Performance Indicators for Sustainable Remanufacturing Closed-Loop Supply Chains
Bibliographic record
Abstract
Abstract Québec is transitioning to a circular economy (CE) by promoting the implementation of CE strategies, such as remanufacturing. However, the adoption of remanufacturing practices to achieve sustainable implementation in an enterprise is demanding and highly challenging. It requires balancing economic, environmental, and social dimensions, and guaranteeing products’ remanufacturability and system circularity along closed-loop supply chains (CLSC). Key performance indicators (KPI) emerge as decision-support tools for decision-makers to control and enhance system performance. Nevertheless, the multidimensional nature of sustainable remanufacturing makes it challenging to determine suitable KPIs to employ. Therefore, this study performs a systematic literature review to identify the main KPIs in sustainable remanufacturing and its scope along its CLSC. A total of 100 documents from the Scopus database were analyzed to reveal the most frequently used 42 key performance indicators (KPI), categorized as 25 economic, 14 environmental, and 3 social-related indicators. The KPIs were distributed among the different CLSC actors, providing insights on selection of the most useful KPIs to consider for each CLSC actor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".