Digital Twin-based bottleneck prediction for improved production control

Bottlenecks in manufacturing systems may significantly reduce their efficiency and productivity. Therefore, bottleneck analysis is a consolidated topic in Industrial Engineering, both in research and practice. Recently, traditional methods for bottleneck analysis have been enhanced with data-driven approaches, such as artificial intelligence and big data analytics. Nevertheless, their exploitation built on the full scope of technologies from digitalization is still not fulfilled. Indeed, the integration with simulation-based methods remains under-explored. This work aims to address bottleneck prediction leveraging on Digital Twin simulation capabilities to predict manufacturing system behavior. For this purpose, the work first offers an extensive review of bottleneck identification methods, inclusive of the ones based on Digital Twin. The main contribution of the work lies in the proposal of a novel Digital Twin-based bottleneck prediction framework with the end purpose to achieve performance improvements actuated through production control. The framework utilizes the Digital Twin for predicting and mitigating bottlenecks in manufacturing systems. The Digital Twin enables the simulation of the future system behavior, while accounting for the current conditions. This insight can then be used by a bottleneck identification method to infer future system bottleneck. The information on the predicted bottleneck is eventually used to support production control decisions, by adapting the order release and sequencing according to the predicted bottleneck. The benefits of adapting production control to the predicted bottleneck are evaluated quantitatively, highlighting how system performance is enhanced. By doing so, this research contributes to bridging the gap between Digital Twin-based performance analysis and production control, providing knowledge and a practical framework transferable to researchers and industrial practitioners.