Performance measurement of sustainable supply chains: a review and research questions
Bibliographic record
Abstract
Purpose – One of the hurdles to the adoption of sustainable practices across supply chains is the lack of pan-chain performance measurements and their related information and organizational structures. The authors review the literature on performance measurement of sustainable supply chains with a focus on comprehensive measures that include multiple supply chain partners as well as different sustainability aspects. The purpose of this paper is to analyze the reviewed literature and propose some research questions. Design/methodology/approach – The authors reviewed 140 journal articles, cases and reports that appeared since 1994. Findings – The authors classify the reviewed literature according to seven sustainability dimensions (economical, environmental, social, reputable, valuable, equitable and sustainable) as well as the type of industry and methodology used. In addition the authors synthesize the available performance measurements into a comprehensive framework that incorporates different stages of the supply chain operations and decision-making processes. Social implications – The results of this study can be used by researchers to focus on research that may have more implications on supply chains. Practitioners can use the authors proposed performance measurement framework for developing practical and comprehensive measures for their respective industries. Originality/value – The work is original in the way the authors integrate sustainability (seven dimensions) across the supply chain taking into account the type of operational decisions. The framework can be used by researchers and practitioners to develop practical sustainability performance measurement systems for supply chains.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".