ERP basato sull'intelligenza artificiale: un caso di studio teorico sulle strategie di implementazione

This thesis investigates the effects of the integration of Artificial Intelligence into Enterprise Resource Planning systems, with a particular focus on its prospective benefits after its implementation in the Iveco Group company. It evaluates the possible applications, financial implications, and overall contribution to the companies. While ERP systems are crucial for the enterprise's core processes, AI gives the possibility to increase efficiency, decision-making, and to innovate the systems. The literature review provides a detailed examination of existing research on ERP and AI. It explores the evolution of the AI-driven-ERP literature, it highlights the sources of information used in this thesis and covers the observed gaps and proposes suggestions. The first chapter of the thesis establishes the theoretical framework, divided in three key sections. First, the foundation of ERP traces the development of ERP systems and emphasizes their role in streamlining enterprise operations and providing a centralized data repository. Second, the foundations of AI cover the core concepts of AI, its methodologies, applications, and the latest technological advancements. Third, it analyses how these two joined solutions lead companies towards establishing a competitive edge with improved data analytics, process automation, and predictive capabilities. The second chapter addresses the financial benefits of implementing AI driven ERP. This includes a cost-benefit overview, assessing the direct and indirect costs against the potential benefits and ROI estimation to quantify financial gains. It also explores the impact on operational efficiency, examining how AI can streamline business processes, reduce errors, and enhance productivity. Additionally, it considers improvements in financial reporting, accuracy, speed, and regulatory compliance. Risk assessment and mitigation strategies are also included. The third chapter illustrates the proposed practical application in a case study about Iveco Group, a leading manufacturer in the automotive industry. This section includes a brief history and background of Iveco. It analyses Iveco's market position, key competitors, and competitive advantages. The product portfolio and primary markets are also reviewed. Additionally, the financial performance of Iveco and of the current ERP system is provided. Potential AI integration into the Iveco ERP system is explored through proposed enhancements, detailing AI-driven improvements. The final chapter interprets the findings, discusses the drawbacks of AI in ERP systems. The limitations of the analysis are acknowledged, highlighting the study’s constraints. Recommendations for efficient implementation strategies are proposed, indicating the areas to be tackled. The conclusion summarizes key findings and their significance with the observed implications of integrating AI into ERP systems. This thesis uses Iveco as a case study to demonstrate practical applications and projected outcomes, offering valuable insights into the future of AI-enhanced ERP systems.