This research is partially supported by NSERC grant OGP0005527
Bibliographic record
Abstract
This paper reports results of a study of inventory policies for Canadian Tire Pacific Associates which operates 21 retail stores and a warehouse in the vicinty of Vancouver, British Columbia and maintains stock of over 30,000 products. We describe a periodic review, fixed lead time, singleproduct, single-facility model with random demand, lost sales and service constraints which we developed for potential application. The model utilizes empirical demand data to calculate the long run average cost of inventory and service level for a given (s,S) policy. We provide a search algorithm to quickly locate an optimal policy based on an updating scheme for the transition probability matrix of the underlying Markov chain, bounds on S and monotonicity assumptions on the cost and service level functions . We compare the computed policies to those currently in use on a test bed of 420 products and find that stores currently hold inventories which are 40% to 50% higher than those determined by our model. We estimate that implementing the proposed policies for the entire system would result in annual savings of between $5.5 and $7 million.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".