Inventory management of perishables under Zero-Inflated Poisson demand
Bibliographic record
Abstract
Blood supply chains involve challenges of perishability, variability in demand, and costs of wastage and shortage, making the management of perishable inventory in blood supply chains particularly complex. Zero-inflation and highly variable demand patterns further complicate inventory management; however, the Zero-Inflated Poisson (ZIP) model provides a suitable framework for capturing these characteristics effectively. In this paper, we investigate how demand characteristics impact inventory performance and suggest strategies to maximize cost efficiency. This research evaluates the classical reorder-point/order-up-to-level (s,S) inventory policy under ZIP demand for a specialized product called Low-Titer O group Whole Blood (LTOWB). A ZIP model is fit to Canadian demand data and embedded in a GPU-accelerated stochastic dynamic program that yields the minimum expected total cost across a 14-day shelf-life. Discrete-event simulation benchmarks the best (s,S) rule against this optimum. Results show a distinct operating threshold: When expected daily demand exceeds one unit per day, the (s,S) policy costs significantly lower, with the small residual driven by shortage and wastage penalties. The identified threshold supplies a practical screening rule: pool demand or redirect elective usage to increase expected demand past one unit and safely employ a (s,S) policy, while advanced optimization tools must remain reserved for facilities with persistently sparse demand.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".