Patrick Moder joined Kühne Logistics University in September 2020 as Ph.D. Candidate supervised by Prof. Dr. Kai Hoberg. Patrick conducts his research on early detection and meaningful decision support for customer escalations in case of demand-supply mismatches. He works in close collaboration with Infineon Technologies AG, where he will apply his theoretical results in the environment of a global semiconductor supply chain.
Patrick received his Master of Science (Dipl.-Ing.) degree from the Technical University of Munich with a major in Mechanical Engineering and Management. In the course of his Master´s Thesis, he applied semantic web technologies to detect changes in the semiconductor product lifecycle and identify inconsistencies in change-related supply chain data. During the Master´s program, he spent a semester abroad in Sweden at Linköping University`s Institute of Technology. He received his Bachelor of Science degree from the Technical University of Munich, focusing on supply chain event management in the course of his Bachelor´s Thesis.
Patrick authored a book chapter in Springer`s Handbuch Industrie 4.0, highlighting the importance of semantic web technologies as one important enabler for advanced digitalization approaches. Moreover, he published two pages at the European Advances in Digital Transformation Conference series and co-authored a publication at the Winter Simulation Conference on topics covering the application of semantic web technologies for problems in supply chain management.
Education
| Since 2020 | PhD Candidate at KLU in collaboration with Infineon Technologies AG |
| 2019 | Master of Science (Dipl-Ing.) in Mechanical Engineering & Management, Technical University of Munich, Germany |
| 2017 | Exchange Semester at Linköpping University´s Institute of Technology, Sweden |
| 2016 | Bachelor of Science in Mechanical Engineering & Management, Technical University of Munich, Germany |
Professional Experience
| 2018 – 2020 | Management and Conduction of Funded Research Projects as Intern and Master`s Candidate for Supply Chain Engineering Innovation, Infineon Technologies AG, Neubiberg, Germany |
| 2015 | Launch of Own Enterprise Providing Independent Services for Application and Service Engineering, Germany |
| 2013 | Research and Development Engineering as Intern with NCC Nano LLC DBA NovaCentrix, Austin/TX, USA |
Publications
(In press): Predicting Complaints in Semiconductor Order Fulfilment with Machine Learning, International Journal of Production Research: .
Abstract: Many customers complain when informed that their order will not be fulfilled as originally confirmed, while other customers may be able to tolerate deviations. However, for suppliers, such complaints can be an early indicator of bad publicity, customer churn, and lost sales; and suppliers can prioritise orders to avoid these negative consequences. Ideally, they would know in advance if any order fulfilment change will trigger a customer complaint. To analyse how suppliers can predict these infrequent events in a business-to-business context, we leverage machine learning models on a large real-world dataset from a global semiconductor manufacturer. Our findings demonstrate that extreme gradient boosted trees effectively address the prediction problem. We explore the impact on model performance for different sampling approaches and cutoff values, as tuning the decision threshold is a meaningful calibration strategy before practical implementation. Our feature importance analysis provides evidence that high order fulfilment quality lowers complaint tendencies. Bridging the gap between advanced analytics and customer behaviour prediction, our research contributes to understanding the influence of subpar order fulfilment on customer satisfaction and offers insights into efficient order management despite disruptions. Our empirical study lays the groundwork for proactive supply chain operations when order fulfilment is at risk.
