Bollati, Edoardo (A.A. 2024/2025) Predict, adapt, overcome: generative AI for the resilience of the global supply chain. Tesi di Laurea in International operations and global supply chain, Luiss Guido Carli, relatore Lorenza Morandini, pp. 107. [Master's Degree Thesis]

PDF (Full text) Restricted to Registered users only Download (921kB) | Request a copy

Abstract/Index

The paradigm shift from optimization to resilience. The research problem: the confluence of non-stationary risks, networked vulnerabilities and cognitive limitations. Foundational concepts and literature review. Evolution of supply chain management philosophies. Supply chain risk management and resilience. The role of analytics and AI in SCM. Synthesis and identification of the research gap. Research methodology. Data acquisition and preprocessing. Generative model selection and theoretical framework. Implementation and experimental design. Evaluation framework. Experimental setup and results. Discussion of findings and a proposed framework for anticipatory resilience. Introduction: a shift to the anticipatory paradigm. From replication to revelation: interpreting the empirical mandate. Final model selection and characteristics of the generated dataset. The calculus of resilience: a proposed framework. Praxis: grounding the research with industry leaders. Applications of the calculus.

References

Bibliografia: pp. 103-107.

Thesis Type:	Master's Degree Thesis
Institution:	Luiss Guido Carli
Degree Program:	Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91)
Chair:	International operations and global supply chain
Thesis Supervisor:	Morandini, Lorenza
Thesis Co-Supervisor:	Valentini, Giovanni
Academic Year:	2024/2025
Session:	Autumn
Deposited by:	Alessandro Perfetti
Date Deposited:	10 Feb 2026 14:54
Last Modified:	10 Feb 2026 14:54
URI:	https://tesi.luiss.it/id/eprint/44763

Downloads

Repository Staff Only

View Item