Competitive advantage is often determined by the effectiveness of an organization's supply chain, and as a result, the evaluation and selection of suppliers has become an increasingly important management activity. But the evaluation process is complex. The data that must be considered are both technical and social/organizational. Much of the data are difficult to obtain and ambiguous or vague to interpret. In addition, the dynamic global environment of changing exchange rates, economic conditions, and technical infrastructure, demand that the pool of potential suppliers be re‐evaluated periodically. Nonetheless, a rational process of evaluation must exist to select the most appropriate suppliers. This paper addresses one dimension of the evaluation process, the information sharing capability of potential supply chain partners. It is an especially important dimension since information technology is necessary to horizontally integrate geographically dispersed operations. Fuzzy logic, a subset of artificial intelligence, together with analytical hierarchy process is used to model this process and rank potential suppliers. It is an appropriate methodology to use for this application and has the potential to be used with other supply chain design decisions since it explicitly handles vague, ambiguous, and imprecise data.
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1 November 2003
Research Article|
November 01 2003
Evaluating the information sharing capabilities of supply chain partners: A fuzzy logic model
Barry Shore;
Barry Shore
Whittemore School of Business and Economics, University of New Hampshire, Durham, New Hampshire, USA
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A.R. Venkatachalam
A.R. Venkatachalam
Whittemore School of Business and Economics, University of New Hampshire, Durham, New Hampshire, USA
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Publisher: Emerald Publishing
Online ISSN: 1758-664X
Print ISSN: 0960-0035
© MCB UP Limited
2003
International Journal of Physical Distribution & Logistics Management (2003) 33 (9): 804–824.
Citation
Shore B, Venkatachalam A (2003), "Evaluating the information sharing capabilities of supply chain partners: A fuzzy logic model". International Journal of Physical Distribution & Logistics Management, Vol. 33 No. 9 pp. 804–824, doi: https://doi.org/10.1108/09600030310503343
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