The Production Logistics system is generally a large-scale complex system with various operational phases and management levels that must integrate. In the specific context of soft and deformable food products, the core of AGILEHAND European project, this complexity increases further due to challenges related to the handling and movement of such items. Efficient coordination of production and logistics phases becomes crucial to ensure product quality, prevent losses, and optimize the entire process. In this article, focus will be placed on a data-driven framework for the automated generation of simulation models, serving as the foundation for digital twins in intelligent factories within the previously mentioned sector. The proposed framework represents a multi-layered data-driven system designed for real-time/near-real-time simulation, planning and synchronization of production and logistics systems during line reconfiguration. The digital model forms the basis for a digital twin with simulation and optimization capabilities, designed to facilitate decision-making at various management levels in the production and logistics process and control activities such as changes, maintenance, quality and safety. Exploiting information provided by the Enterprise Traceability system, the digital twin aims to establish a real-time/near-real-time information flow. This flow enables accurate capturing of dynamics occurring in the physical layer and effective assessment of their negative effects on the overall operational state of the system in the digital layer. In this context, the use of the digital twin is intended to simplify and expedite the reconfiguration of production and logistics systems. This is achieved through the early detection of system design or process sequence through cross-sectional simulation.