Exploiting Machine Learning and Industry 4.0 traceability technologies to re-engineering the seasoning process of traditional Parma's Ham

The work presents a Machine Learning approach for predicting the quality of the curing process of Parma ham, combined with a study of business process re-engineering, based on RFID and Deep Learning technologies for automatic recognition and tracking of the hams along the curing process. Quality management has proven to be crucial for efficient and effective processes, even more so for the food industry, both for commercial and regulatory purposes. This is even more evident in artisanal-based processes, such as the one concerning traditional Prosciutto di Parma seasoning. The work proposes and compares a Feed-Forward Neural Network and a Random Forest for predicting the distribution of the number of hams by commercial quality class of a given aging lot. Such a prediction, based on origin, process, and curing data, can provide early indications of process output, enabling strategic commercial competitive advantages. The importance of the genetic component in the determination of the final quality is also evaluated, as it is considered one of the most influential external variables. Moreover, following the AS-IS description of the current process, a redesign is proposed, to enable data collection and tracking of individual ham in order to propose a future precision prediction system that would allow even finer control of the process.