Enhancing Retail Sustainability: Data-Driven Approach for Food Waste Detection and Prevention
Bibliographic record
Abstract
Food waste represents a significant challenge within the retail supply chain, leading to considerable economic and environmental impacts. Traditional methods often fail to capture the complex, non-linear interactions between key drivers of food waste, resulting in less effective management strategies. This study aims to improve the performance of food waste detection by employing machine learning (ML) algorithms, including tree- based algorithms, neural networks, and regression models. We assess the ML models on a dataset that includes 33 temporal, meteorological, and operational features from a multi-branch grocery in a mega city. Furthermore, this research utilizes SHapley Additive exPlanations (SHAP) values for a comprehensive feature importance analysis. The results show that models such as CatBoost and ID CNN outperform traditional methods. These models provide more reliable detection and valuable insights into the factors related to food waste. We also identify the most impactful features in waste generation, providing valuable insight for retailers in sup- ply chain planning. This practical implication offers actionable feedback for improving sustainability and operational resilience in the retail supply chain.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".