Performance measurement of sustainable supply chains: a review and research questions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it