Bottleneck Detection Through Data Integration, Process Mining and Factory Physics-Based Analytics

Production systems are evolving rapidly, thanks to key Industry 4.0 technologies such as production simulation, digital twins, internet-of-things, artificial intelligence, and big data analytics. The combination of these can be used meet the long-term enterprise goals profitability, sustainability, stability by increasing throughput reducing costs. Owing digitization, manufacturing companies now explore operational level track performance making processes more transparent efficient. This untapped granular leveraged better understand system identify constraining activities or resources that determine system’s throughput. In this paper, we propose a data-driven methodology exploits technique integration, process mining, analytics based on factory physics constrained resources, also known bottlenecks. To test proposed methodology, case study was performed an industrial scenario were discrete event simulation model is built validated run future what-if analyses optimization scenarios. easy implement generalized any other organization captures data.