Trade Credit and Preservation Technologies: An Inventory Replenishment Model for a Sustainable Supply Chain
Bibliographic record
Abstract
Achieving sustainability within today's competitive environment is highly challenging.Therefore, we proposed a supply chain inventory replenishment model incorporating a finite planning horizon.So, to enhance their profit and lessen the total cost and carbon, this research study examines the investment in green (carbon offset) and preservation technologies.Additionally, we analyzed the trade credit duration granted by suppliers to the retailers.Carbon offsets/green technology represent a prevalent and significant measure to reduce carbon emissions.Time becomes a critical factor influencing demand rates in this context, while the degradation of materials affects a vast number of business sectors.Therefore, the cost of investing in preservation or green technology to control the deterioration of the materials, and reduce environmental emissions, the cost for ordering, the holding cost, and the replenishment cycle duration are all calculated.Consequently, a numerical iterative algorithm is prepared to identify the optimized solution for the supply chain approach for inventory control and management challenges.The optimality and uniqueness of the parameters of the proposed research study are furnished with a theoretical, mathematical, tabular, and pictorial analysis.Also, proposed research studies are provided with managerial implications that provide practical insights for industry practitioners.In conclusion, this research not only contributes valuable theoretical insights but also offers a tangible framework applicable to real-world scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".