Identification, evaluation and validation of lean, agile, resilience and green activities in remanufacturing using the IPPA and data mining approaches
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".