A collaborative scenario-based decision model for a disrupted dual-channel supply chain
Bibliographic record
Abstract
Purpose Distribution systems usually utilize both traditional retailing channels in conjunction with e-channels. The purpose of this paper is to investigate a dual-channel supply chain, comprising a traditional retailing channel and an e-channel under disruption. By benchmarking against the centralized decision structure, the authors intend to propose a collaboration model to achieve channel coordination as well as more reliable decisions. Design/methodology/approach Four different channel disruption scenarios, with customers’ reaction toward disruptions, are examined, and then, optimal pricing decisions for both centralized and decentralized decision-making structures are extracted. Next, a collaboration mechanism based on the dominancy power of channel members is developed to entice all channel members to participate in channel coordination. By benchmarking the proposed collaboration model against both the decentralized/centralized structures a win–win solution is guaranteed for all channel members. In addition, the proposed model ensures more reliable decisions than the centralized structure, as it guarantees less fluctuated income levels. Findings This study shows, as the disruption probability grows, the channel profit decreases while the channel-retailing price increases. Furthermore, the exact alignment of the centralized decision-making approach and the proposed collaboration model is not achievable due to the problem infeasibility. Numerical experiments and sensitivity analyses benchmark the performance of the proposed collaboration mechanism against the centralized structure for the full alignment with centralized decision-making approach. Originality/value This study contributes to the channel conflict literature as jointly considers pricing decisions, disruptions and coordination. Further, consumers’ reaction toward disruption is analyzed through a transshipment agreement.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".