Digital Twin Modeling Framework for Manual Warehouses

This paper introduces a novel framework for replicating manual processes in digital twins. The proposed approach allows the modeling of manual process variability, a key aspect often overlooked in the literature. Our research highlights the potential of AI to tailor the digital twin to specific contexts influenced by human factors. The model's accuracy can be improved with each simulation synchronization cycle through supervised machine learning, which enhances the alignment of the virtual and physical processes. The proposed digital twin framework aims to avoid discrepancies from the physical counterpart, disregarding decisions based on fixed parameters that do not evolve over time and ensuring a higher degree of realism of the virtual replica. Ultimately, we aim to design the digital twin as a realistic and effective decision-making aid for all human participants engaged in the digital twin loop.