On the value of response time characteristics in robust design of supply flow
Bibliographic record
Abstract
Purpose – The purpose of this paper is to provide a decision-making tool achieving robust supply flow by incorporating strategic stock and contingent sourcing in mitigating minor and major disruptions. Design/methodology/approach – The authors consider a firm with two suppliers where the main supplier is cost-effective but prone to disruptions and the back-up supplier is reliable but expensive due to built-in volume flexibility. In order to incorporate the randomness associated with disruptions and the available capacity during response time in the decision-making stage, the authors present a multi-stage robust optimization (RO) model. The design problem is to determine optimal strategic stock level and response speed of volume-flexible back-up supplier in order to achieve a robust supply flow. Findings – The results show that the quality of optimal solution is improved by considering the randomness associated with available capacity. In addition, incorporating congestion effects allows identifying the appropriate level of supply chain responsiveness, thus improving the overall performance. Originality/value – The novelty of the proposed model is the consideration of both strategic stock and volume flexibility in maintaining a robust supply performance while incorporating response capability and congestion effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".