Decision-Support Frameworks for Supply Chain Resilience and Sustainability in SMEs, Enabled by AI, Modelling, and Digital Automation

Nowadays, manufacturing organizations are working in ever increasing volatile and complex environments due to increasing disruption frequencies and sustainability regulations. These challenges come across all organizational supply chains regardless of their size. However, small and medium-sized enterprises (SMEs) are more prone to these risks due to their limited resources in terms of building resilience and achieving sustainability goals. This thesis addresses the dual challenge, risk mitigation and enhancing sustainability performance, by providing decision-making frameworks through the integration of artificial intelligence, modeling & simulation, and multi-criteria decision-making. In the resilience domain, the thesis contributed with two conceptual frameworks; the first framework enhances supply chain resilience for SMEs by integrating generative artificial intelligence at different stages of standard risk management cycle i.e., risk identification, risk assessment, risk mitigation, and risk control. The second framework aids in choosing best collaboration strategy for SMEs to minimize operational impacts while facing environmental disruptions. The conceptual framework integrates modeling & simulation and artificial intelligence in decision-making. In the sustainability domain, this thesis developed a comprehensive sustainability assessment framework with the integration of structured set of European Sustainability Reporting Standards’ (ESRS) indicators and multi-criteria decision-making technique Analytical Hierarchy Process (AHP). The framework has been validated by a case study applied to an Italian manufacturing organization. The results of the case study provided insights for strategic decision-making in improving sustainability performance. Moreover, this thesis also contributed by the digital implementation of the developed sustainability assessment framework which brings transparency and avoids manual handling of the sustainability data. Overall, this thesis provides a practical guide that supports data-driven decision-making in managing supply chain risks and sustainability challenges especially in the context of SMEs.