Conceptualization and development of an intelligent decision support system for supply chain risk management in the automotive sector

The rapid rise of artificial intelligence in recent years has presented transformative opportunities for industries, including the automotive sector. Concurrently, the increasing complexity of global supply chains has introduced significant challenges, particularly in managing supply chain risks in this sector. In this context, intelligent decision support systems, which extend traditional decision support systems by incorporating artificial intelligence features, have shown great potential in addressing these challenges by offering data-driven insights and solutions. To this end, this thesis adopted a design science research approach to develop prescriptive knowledge on how to build an intelligent decision support system tailored to managing supply chain risk management activities within the automotive sector. The methodology involved extensive literature reviews combined with experimental design conducted in collaboration with an Italian automotive company. The research yielded several key contributions. First, a comprehensive framework was developed to guide the high-level design of an intelligent decision support system for supply chain risk management. Additionally, a generalized reference data model was created to inform the design of the database management system, which supports such an intelligent decision support system. Furthermore, valuable knowledge was generated on how to design predictive and prescriptive models within the model-based management system. Lastly, empirical results from a case study in the automotive sector demonstrated the tangible benefits of adopting intelligent decision support systems for managing supply chain risks.