Dissertations Dr.-Ing. Daniel Fruhner

Demand and capacity management (DCM) is an elementary component of supply chain management in the automotive industry. The purpose of DCM is to synchronize the resource demand resulting from expected or realized market demand with the capacities and restrictions of the supply chain and production system (Figure 1). A significant challenge for DCM lies in the uncertainty and volatility of demand resulting from a large variety of products. Information technology has been supporting complex DCM processes with increasing success, making all systems dependent on an efficient and holistic product representation. The automotive industry is currently confronted with two major trends. The first is the diversification of the powertrain (in particular, e-mobility), which produces changes in physical vehicle architecture. The second is automotive digitalization, such as autonomous driving, which introduces new and changed dependencies between components, such as hardware and software compatibility. It is essential to effectively document these new and changed dependencies when developing a product representation for DCM in the automotive industry. The goal of Daniel Fruhner’s dissertation is an efficient and flexible product representation for DCM in the automotive industry to map information relevant to logistics for digitalized vehicles. Due to the accelerated changes the automobile has undergone, a more flexible and efficient DCM process is required. However, the DCM process currently in use is not based on all the required information. To address this problem, the process characteristics are identified based on the process variants of the DCM. A literature review and expert interviews are used to identify requirements and product information from the business units, which must be included in the product representation. The product information from the business units is analyzed and segmented. The requirements and especially product information (digital characteristics), which must be integrated into the product representation for holistic representation due to the increasing digitalization of the automobile, are extracted. Digital characteristics create new dependencies to be integrated as they have an impact on logistics. To select the data structure and concept for an efficient and flexible product representation for the DCM process, a second literature review is conducted. Based on the results, a graph-based ontology of efficient and flexible product representation is designed to map information relevant to logistics for digitalized vehicles (Figure 2). On the basis of a real use case of a German car manufacturer, the efficient and flexible product representation is implemented and validated in a prototype.

Daniel Fruhner graduated with a Bachelor's and Master's degree in Computer Science from Fachhochschule Dortmund - University of Applied Sciences and Arts. After gaining three years of practical experience as a software developer for embedded systems in the automotive industry, he returned to Fachhochschule Dortmund. Data structures and knowledge representations of automotive logistics were his main research topics as a research associate. From 2016 to 2022, he earned his doctorate in engineering as part of a cooperation between TU Dortmund University (LFO) and Fachhochschule Dortmund (IDiAL). He defended his dissertation on 3 June 2022.