Performance measurement of reverse logistics enterprise: a comprehensive and integrated approach
Bibliographic record
Abstract
Purpose Reverse logistics (RL) has gained considerable attention in the literature. The first objective of this study is to develop a comprehensive performance measurement (PM) framework and scorecard for RL enterprise. The second objective is to integrate analytical hierarchy process (AHP) approach for RL PM. Design/methodology/approach The present work presents understanding RL performance and proposes a conceptual comprehensive reverse logistics PM framework and scorecard for managing RL enterprise. The framework developed in the paper is based on an extensive review of literature on RL, PM frameworks such as Balanced Scorecard and performance prism. It is further supported by AHP for calculation of overall comprehensive performance index (OCPI). Findings The scorecard consists of six performance perspectives, as well as key performance measures. The relevance of these perspectives, especially from the reverse logistics viewpoint, has been authenticated. With respect to each perspective, measures have been proposed that efficiently and effectively address the vital facets of an enterprise's business excellence. The paper further proposes a method to prioritize the different performance levels using AHP methodology. It also suggests an OCPI of the enterprise reflecting its relative position and benchmark in the industry sector. Practical implications This study provides a comprehensive PM system and scorecard for measuring and managing RL performance. The integrated AHP methodology developed provides useful guidance for practical managers in evaluation and measuring of RL in a complete and holistic way. Originality/value This paper proposes a comprehensive PM system and scorecard for RL. While suggesting scorecard, different performance measures have been assigned into six different perspectives. The OCPI has been calculated and prioritized performance measures are determined to focus on for continuous improvement.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".