Implementation and Impact Assessment of a MES System in a Yacht Manufacturing Plant

This thesis explores the implementation of a Manufacturing Execution System (MES) within a highly specialized and handcrafted production environment. The first part provides an overview of the evolution of Industry 4.0, focusing on the main digital actors that enable smart manufacturing: Enterprise Resource Planning (ERP) systems, which manage business processes; Advanced Planning and Scheduling (APS) systems, which optimize production planning; and Manufacturing Execution Systems (MES), which bridge the gap between enterprise management and shop floor operations. Traditionally, MES systems are implemented in automated industries, where data collection is facilitated by machinery that continuously reports production parameters such as cycle times, set-ups, and machine downtimes. These systems allow real-time monitoring and performance optimization, as well as rapid detection of inefficiencies. However, transferring this same logic to an environment characterized by manual labor, product customization, and artisanal expertise poses a significant challenge. The case study focuses on the Azimut Yachts plant in Avigliana (TO), a site specializing in the production of luxury yachts below 23 meters in length. Unlike typical industrial contexts, Azimut’s manufacturing processes rely heavily on skilled operators rather than automation. Each yacht is unique, designed according to specific customer requests that often involve optional or fully customized solutions. This results in a highly variable and complex production flow, making it difficult to standardize and monitor operations. The introduction of the MES in this context required a complete redefinition of data collection and tracking methods. Instead of connecting the system to machines, as in automated factories, the MES had to be linked to individual operators, who became the primary source of production data. Through continuous coaching and a gradual adaptation process, workers learned to record their activities accurately and consistently. The role of Crew Leaders, team supervisors responsible for assigning tasks and ensuring data integrity, proved fundamental to the success of the implementation. The results achieved demonstrate a significant cultural and operational improvement. Operators have developed greater awareness of time management and task allocation, while supervisors can now rely on real-time production data to monitor progress and analyze inefficiencies. The availability of accurate, structured data allows the company to compare actual versus planned hours, evaluate the distribution of workload among teams and continuously improve productivity.