Closed-loop Supply Chain Games with Innovation-led Lean Programs and Sustainability
Bibliographic record
Abstract
This paper studies the impact of some innovation-led lean programs in a Closed-loop Supply Chain(CLSC)setting. We use a game-theoretic approach to model a CLSC composed of one supplier and one manufacturer. The supplier sets the wholesale price of an intermediate product while the manufacturer sets the selling price of a final product. Further, the manufacturer invests in innovation-led lean practices to entail both a strategic effect and a process innovation effect. The strategic effect consists of responsiveness involving the CLSC's capacity to properly respond to consumers' needs and leading to increase in sales. Further, the strategic effect enhances sustainability as consumers align their behavior to the CLSC's attitude of reducing the waste through lean, thus using their products for longer time period and entirely exhausting their residual value. Innovation-led lean practices also generate a process innovation effect, which consists of the marginal production cost abatement. Our findings indicate that lean practices leading to both strategic and process innovation are profitable for the manufacturer and sponsor sustainability. When only one of those can be presented, CLSCs should prefer the adoption of a strategic lean program. From its side, the supplier is much less sensitive to environmental benefits, thus it focuses on sales and operational matters. Furthermore, in a centralized CLSC, the preferences for strategic vs. process innovation lean follow the same path of the decentralized CLSC. Nevertheless, we pinpoint that the manufacturer in the decentralized CLSC has a larger incentive to adopt a strategic lean program than in the centralized CLSC. Also, the supplier always obtains larger economic benefits in the decentralized CLSC under any type of innovation-led lean program.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".