Identification, evaluation and validation of lean, agile, resilience and green activities in remanufacturing using the IPPA and data mining approaches

Purpose – Nowadays, many industries, including the electrical and electronic equipment (EEE) industry, are facing significant environmental challenges. The remanufacturing process is an effective strategy for conserving resources and reusing them in subsequent production cycles, making it a key factor in reducing environmental impacts. Therefore, identifying lean, agile, resilience and green (LARG) activities in remanufacturing is essential for the growth of the remanufacturing industries, while this issue has been neglected in previous research. Design/methodology/approach – The aim of this paper is to identify and evaluate LARG activities in the remanufacturing process of the EEE industries in the US and Canada. Using fuzzy analytic hierarchy process (FAHP), fuzzy stepwise weighted ratio analysis (FSWARA) and the newly proposed method of importance-performance-productivity analysis (IPPA), 24 LARG activities were evaluated. Finally, the results were validated using data mining. Finally, a benchmarking index based on IPPA was introduced. Findings – According to the proposed IPPA method, eight octants were defined based on the importance, performance and productivity scores. It indicates that only the demand management activity is placed in the first octant and six activities (multi-skilled workers, total productive maintenance (TPM), customer relationship management, sustainable cost management, sustainable total quality management (TQM) and eco-responsive decision-making) with poor importance, performance and productivity are placed in the eighth octant. Originality/value – The manuscript presents a novel integration of fuzzy MCDM techniques (FAHP and FSWARA) with a newly proposed importance-performance-productivity analysis (IPPA) framework, specifically tailored for evaluating LARG activities in remanufacturing. Unlike prior studies, it uniquely combines qualitative prioritization with quantitative benchmarking and validation through data mining, offering a comprehensive and data-driven approach to improve sustainability practices in the EEE remanufacturing sector.