An integrated fuzzy-goal-programming-based framework for selecting suppliers in strategic alliance formation

There has been an exponential growth in the number of strategic alliances formed in the last decade between manufacturers and their suppliers. Yet 60-70% of these alliances fail in their first year. One of the reasons for such failure is the incompatibility of members of the alliance. Hence, there is a need for a tool to help decision makers to rate the compatibility of potential partners in a strategic alliance. This paper fulfills this requirement by presenting an integrated method for rating the compatibility of potential members of a strategic alliance. The method utilizes a model based on fuzzy logic/goal programming to analyze the vague, imprecise, and usually subjective information regarding the compatibility of potential suppliers that is available during the early formation of a strategic partnership. A sample case study is presented to demonstrate the application of the framework.

[1]  S. H. Ghodsypour,et al.  Fuzzy multiobjective linear model for supplier selection in a supply chain , 2006 .

[2]  Seyed Hassan Ghodsypour,et al.  A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming , 1998 .

[3]  V. Lo,et al.  Practical framework for strategic alliance in Pearl River Delta manufacturing supply chain: A total quality approach , 2004 .

[4]  T. Nishiguchi,et al.  Strategic Industrial Sourcing: The Japanese Advantage , 1994 .

[5]  Gin-Shuh Liang,et al.  Using fuzzy MCDM to select partners of strategic alliances for liner shipping , 2005, Inf. Sci..

[6]  David C. Treadway.  Before It's Too Late , 1989 .

[7]  Eric W. K. Tsang.  Motives for strategic alliance: A resource-based perspective , 1998 .

[8]  W. H. Hoffmann,et al.  Success Factors of Strategic Alliances in Small and Medium-sized Enterprises—An Empirical Survey , 2001 .

[9]  Daniel J. McAllister.  Affect- and Cognition-Based Trust as Foundations for Interpersonal Cooperation in Organizations , 1995 .

[10]  Sean P. Willems,et al.  Supply Chain Design: Safety Stock Placement and Supply Chain Configuration , 2003, Supply Chain Management.

[11]  Chen-Tung Chen,et al.  A fuzzy approach for supplier evaluation and selection in supply chain management , 2006 .

[12]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[13]  R. Handfield,et al.  Success factors in strategic supplier alliances: The buying company perspective , 1998 .

[14]  Robert E. Spekman,et al.  The dark side of alliances:: Lessons from Volvo–Renault , 1998 .

[15]  I. Bitran,et al.  SMART: System for the development, management and support of strategic alliances , 2002 .

[16]  J. Hsuan.  Impacts of supplier–buyer relationships on modularization in new product development , 1999 .

[17]  R. M. Monczka,et al.  Global Sourcing: A Development Approach , 1991 .

[18]  A. Gaballa.  Minimum Cost Allocation of Tenders , 1974 .

[19]  Lynn Isabella,et al.  Creating strategic alliances which endure , 1996 .

[20]  J. Current,et al.  A MULTIOBJECTIVE APPROACH TO VENDOR SELECTION , 1993 .

[21]  C. C. LI,et al.  A new measure for supplier performance evaluation , 1997 .

[22]  Yves L. Doz,et al.  Rescuing troubled alliances... before it's too late , 2000 .

[23]  M. Y. Yoshino,et al.  Strategic Alliances: An Entrepreneurial Approach to Globalization , 1995 .

[24]  Bimal Nepal,et al.  Integrated fuzzy logic-based model for product modularization during concept development phase , 2005 .