Assessing business impacts of agility criterion and order allocation strategy in multi-criteria supplier selection. Issue 3 (15th February 2015)

• Record Type: Journal Article
• Authors: Lee, Jaehun; Cho, Hyunbo; Kim, Yong Seog
• Is Part Of: Expert systems with applications. Volume 42:Issue 3(2015)
• Page Start: 1136
• Page End: 1148
• Publication Date: 2015-02-15
• Subjects: Supplier selection -- Agile supply chain -- Pareto fronts -- Bullwhip effect -- Fuzzy AHP -- Fuzzy TOPSIS -- Multi-criteria decision-making (MCDM); Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.08.041
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Formulate the supplier selection problem as a multi-criteria decision-making (MCDM) problem. Help decision makers to select the fittest suppliers for varying priority weights of multi-criteria. Quantify the importance of the agility criterion by measuring the magnitude of bullwhip effect. Visualize the operational and economic impacts of the agility criterion using the Pareto fronts. Compare resilience of agile and non-agile supply chains before and after unexpected disruptions. Abstract: This paper formulates supplier evaluation and selection as a multi-criteria decision-making (MCDM) problem with subjective and fuzzy preferences of decision makers over evaluation criteria. As an outcome, this paper provides decision makers with a decision support system that presents the Pareto fronts, a set of best possible high-quality suppliers and optimized business operation levels from such suppliers. In addition, this paper quantifies the importance of the agility criterion and its sub-criteria in the process of evaluating and selecting agile suppliers by measuring the magnitude of bullwhip effect and inventory costs. The proposed system uses a fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) to successfully determine the priority weights of multiple criteria and selects the fittest suppliers by taking the vagueness and imprecision of human assessments into consideration. More importantly, it presents approximated Pareto fronts of the resulting supplier chains for varying priority weights of the agility criterion and its sub-criteria. Finally, we compare business costs of agile and non-agile supply chains before and after reconfigurations of original supply chains in response to unexpected disruptions under two order allocation strategies, a skewed order allocation (SOA) strategy and an even order allocation (EOA) strategy.