Designing a Transportation-Strategy Decision-Making Process for a Supply Chain: Case of a Pharmaceutical Supply Chain
Bibliographic record
Abstract
Including an active participation of stakeholders along the transportation decision-making process is increasingly recognized as a necessary condition for reaching successful and high-quality decisions. This paper presents a framework for deciding on the appropriate transportation strategy for a supply chain from a multistakeholder perspective. It consists of three steps: (1) defining the transportation-strategy decision-making context and the objectives that must be achieved; (2) analyzing the actual transportation strategy regarding its three components: transportation network; transportation mode; and transportation insource/outsource, as well as identifying the stakeholders interested in the study; and (3) conducting a group decision making regarding each transportation strategy's component, while involving the key stakeholders and taking into account the specificities of transported products. The proposed framework is then applied to a real case of the Moroccan public pharmaceutical supply chain, which has different features that distinguish it from other supply chains including its importance, urgency, and regulation. We employed the DELPHI method to determine the key stakeholders that should be involved in the decisional process. After that, we applied the group AHP method for selecting the appropriate transport-network design option while involving the identified key stakeholders.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".