AI applications in Manufacturing and Production: an Exercise of Technology Monitoring and Assessment

In today’s fast-evolving manufacturing and production landscape, Artificial Intelligence (AI) has emerged as a transformative force in these sectors, and it is crucial to understand its real prospects. For this,the technique called Technology monitoring can play a crucial role in identifying and evaluating AI applications, ensuring their effective integration into industrial processes. This thesis aims to explore the impact of AI in key areas such as quality control, robotics, automation, and supply chain management, focusing on its potential to enhance efficiency and innovation by examinng the current and future challenges organizations face in adopting AI technologies. The research employs a mixed methodology in the framework of Technology monitoring, including a literature review to define foundational concepts of an analysis of applications and a survey of industrial reports to identify trends and best practices. To provide practical insights, the study incorporates industrial case studies, particularly of tesla and the oil and gas sector, to illustrate real-world applications of AI. The findings suggest that AI offers significant opportunities for optimizing production processes, improving quality, and reducing operational costs, However, its adoption requires addressing unresolved challenges, including technical, organizational, and ethical/regulatory barriers, Beyond its considerable prospects, further developments in both technology and implementation strategies are necessary to fully realize the AI potential in manufacturing and production environments.