Synergies and Trade-Offs between Lean-Green Practices from the Perspective of Operations Strategy: A Systematic Literature Review
Bibliographic record
Abstract
In the operations management and sustainability literature, the integration of Lean and Green manufacturing is considered one of the great solutions to balancing operational gains and environmental sustainability. This literature focuses mainly on the integration between them. However, there are no studies investigating how this integration is related to the Operations Strategy content: competitive priorities and decision areas. Thus, this study aims to contribute to reducing this research gap by providing a more in-depth understanding of the relationships between Lean-Green practices from the point of view of the Operations Strategy. We identify synergies and potential trade-offs between competitive priorities and changes in decision areas when Lean-Green practices are implemented. We performed a systematic literature review to answer two questions: Does the implementation of Lean and Green practices affect operations’ competitive priorities, causing synergies or trade-offs? What decision area(s) are modified with the implementation of each practice? This systematic review analyzed 338 selected articles. Competitive priorities, decision areas, Lean practices, Green practices and Lean-Green practices were identified and discussed, highlighting trade-offs, synergies and changes in decision areas. The results suggest that Lean and Green are synergistic in most practices, but they must be managed according to the Operations Strategy, especially as their focuses are essentially different and trade-offs may occur.
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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 | 0.020 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".