A fuzzy group Electre method for electronic supply chain management framework selection
The rapid growth of the Internet and electronic commerce has given rise to electronic supply chain management (e-SCM) which enhances communication, coordination, and collaboration between the trading partners. However, when confronted by the range of e-SCM frameworks, organisations struggle to identify the one most appropriate to their needs. In this paper, we propose a novel fuzzy group multi-criteria method for e-SCM framework evaluation and selection. The contribution of the proposed method is fourfold: (1) it addresses the gaps in the SCM literature on the effective and efficient assessment of the e-SCM frameworks; (2) it is grounded in the excellence model introduced by the European Foundation for Quality Management; (3) it provides a comprehensive and systematic framework that combines real options analysis and the analytic hierarchy process with a group Electre method; and (4) it considers fuzzy logic and fuzzy sets to represent ambiguous, uncertain, or imprecise information. We also present the results of a real-world case study to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and the algorithms. We show that the proposed framework can help a group of decision makers to think systematically by decomposing the alternative evaluation process into manageable steps and integrating the results to arrive at a solution consistent with managerial goals and objectives. The analysis of this case study allows for the articulation of a series of key factors that can be considered important in contributing to the successful implementation of the e-SCM framework. The first element is getting the key people on board. The second factor is building internal alliances. The third key ingredient is the persistent and systematic process put in place to evaluate the e-SCM success.
Zandi, Faramak; Tavana, Madjid; and Martin, David, "A fuzzy group Electre method for electronic supply chain management framework selection" (2011). Business Systems and Analytics Faculty Work. 266.