Measuring the performance sensitivity of replenishment systems using tradeoff curves
Bibliographic record
Abstract
There are a number of studies in the literature where replenishment systems have been compared on the basis of mean performance. However, little attention has focused on comparing systems on the basis of sensitivity to changes in the supply chain environment. This study compares the performance sensitivity of reorder point (ROP) and Kanban replenishment systems in a capacitated supply chain using an optimum-seeking simulation approach. Changes in the supply chain environment include transit time variability, transporter frequency, demand rates and lot setup times. Performance tradeoff curves, showing the interaction of inventory and delivery performance, are generated and an index based on the areas under the tradeoff curves is proposed to quantify the performance sensitivity. It is found that the Kanban system is generally less sensitive, in part because it operates optimally at a lower utilisation level. It is also observed that performance sensitivity depends on the environmental factor that is perturbed.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".