Modelling Resilient Healthcare Supply Chain: A Hybrid Vulnerability-Capability Model with TISM-MICMAC Approach
Bibliographic record
Abstract
In today's global business context, high competition forces companies to operate in highly uncertain conditions.Whenever these uncertainties convert into risk, and the risk becomes reality the companies may face profitability loss.Moreover, if the company is dealing in the healthcare sector, a loss is not limited to profitability instead, it may lead to the loss of lives.Resilient Healthcare Supply Chain (RHSC) could be an answer to the uncertain disruption challenges.Although various studies have proposed a resilient supply chain, this research paper addresses the partially filled gap for RHSC.This research assumes that all medicinal products supply chain does not require the same level of resilience.Moreover, it is challenging to achieve resilience free of cost.Hence, this research uses a vulnerability-capability framework to map the resilience requirements as a function of vulnerability and corresponding capability.Furthermore, it uses Total Interpretative Structural Modelling to establish a hierarchical relationship among various factors to better explain the relationships.In addition, this study uses MICMAC analyses which helps classify variables as drivers, linkages, and autonomous and dependent variables.This research concludes with interesting findings about the "what " and "how" of the theory and some future research directions with limitations of this research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".