A Digital Twin Framework for Optimized Production Configuration and Simulation for Order Deadline Fulfilment: A Case Study in an Italian Food Company

The food manufacturing sector increasingly requires advanced technologies for production management and process optimization. In particular, the development of Digital Twins (DT) enhances synchronization between logistics and production by integrating innovative technologies such as Big Data, IoT, and Artificial Intelligence. These technologies enable process optimization, reducing wait times and improving resource utilization. In the food sector, this translates in minimizing food waste and ensuring product freshness. This study focuses on optimizing production management and human resource allocation in collaboration with an Italian company specializing in small fruit processing as part of the AGILEHAND European project. The optimization approach includes improving forecasting techniques through AI-based algorithms, optimizing workforce allocation and streamlining production workflows to improve adaptability to fluctuating demand. The DT serves as a decision-making tool by analysing both real-time and historical data, simulating various operational scenarios, and determining optimal production configurations using a non-real-time simulation module. The results demonstrate the system accuracy in meeting demand deadlines while optimizing workforce requirements, leading to more efficient resource management. By leveraging DT technology, the system enables proactive adjustments in production schedules and workforce distribution.