My research interests fall in the area of Operations Management. I develop predictive and prescriptive Business Analytics approaches for the quantitative analyses of dynamic and stochastic systems. The research can be clustered in the following three areas:
Predicting key performance measures such as throughput and cycle time is the basis for the design and control of systems that are subject to stochastic and dynamic influences (see Figure). This research area focuses on the development of queuing models and approaches for their quantitative analysis. Challenging is, e.g., the derivation of performance measures beyond the mean such as quantiles or service levels. This information is key for a solid risk assessment. The models and methods find applications in the analysis and optimization of manufacturing systems and logistics systems such as material handling systems.
The digitalization has extensive effects on supply chains and production systems. This research area focuses on methods that exploit available data and information about future developments of customer demand and the production system itself, on the control of digitalized production systems. This is relevant, e.g., for seasonal demand or during production ramp-up. Moreover, solution approaches that include the identification of structural properties of the underlying optimization problem in combination with simulation/
The trend towards shortening product life cycles leads to more frequent changes between product generations. Managing the transition phase is challenging because of the complex interdependencies between companies’ decisions and customer reactions. In particular, substitution effects between product generations need to be captured. The research aims at deriving structural insights about the impact of finite and stochastic production capacities on strategies for the transition phase that give guidance regarding product offering, pricing, production, and inventory decisions.
The research project “Productive 4.0 - Analytical approaches for performance evaluation and optimization of stochastic and dynamic processes in digital production” investigates how electronics and Information and Communication Technologies (ICT) can enhance production efficiency in semi-conductor supply chains across the entire value chain and product life cycle. With 109 partners from industry and research institutions, Productive4.0 is Europe’s biggest research project in the field of Digital Industry. The project is recognized as an ECSEL Lighthouse Initiative and receives funding from the EU and the German Federal Ministry of Education and Research (BMBF). The Chair of Production Management focuses on two challenging optimization problems: (i) operational planning of start of production decisions and (ii) product rollover management. These problems are of practical relevance due to the frequent ramp-ups and ramp-downs caused by short product life cycles in the semiconductor industry.
The goal of this research project is the development of the theoretical foundation to support decision makers in creating shift schedules for the workforce and other resources. We consider the stochasticity and time-dependency of demand as key aspects that must be taken into account. Relevant objectives that will be investigated are the minimization of the employed workforce and related workforce costs as well as profit-oriented performance measures. The resulting plans must satisfy several constraints. The most important ones are contractual agreements on the working hours and on-time delivery of the goods. The main focus of this research project is the modeling of multi-stage systems with stochastic influences as well as the optimization and evaluation of the shift schedules. Our goal is to make a significant contribution to the research community and support decision makers of production and logistics systems with new managerial insights and the development of new optimization methods.
(Funded by Deutsche Forschungsgemeinschaft e.V. in Collaboration with Karlsruher Institut für Technologie) (2018 - 2020)
Doctoral Studies in Business Administration, Chair of Production Management (Prof. Stolletz), University of Mannheim
Studies in Business Engineering (Diploma), Karlsruhe Institute of Technology (KIT)
Studies in Engineering and Technology Management (M.S.), Portland State University
Abitur, Gymnasium Oberalster, Hamburg
Assistant Professor (Akademischer Rat auf Zeit), Chair of Production Management (Prof. Stolletz), University of Mannheim
Visiting Scholar, Koc University, College of Administrative Sciences and Economics, Turkey
Visiting Scholar, Massachusetts Institute of Technology, Laboratory for Manufacturing and Productivity, USA
Research Assistant, Chair of Production Management (Prof. Stolletz), University of Mannheim
Internship, The Boston Consulting Group GmbH, Johannesburg South Africa
Internship, Robert Bosch GmbH, Karlsruhe
Internship, Preh GmbH, Bad Neustadt a. d. Saale