A hybrid multi‐criteria decision‐making approach to evaluate interrelationships and impacts of supply chain performance factors on pharmaceutical industry
Bibliographic record
Abstract
Abstract Pharmaceutical Supply Chain (PSC) plays a critical role in the healthcare sector. This study identifies, validates, and prioritises the factors that play a crucial role in PSC performance, adopting a threefold approach. In the first stage performance, indicators were identified through an extensive review of the literature. With the help of expert opinion, the identified factors were validated and then categorised based on technological—organisational—environmental (TOE) and supply chain (SC) theories to propose a framework. The Pakistani Pharmaceutical sector firms were selected to investigate the cause and effect relationship among the factors, their interdependencies, and impact on overall PSC performance. This investigation was supported by a novel integrated analytic model composed of best worst method (BWM), decision‐making trial and evaluation laboratory (DEMATEL), and analytical network process (ANP) methods. The results indicate that ‘human resource skills, competencies, and involvement’, ‘process improvement and healthcare reform, and manufacturing’, and ‘distribution and inventory management’ are the top three factors that have a high impact on the overall PSC performance. This study outcome help inform decision‐makers and managers in the healthcare sector in formulating strategies to improve their SC performance.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".