Resilient and Sustainable Supplier Performance Evaluation in Uncertain Conditions: A Novel Comprehensive Approach with Fuzzy Data Envelopment Analysis

Abstract

In today’s competitive world, serious attention is required to optimize costs, improve quality, sustain, growth, and develop companies, as well as manage risk. One crucial element in achieving these goals is the focus on the suppliers. Suppliers play a vital role in the supply chain, and it is essential to engage efficient suppliers for effective participation. Criteria derived from the organization’s mission are extracted and used for evaluation to select these efficient suppliers. Data envelopment analysis (DEA) is a linear programming-based method that enables the determination and assessment of the relative performance efficiency of various decision-making units. Fuzzy theory also plays a significant role in the DEA model, especially when quantitative data measurement is not feasible in certain conditions. This article presents a comprehensive method for evaluating and ranking decision-making units in uncertain situations, considering both optimistic and pessimistic perspectives, and accounting for different confidence levels. For the optimistic and pessimistic models, a two-pair model is employed to establish an interval efficiency, and the ranking is determined based on the geometric mean of the upper and lower bounds of the interval efficiency. The proposed model is tested and compared with two existing models in the literature using an example, highlighting the strengths and advantages of the proposed approach. Ultimately, The suggested model is utilized to assess the effectiveness of suppliers in the dairy industry based on the organization’s mission, leading to the identification of efficient suppliers. The results showed that the proposed model is uniquely capable of ranking the DMUs, is superior to the previous models, and obtains the optimistic and pessimistic value of the fuzzy number in an algorithm using the self-duality property.

Keywords

Fuzzy data envelopment analysis / interval efficiency / evaluation / geometric mean

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