When Signals Become Solutions: MES as a Driver of Industrial Improvement

This thesis investigates the digital transformation process within Faber Industrie S.p.A., an Italian manufacturer specialized in the production of high-pressure gas cylinders. In this context, Faber is trying to increase productivity, reduce waste, and exploit data coming from its production lines to make better decisions. The focus of this research is to examine how a Manufacturing Execution System (MES), already implemented in the company, can be used not just for data collection, but to generate real-time insights that are operationally meaningful. The study first analyzes how MES data can be validated and structured to calculate key performance indicators such as Overall Equipment Effectiveness (OEE), and to assess performance variability across materials and processes. This enables a better understanding of production behavior, useful for optimizing planning and process standards. After that, the emphasis switches to maintenance area where a classification model was created to find reoccurring failures by examining past tickets. As a result, standardized failure codes and procedures are introduced, enhancing traceability and supporting the creation of dashboards for Mean Time Between Failures (MTBF) analysis. Finally, the research explores energy efficiency by integrating MES machine states with consumption data in order to create benchmarks for energy use within each production phase. This enables the identification of inefficiencies and promotes a more conscious use of resources. In conclusion, the results show that MES can be transformed from a passive data store into a strategic tool that supports continuous improvement, enhances traceability, and facilitates data-driven decisions.