Within the context of Industry 4.0, industrial systems are increasingly integrating digital tools such as discrete-event simulation (DES) and digital twins to enhance operational performance and facilitate data-driven decision-making. This research focuses on the design and implementation of an innovative digital twin to diagnose and optimize the productivity of manufacturing systems. A key advancement of this tool involves the integration of a material flow simulator, specifically WITNESS Horizon, with a suite of mathematical and digital models to create an integrated manufacturing digital tool. Rather than modeling individual pieces of equipment or isolated workstations, this digital twining approach encompasses the entire manufacturing architecture. To demonstrate the capabilities and efficacy of this integrated tool, we applied it to a real-world case study: the “Hub R2-11” production line. Utilizing the diagnostic features of the tool for this case study, we identified significant bottlenecks, revealing that over 70% of the conveyor was blocked and more than 60% of the workstation was underutilized. Utilizing the simulation and optimization features of the tool, we increased productivity by restructuring the manufacturing architecture. This involved implementing parallel machining, regulating inputs, and incorporating robotic palletizing. The resultant manufacturing architecture demonstrated a substantial improvement, increasing weekly pallet output from 17 to 41 while reducing conveyor block time by more than 50%. Additionally, a financial assessment indicated a favorable Net Present Value (NPV) and an Internal Rate of Return (IRR) exceeding 35% over three years. The research presented here employs a digital approach grounded in realistic operational constraints, effectively bridging technical innovations with economic feasibility. The findings underscore that this integrated manufacturing digital tool presents a scientifically robust and economically sound strategy for optimizing production systems.
Chiscop et al. (Sat,) studied this question.