Inventory Management with Advance Booking Information: The Case of Surgical Supplies and Elective Surgeries
Bibliographic record
Abstract
Problem definition: Medical operations require a large volume and variety of consumable supplies that are kept in hospital inventory and replenished on a regular basis. Stringent requirements on the availability of these supplies, together with high variability in their daily usage, contribute to the high inventory costs of the surgical departments in hospitals. We investigate the value of utilizing Advance Booking Information (ABI) on elective surgeries—which are often booked up to months in advance—in reducing inventory costs. Methodology/results: We study a single-item, periodic-review, stochastic inventory control problem, where the item demand in each period is driven by the number and type of surgeries requiring the item, and with the available information on elective surgeries integrated into the ordering decisions. Given that item usage from each case is uncertain and only realized after the surgery, ABI provides imperfect information on future demand. Through exact analysis of a simplified version of the problem, as well as extensive numerical experiments using synthetic and real data, enabled using a state aggregation technique, we provide insights on and quantify the value of using ABI as a function of the number of periods of ABI integrated into the ordering decisions. We identify a relevant parameter regime—namely, high backlog (relative to holding) costs and when surgeries are booked sufficiently in advance—where the value of using ABI could be significant and the majority of the benefits can be gained through incorporating only one period of ABI beyond the order lead time. In a case study conducted using real data, we observe up to 26% reduction in average inventory levels, without violating the service levels. Managerial implications: By incorporating readily available elective surgery schedules into replenishment decisions of surgical supplies, hospitals could significantly reduce inventory costs without compromising the availability of the supplies. Funding: This work was partially funded by The Ontario Ministry of Government and Consumer Services (MGCS). The views expressed in the paper are the views of the authors and do not necessarily reflect those of the Province. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0063 .
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".