An In-Depth Analysis of Contingent Sourcing Strategy for Handling Supply Disruptions
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we consider a make-to-stock producti-on-inventory system where a manufacturer's production may be entirely interrupted due to a supply disruption. Customers react dynamically to the subsequent inventory shortage, depending on factors including market condition, customer characteristic, and behavioral interaction. The manufacturer can adopt contingent sourcing to manage the disruption. Consequently, the postdisruption demand and inventory exhibit complicated dynamics in terms of customer behavior, demand recovery, and the adoption of contingent sources. We first model and forecast the postdisruption customer behavior. Customers are classified into two types based on brand loyalty and the interaction is captured as “demand learning” within each type. Using differential models, we analytically characterize customers' postdisruption behavior in five possible scenarios, depending on customers' constitution, transient reaction, brand loyalty, and competition intensity. Next, we propose dynamic contingent sourcing strategies to mitigate the supply disruption, and the optimal sourcing time is derived. Finally, by conducting numerical analysis, we obtain further managerial insights on how to adapt dynamic contingent sourcing strategies according to various contributing factors.
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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 | 0.002 | 0.002 |
| 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 it