Tim Schlaich started his PhD program at Kühne Logistics University in May 2020 under the supervision of Prof. Dr. Kai Hoberg and Prof. Dr. Sandra Transchel. His research focuses on the optimization of food supply chains through information sharing and technological advancements. He conducts his research in cooperation with Peter Cremer Holding GmbH & Co. KG, a family-owned trading and production company based in Hamburg.
Tim already joined KLU in 2017 for his Master of Science in Global Logistics and Supply Chain Management. He was awarded the Best Student Award Class Master of Global Logistics and SCM 2019 and received the BVL Thesis Award for his master thesis on consumer mobility in the context of traceback models for food-borne diseases. After graduation, he worked as a research associate and co-authored a joint paper entitled "A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks", which was published by the International Journal of Environmental Health and Public Research (2020).
Before joining KLU, he obtained a Bachelor of Arts in International Business from the Baden-Wuerttemberg Cooperative State University. During his Bachelor studies, he was employed by the Evonik Industries AG and gained work experience in several departments.
Education
| Since 2020 | PhD Candidate in Supply Chain Management, Kühne Logistics University, Hamburg, Germany |
| 2017 - 2019 | Master of Science Global Logistics and Supply Chain Management, Kühne Logistics University, Hamburg, Germany |
| 2014 - 2017 | Bachelor of Arts International Business, Cooperative State University (DHBW), Mannheim, Germany |
Professional Experience
| 2018 | Intern in Logistics Department, Bonprix Handelsgesellschaft mbH, Hamburg, Germany |
| 2016 – 2017 | Practical Semester, Process Management Supply Chain, Evonik Performance Materials GmbH, Darmstadt, Germany |
International Experience
| 2018 | Semester abroad, Universidad de los Andes, Bogotá, Colombia |
| 2016 | Practical semester, Evonik Corporation, Birmingham, USA |
| 2015 | Semester abroad, Universitat Politècnica de València, València, Spain |
Publications
(2020): A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks, International journal of environmental research and public health, 17 (2): 444.
Abstract: Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health.
