| Titre : | Improving supply chain resilience and delivery performance in the medical device industry : a ripple effect simulation and ai-based decision support approach |
| Auteurs : | Zakaria Abdelbassat Boudjellal, Auteur ; Houssem Eddine Bouslimane, Auteur ; Iskander Zouaghi, Directeur de thèse ; Mohamed Hazi, Directeur de thèse |
| Type de document : | document électronique |
| Editeur : | [S.l.] : [s.n.], 2026 |
| Format : | 1 fichier PDF (7.7 Mo) / ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d'Études : Génie Industriel. Management Industriel : Alger, École Nationale Polytechnique : 2026 Bibliogr. p. 125 - 130 . - Annexe p. 131 - 150 |
| Langues : | Anglais |
| Index. décimale : | PI00426 |
| Tags : | Supply Chain resilience Ripple effect Medical devices Fuzzy ANP–TOPSIS Stochastic modeling Discrete-event simulation Agentic AI Decision support |
| Résumé : | Medical device supply chains are particularly vulnerable to disruptions due to their globalized structure, regulatory constraints, and reliance on international sourcing. This dissertation investigates disruption propagation within the Wing-to-Wing (W2W) supply chain of GE HealthCare Algeria, an import-dependent, zero-inventory, make-to-order system. A three-pillar methodology is developed. The first pillar applies a Fuzzy ANP–Fuzzy TOPSIS approach to prioritize previously identified disruptions according to their impact, likelihood, propagation effect, and controllability, while establishing an order-centric stochastic representation of the supply chain to formalize disruption propagation. The second pillar develops a discrete-event simulation model in AnyLogic to quantify disruption impacts on delivery performance. The third pillar introduces SC Copilot, an AI-powered decision-support system integrating large language models with simulation outputs for scenario analysis and managerial decision-making. Results reveal a strong asymmetry in disruption behavior, where missing-item disruptions generate substantially greater delays than factory delays by triggering a complete second import cycle. The study contributes a methodological framework for disruption prioritization, stochastic modeling, and simulation-based analysis in zero-inventory supply chains, while demonstrating the potential of agentic AI to enhance resilience and decision- making in the medical device sector. |
Exemplaires (1)
| Code-barres | Cote | Support | Localisation | Section | Disponibilité | Spécialité | Etat_Exemplaire | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| EP01138 | PI00426 | Ressources électroniques | Bibliothèque centrale | Projet Fin d'Etudes | Disponible | Management_Industriel | En Traitement |
