A Model for Designing and Evaluating LARG-Based Supply Chain using Axiomatic Design and the Best-Worst Method in a Hesitant Fuzzy Environment

There have been various approaches to the supply chain such as lean, agile, robustness, sustainability, resilient, and green that each one focuses on supply chain from specific aspect. One of the new approaches to the supply chain is an integration of Lean, Agile, Resilient, and Green (LARG) that benefiting from the advantages of different approaches and avoiding their disadvantages. The present study proposes a model to design and evaluate LARG-based supply chain in the Iran automotive industry using the concept of Axiomatic Design (AD) in a Hesitant Fuzzy (HF) environment. The study process consisted of two stages: designing stage and evaluating stage. In the first stage, the Functional Requirements (FR) and chain Design Parameters (DP) identified in the LARG supply chain based on the Delphi technique and literature review. Based on independence axiom, it should be considered that whether the satisfaction of one FR by the related DPs affects the quality of the other FR or not, which is examined based on the design matrix. In the second stage an integration of information axiom, the Best-Worst Method (BWM), and hesitant fuzzy logic was used to evaluate four supply chains in Iran automotive industry. The weight of supply chain criteria, the utility rate of desired supply chain criteria, and the current status for each supply chain criteria identified in this stage. The results indicated that the excellent LARG supply chain was consisted of 13 indicators. The model also revealed that the excellent supply chain was contained less information axiom and complexity.