Drivers of Product Expiration in Consumer Packaged Goods Retailing
Bibliographic record
Abstract
Product expiration is an important problem in the consumer packaged goods (CPG) industry costing 1%-2% of gross retail sales and eroding industry profits substantially. It can be caused by several factors related to store operations, supply chain practices, and product characteristics. Existing methods used in the industry are inadequate to identify the causes of expiration, leading to inadequate efforts to reduce expiration. Using retail data for 768 SKUs and 10,000 stores (745,638 store-SKU-level observations), as well as upstream supply chain data from a CPG manufacturer, we show the extent to which expiration of products in retail stores is driven by case size, inventory aging in the supply chain, minimum order rules, manufacturers' incentive programs for the sales force, replenishment workload, and many control variables. A counterfactual analysis based on the model shows that our subject manufacturer can reduce expiration by up to $38.82 million per year by implementing four selected initiatives involving case size, supply chain aging, minimum order rules, and sales incentives. Further, targeted initiatives can be designed using combinations of these variables for subsets of products with the highest occurrence of expiration.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".