Supply Chain Risk Mitigation: Modeling an Approach for Greater Visibility in Moroccan Automotive Industry
Bibliographic record
Abstract
Managing the customer-supplier relationship in procurement has always been characterized by its complexity, especially during crises.Large enterprises manage to mitigate risk thanks to their ability to adopt high-performance tools that ensure real-time visibility across their entire supply chain (SC).In contrast, small and medium-sized firms struggle to adapt.This study examines visibility enhancement strategies in emerging automotive markets taking the Moroccan model as a leading example, revealing that there are effective technological tools that are not accessible or widely adopted by most companies.To fill this gap, this study presents a conceptual architecture of a decision support tool that will assist manufacturers and researchers alike in achieving better visibility of their supply chain and identifying capacity risks based on the trilogy of customer, Tier I, and Tier II suppliers.Findings reveal that managing risk in the supply chain requires a multifaceted approach.Firstly, it identifies crucial measures such as establishing backup suppliers and planning overtime based on stakeholders' responsibilities, which are essential for mitigating risk.Secondly, the study proves that risk localization within the supply chain is feasible, enabling companies to target their risk management efforts more effectively.Finally, it underscores the significance of monitoring supplier performance through a dedicated key risk indicator (KRI) called supplier risk follow-up (SRF-U), ensuring that suppliers meet performance standards.These findings collectively provide a comprehensive strategy for improving supply chain resilience (SCR) and efficiency.This study implements a short-term solution, increases visibility, and responds quickly to complicated crises.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".