The effect of visibility on forecast and inventory management performance during the COVID-19 pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, healthcare facilities faced significant shortages of critical supplies like personal protective equipment, with dire repercussions. This study evaluates the potential role of decreased data visibility on these shortages, analysing different forecasting methods integrated with a periodic review inventory system (i.e. a base stock policy) on semi-simulated data encompassing several visibility issues. The forecasting methods chosen pertain to different data types: Holt and naïve methods are used as demand-based predictors, while a modified epidemiological model utilises pandemic data for demand forecasting. We scrutinise three prevalent data visibility issues through specifically crafted scenarios examining the effects of delayed, temporally aggregated, and erroneous data on system performance. Generally, our research illustrates that data visibility issues have a detrimental impact on the healthcare supply chain's efficacy. Interestingly, the system performance sees an uptick when these issues spur significantly oversized over-forecasts, e.g. when employing an epidemiological compartmental model for predictions.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".