Impact of inventory cannibalisation on a retailer selling substitutes
Bibliographic record
Abstract
The reasons customers substitute, are well understood from an economic perspective. However, its exact impact on retailer's inventory and profit is not, when customers substitute as a result of a shortage. Shortage of one product may lead to demand spillover, due to substitution, resulting in shortages for the second product. We call this inventory cannibalisation. This is a store level, retailer observed phenomenon that is a direct result of customers' willingness to switch between substitutes due to stockout of one product. Many retailers are experiencing stockouts related to substitution, resulting in a significant loss of revenue. We model a retailer's selling two substitutes, facing price-sensitive stochastic demand. Our model incorporates cannibalisation explicitly and generalises the existing literature on inventory substitution. We perform analytical and numerical analysis to study the impact of stockout-based substitution and the related inventory cannibalisation on retailer's decisions. We find that the impact of cannibalisation is felt most acutely by the retailer for products with low degree of substitution.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".