Analysis of the LARG of the hospital medical equipment supply chain using the fuzzy inference system - Ramin Pabarja; Gholamreza Jamali; Khodakaram Salimifard; Ahmad Ghorbanpur (International Journal of Research in Industrial Engineering)
The Lean, Agile, Resilience, and Green (LARG) supply chains are more competitive than conventional ones. Evaluating its performance under current conditions and developing suitable strategies is crucial to enhance LARG. This study aims to create an assessment model for LARG in Iran's hospital medical equipment supply chain, especially in Hamadan. The Fuzzy Inference System (FIS) evaluates LARG across four dimensions: lean, agile, resilient, and green. Key indicators obtained from a comprehensive review of the literature and other published reports in the field of LARG were also confirmed by a focused group of experts in the medical equipment supply chain field. The findings indicate that the value LARG of the medical equipment supply chain is 0.787. Key indicators for the evaluation of LARG in the hospital medical equipment supply chain include reducing overall supply chain costs, optimizing inventory management, shortening supply chain development cycle time, increasing the introduction of new products, promoting information sharing among supply chain members, establishing flexible supply bases and sourcing, reducing fossil fuel consumption, and implementing waste management practices such as reuse and recycling of recyclable materials. This research provides managers with valuable insights into the current state of LARG and serves as a reference for formulating LARG strategies and practices. The study's results enable supply chain actors, particularly in Iran's Hamadan Province, to comprehend the key indicators for improving LARG performance in the hospital medical equipment supply chain. The proposed model can be adapted to other industries and service sectors by adjusting the indicators and assessing data availability.