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Dissertation Projects

Martin Böhmer (Fraunhofer IML, Software Engineering)


Working Title: Moderne Ansätze zum daten-zentrierten Wissensmanagement in industriellen Großunternehmen

Isabel Bücker (Graduate School of Logistics)


Subject: Entwurf von Prozess- und Informationsarchitekturen für Industrie 4.0- Szenarien in der Automobillogistik

Brief Overview: Nowadays a company is confronted with a multitude of market and business drivers. Examples are the development of global markets (e.g. in Asia), an increasing individuality of customer requirements, which is reflected in a greater variety of models, a growing number of variants and decreasing product life cycles. At the same time, an increasing digitalisation of the vehicles themselves, the production processes and the business relationship with the customer can be observed. In this situation, logistics has to meet various, sometimes conflicting requirements, in particular increasing adaptability, increasing transparency, shortening planning horizons, supplying a growing production network and increasing the efficiency of logistics processes.

Against this background, the aim of the work is the design of process and information architectures for Industrie 4.0 scenarios in automotive logistics.

Jens Grambau (Hochschule der Medien Stuttgart, Fakultät Druck- und Medientechnologie / Digital Publishing)


Working Title: Predictive Maintenance in the context of service and the role of Social Media data in this context

Brief Overview: Predictive Maintenance tries to predict failures and defects of products for being able to budget maintenance and order parts as well as for being able to reduce possible failures and damages of products. This technology enables companies to enhance and improve their processes and products to provide a 360-degree service for customers. Nowadays more and more data about products is also shared in Social Media networks which offer companies new way of gathering information.

This work will analyze in a first step Predictive Maintenance models and the role of Social Media data within these models. In the next step a Framework will be built to combine Social Media data, and already existing data in a company. It describes which and how analytical processes and models are used and executed. The final part of the thesis is the evaluation of the processes and the models in use case with productive data.

Rainer Halmheu (AUDI AG, Technische Hochschule Ingolstadt)


Subject: Lokalisierung und Steuerung von Fahrerlosen Transportfahrzeugen über externe Sensorik

Brief Overview: Especially in the age of Industry 4.0, with the aim of a smart factory, the versatility of transport technologies is a decisive criterion for their success. In the logistics of large companies, therefore, increasingly automated guided vehicles with free navigation are used; they can provide this flexibility.

As part of the promotion, a new, innovative technology was developed, which allows the free navigation of automated guided vehicles by an external sensor (laser scanner). What is new about this technology is that the automated guided vehicles can also be located one behind the other.

Mario Hermann (TU Dortmund University, Chair For Industrial Information Management)


Subject: Entwicklung eines Industrie-4.0-Reifegradmodells für die Logistik

Brief Overview: The term Industrie 4.0 is currently being intensively discussed both in science and in practice. However, there is no uniform understanding of the term. A particular challenge in practice is to assess what implications Industrie 4.0 has for one's own company and how the company is positioned with regard to these implications.

Against this background, the dissertation develops a Maturity Model, which helps companies to take advantage of the benefits of Industrie 4.0. Industrie 4.0 is understood as a new organizational concept that fundamentally influences the future design of companies. For this reason, the effects of Industrie 4.0 on companies are analysed and 24 Industrie 4.0 fields of action are derived from this. Each of these fields of action is in turn subdivided into levels that build on one another and point out a development path. This enables companies to measure their current Industrie 4.0 maturity, identify Industrie 4.0 potentials and develop an action plan.

André Moetz (Volkswagen AG)


Subject: Entwicklung eines integrierten, adaptiven Verfahrens zur Resequenzierung des Fahrzeugprogramms in automobilen Produktionsnetzwerken

Brief Overview: The primary objective of this doctoral thesis is the development of an integrated, adaptive method for resequencing the production schedule in case of short term disruptions. In automotive production networks, short-term disruptions (e.g. machine failures, supply disruptions) are an important driver of schedule instability. Such occurrences do not only affect the OEM's plant, but also trigger far-reaching effects within the entire network. Thus, schedule instability in production networks is a highly relevant topic for practitioners and academics that lacks research, especially from a network perspective. To support the development of such an generic IT artefact in an a priori unknown and complex field of research, a design science research approach was chosen.

Sebastian Opriel (Fraunhofer ISST, Digitization in Logistics)


Subject: Tools and Methods to Support the Digitization of Supply Chains

Tobias Pentek (University of St. Gallen)


Subject: Capability Reference Model for Data Management

Kurzbeschreibung: In the context of the digital transformation and the increasing importance of data-driven business models, data has turned into an important corporate resource with strategic relevance. Both the scientific and practitioners’ communities consider the management of data as a key capability. Despite the growing importance of and attention to data management, a framework for designing and structuring data management in digital, data-driven enterprises does not exist. The dissertation project bridges this gap by introducing the Data Excellence Model as a reference and maturity model for data management, which serves as a “blueprint” for implementing, developing, and assessing data management capabilities. The doctoral dissertation describes the design process, presents the resulting artifacts, and demonstrates their applicability and utility in several case studies.

Mathias Quetschlich (AUDI AG)


Subject: Forecasting Automotive POtions with Time Series and Machine Learning Approaches

Brief Overview: Different methods, processes, layouts of data flow and algorithms, using numerous predictors, such as online car configurators, are compared and evaluated. The goal is to generate accurate forecasts for sales options.

Ann-Carina Tietze (Volkswagen AG)


Subject: Zeitkritisches Wissensmanagement am Beispiel des Engpassmanagements in der Automobilwirtschaft

Brief Overview: The German automotive industry today is characterized by an increasing diversity of variants and decreasing in-house production depth, which leads to a complex supplier network for the production supply. This multi-faceted supply situation can provoke the occurrence of supply bottlenecks. For that reason the right handling of occurring delivery problems comes into focus. Currently, due to a lack of information on data quality, availability and timeliness, only a very delayed reaction on bottlenecks is possible. The selection of optimally targeted measures and their implementation is therefore difficult for the OEM. There are many data and expert knowledge available. Therefore, the aim must be a more effective use. The solution could be a knowledge management for a more effective use of knowledge through stringent knowledge generation. The direct transfer of the time-independent models to the bottleneck management, however, fails because of the time criticality of the practical problem. The aim of this work is therefore the demonstration of knowledge as a function of time through the development of an assistance system architecture for bottleneck management and, as a result, the derivation of time-influencing factors for the extension of an existing knowledge management model by the factor “time”.

Johannes Zrenner (Graduate School of Logistics)


Title: Interorganisationale Informationssysteme für das kollaborative Risikomanagement in automobilwirtschaftlichen Liefernetzwerken

Brief Overview: The supply networks of the automotive industry are becoming increasingly complex and susceptible to faults. The main reason for that is the global sourcing of automotive components in order to meet customer requirements in terms of price and functionality. Thus, the efficiency and effectiveness is an important competitive factor for automobile manufacturers. In practice, however, supply bottlenecks occur and are detected too late due to a lack of transparency in the supply network. The bottlenecks often result in financial losses for carmakers and their Partners.

The internal data in the various systems of the automobile manufacturers are not sufficient to generate a holistic view of the respective supply network. For this reason, a collaboration between the actors involved in the supply network is necessary. The result of this dissertation is an information model that describes an inter-organizational information system for collaborative risk management in the automotive supply network.