Research & industry transfer
The IPEM Chair at the University of Siegen sees itself as a reliable and competent partner and service provider for the manufacturing industry with the aim of driving forward the digital transformation in South Westphalia and beyond. To this end, we offer you the opportunity to collaborate directly in individual planning and consulting projects as well as in publicly funded research projects. We contribute our expertise in a targeted manner to the joint development of solutions. To ensure that the manufacturing industry can also regularly participate in the latest research developments, there is also the opportunity to actively participate in one of our working groups. The Best Practice Circle Production and the Innovation Circle: Digital Business Models create a platform for intensive exchange, content-related impulses, the discussion of selected issues and the network for further cooperation. We also offer a wide range of training courses to prepare you and your employees for the challenges of tomorrow.
You can find more information on the SDFS website.
Publications
Current publications
Enhancing brewery logistics with smart kegs: A simulation study
Enhancing brewery logistics with smart kegs: A simulation study
Cyber-Physical Optimization of Production Processes Using Cascade AIs: A Robot-Guided MAG Welding Use-Case
Cyber-Physical Optimization of Production Processes Using Cascade AIs: A Robot-Guided MAG Welding Use-Case
Intelligent Order Release In Matrix-Structured Assembly Systems
Intelligent Order Release In Matrix-Structured Assembly Systems
Paving the way for automated factory planning - applying rule-based expert systems to capacity planning
Paving the way for automated factory planning - applying rule-based expert systems to capacity planning
Enhancing Resource Efficiency And Monetization In Metal Recycling Through Supply Chain-Wide Digitalization: An Approach For Single-Variety Metal Stream Optimization And CO2 Reduction
Enhancing Resource Efficiency And Monetization In Metal Recycling Through Supply Chain-Wide Digitalization: An Approach For Single-Variety Metal Stream Optimization And CO2 Reduction
Machine learning implementation in small and medium-sized enterprises: insights and recommendations from a quantitative study
Machine learning implementation in small and medium-sized enterprises: insights and recommendations from a quantitative study
Enhancing Resource Efficiency And Monetization In Metal Recycling Through Supply Chain-Wide Digitalization: An Approach For Single-Variety Metal Stream Optimization And CO2 Reduction
Enhancing Resource Efficiency And Monetization In Metal Recycling Through Supply Chain-Wide Digitalization: An Approach For Single-Variety Metal Stream Optimization And CO2 Reduction
Applications of machine learning in the brewing process: a systematic review
Applications of machine learning in the brewing process: a systematic review
Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques
Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques
Optimize existing structures and workflows: Using a real-time locating system (RTLS) to enhance cleanliness in modern production areas
Optimize existing structures and workflows: Using a real-time locating system (RTLS) to enhance cleanliness in modern production areas
Uncovering the behaviour of facility layout problem solutions in relation to factory design applications
Uncovering the behaviour of facility layout problem solutions in relation to factory design applications
Optimize existing structures and workflows: Using a real-time locating system (RTLS) to enhance cleanliness in modern production areas
Optimize existing structures and workflows: Using a real-time locating system (RTLS) to enhance cleanliness in modern production areas
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