages the technique certainly has numerous limitations. At the end of the process, we arrive at a number of rules, which, when applied are meant to provide us with relative certainty on relationships. But, the various rules do not have significance attached to them. All rules are viewed as equally likely to occur. This limitation can be mitigated by developing some significance statistics, utilizing probability values that can identify strength of relationship and certainty of factors and functions (e.g. see Pawlak, 2002). Another basic limitation is the use of grey based scales that may provide a more continuous result in outcomes. For example, we provided ranges for normalized, average performance outcome measures. These ranges may vary and the determination is relatively arbitrary. Finding more theoretical or practical reasoning for normalization of outcomes is something that needs to be investigated. In this paper we made the assumption that all the data was complete. The presence of incomplete data is a realistic situation. Advances exist in rough set approaches, with some minor variations to our technique may benefit from integrating techniques to evaluate incomplete information. In the reduction categorization of types we only focused on the conditional attributes (supplier development programs) integration of decision attributes (performance outcomes) may also be used in these approaches. One of the greatest limitations of this particular study is that we have only developed a conceptual set of data to describe our technique. A real-world application validation may provide additional insights to help advance this methodology. We have provided some directions for future research and development with this methodology. Additional consideration would be to apply this technique to a broader array of performance measurement systems within organizations. We only considered performance measurement associated with supplier development programs. There are a number of internal performance measurement applications ranging from performance of products in marketing, to performance of personnel in human resources to performance of equipment in operations. A number of avenues for a broadened application of rough set and grey based methodologies to organizational performance measurement systems is both feasible and should be encouraged.