Disruption Risk Management in Serial Multi-Echelon Supply Chains
Bibliographic record
Abstract
This research focuses on managing supply chain disruption risk using inventory and reserve capacity in serial multi-echelon supply chains. The research problem is to determine the optimal risk mitigation inventories and reserve capacities when product transformation occurs at each echelon. Disruptions at each echelon are modeled as a random process. We derive insights on the optimal location and quantity of risk mitigation inventory (RMI) and reserve capacity held in serial supply chains. We show that the downstream echelon typically holds at least as much RMI as the upstream echelon. At the same time, it is often optimal to hold additionally more reserve capacity downstream than upstream. These results hold under the assumption that inventory and reserve capacity holding costs are larger downstream than upstream. Our research also suggests that RMI is preferred to reserve capacity as a risk mitigation lever in long serial supply chains, i.e., in supply chains with a large number of echelons. This research problem is inspired by a risk management problem of a leading pharmaceutical company.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".