Presentation (online) by Dr. Christian Sonntag, Head Of Innovation at INOSIM, in cooperation with CSL Behring

Maximizing Efficiency And Transparency In Batch Production With Simulation-Based Decision Support

Production plants in the process industry are highly complex systems in which often dozens of different products are manufactured using complicated, sometimes dynamic production recipes. Hundreds of individual steps, a high degree of automation, and dynamic conditions make it difficult for operating personnel to make predictions about long-term plant behavior. As a result, plant operators regularly waste huge optimization potential in production, resource, and employee planning.

In this presentation, we introduce the new INOSIM Foresight system for predictive decision support in complex batch plants. Based on high-precision material flow simulation, INOSIM Foresight delivers more accurate predictions than previously possible: plant operation and resource utilization can now be planned more precisely and efficiently than before; savings in the millions are the result. In addition, INOSIM Foresight reliably predicts unplanned delays and outages as well as consequences of planned events (maintenance, cleaning). Negative effects are thus avoided.

The lecture also gives an overview of current research that is based on INOSIM Foresight: In the OptiProd.NRW project, we develop methods for the automated generation of production schedules. Within the framework of the innovation platform KEEN, AI approaches are developed for the reconstruction of batch parameters and the AI-driven optimization of production in the process industry.

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