Using simulation to evaluate a blood supply network in the Canadian maritime provinces
Bibliographic record
Abstract
Purpose The purpose of this paper is to evaluate the impact on customer service of amalgamating two production/distribution facilities in a blood distribution network, located in the Maritime region of Canada, into a single production facility and a satellite distribution facility. Design/methodology/approach Simulation models of the existing distribution network and the future distribution network were built. Experiments were conducted, using the two models, to compare the performance of each. Findings Results indicate that there is no evidence to suggest a decrease in customer service resulting from the consolidation of the two facilities. Furthermore, results indicate that this conclusion is robust with respect to lower inventory levels at the satellite and up to three road closures per annum. Research limitations/implications The results are based on specific operational assumptions regarding the number of hospitals supplied by the satellite facility and the methods used to transport blood products between locations. Social implications The results of this study have important implications for how vital blood products are distributed to patients in the Maritime provinces of Canada. Originality/value This paper is a case study describing a unique application of simulation methods to an important area of application. It will be of interest to readers interested in the management of blood supply chains and to researchers applying simulation methods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".