A decision framework for selecting Industry 4.0 technologies in perishable goods reverse logistics

Purpose: This study aims to develop a structured decision-support framework to prioritize Industry 4.0 technologies (I4.0Ts) for reverse logistics operations in perishable-goods supply chains. These supply chains face unique challenges, including product perishability, unpredictable return flows, and the need for real-time monitoring and sustainability. Design/methodology/approach: A mixed-method approach was adopted, combining a systematic literature review with expert validation to define relevant evaluation criteria based on the Technology-Organization-Environment (TOE) framework, which was extended to include scalability and social acceptance dimensions. The Fuzzy Analytic Hierarchy Process (FAHP) was used to weight five main and 14 sub-criteria, and the Fuzzy TOPSIS (FTOPSIS) was applied to rank four I4.0Ts: IoT, Blockchain, AGVs, and Big Data. Findings: The results reveal that cost-effectiveness, scalability, and technological feasibility are the most influential criteria in technology selection. Among the evaluated alternatives, IoT and Blockchain emerged as the top-ranked technologies due to their strong alignment with the needs for traceability, monitoring, and operational responsiveness in perishable reverse logistics. Originality/value: This study proposes a comprehensive, criteria-driven framework specifically for evaluating and prioritizing I4.0Ts in the context of reverse logistics for perishable goods.