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Department Business and Economics


Ongoing projects (selection)

Supply Chain Management in the Energy Sector
Hardly any other sector is currently in the media spotlight as much as the energy sector. This sector is facing great change, especially due to political influences. This change can also be observed at the supply chain level. The aim is to shed light on existing SCM concepts and to consider further coordination potentials.
(Dissertation project)
Risk management in distributed systems using the example of the banking crisis
The banking crisis from 2007 onwards has shown that some risks in the banking sector were misjudged and thus a large number of banks ended up in insolvency. The project therefore aims to take an analytical look at risk management in this distributed system. For this purpose, both the complex interconnections in the distributed system of the banking industry and the existing risks will be identified and modelled. By simulating and analysing the structures and the effects of crises, valuable insights can be gained regarding risk assessment and management in distributed systems.
(Dissertation project)
Wladimir Wenner

Completed projects (selection)

Influence of demand forecasts on the coordination of customer-supplier relationships
The subject of the study is a two-tier supply chain consisting of a supplier and a customer, the latter serving an end-customer market with stochastic demand. For example, the extent to which early announcement of demand by the customer can coordinate the supply chain under certain contract designs is investigated. In addition, converging as well as diverging supply chain structures are considered in this context.
(Dissertation project)
Philipp Schlüter
Design of an integrated risk management information system
The aim of the project is to analyse the weaknesses of existing risk management information systems (RMIS) and to develop a concept for improving these systems. For this purpose, on the one hand, the potential for improvement in risk management through the use of software is systematically worked out along the risk management process. On the other hand, the expansion potentials of existing RMIS are analysed. On this basis, a risk management information system is designed that eliminates the shortcomings of existing RMIS and expands them with functions that help companies to better cope with the problems and challenges in risk management.
(Dissertation project)
Tobias Anton
Business process analysis, optimisation and simulation using the example of an IT company
Optimisation of product tailoring and price structure for gas storage facilities
Market transparency in the banking sector and credit volume
Automation of data integration processes in the data warehouse - possibilities and limits using the example of the ETL process in zeb/control
Analytical customer relationship management - conception and realisation on the basis of the business intelligence instruments data warehouse and data mining.
The goal of all efforts in customer relationship management is additional customer acquisition and retention effects. Analytical customer relationship management (aCRM), which is built on the basis of the business intelligence (BI) instruments data warehouse and data mining, has been discussed as a solution for years. After reviewing the literature, however, it can be seen that there is still no self-contained business management concept using BI from conception to realisation. Therefore, the author Dr. Dirk Hiestermann developed a holistic approach that describes the procedure in aCRM and comprehensively illustrates it in the form of guidelines. The practical relevance is also demonstrated by a case study based on a company's customer data. The book is aimed at specialists and managers in sales/marketing, controlling and IT in order to obtain basic information and/or to introduce analytical CRM in the company. Due to its theoretical basis, the book is also predestined for students of business informatics and business administration.
(Dissertation project Dr. Dirk Hiestermann)
Concept for a distributed, self-learning control system based on process data in a distributed system
The calculated risk - risk cost accounting as an information system for integrated risk management
The importance of risk management is often only brought to people's attention in the event of spectacular corporate collapses or crises. However, this is the point in time when it is usually too late to effectively counteract a crisis. In many cases, only the symptoms can be treated. For this reason, every company should operate a risk information system that provides information about the risk exposure of all possible company divisions and their services. For better acceptance and ease of use, this is integrated into the existing systems instead of creating a new system. All risks are assessed monetarily with uniform standards so that it is immediately apparent which risks threaten the existence of the company and which are bearable. In order to be prepared for the occurrence of risks, the creation of reserves to "finance" risks that occur by pricing in risk costs is proposed. (Dissertation project Dr. Markus Siepermann)
Development of a strategic supplier management for the aviation industry and implementation of a supplier controlling based on it
Ethno-Marketing - Developing Russian-Speaking Customers in the German Banking Sector Using the Example of Berliner Volksbank
Conception and practical testing of a system for sequence planning for JIS productions
Quality management in the back office area of service companies using the example of a telecommunications service provider
Data mining to support decision-making processes
Data mining is known as the application of algorithms to determine data patterns from large data sets. This dissertation extends the discussion of data mining methods in the literature, which is mostly purely technical, to their potential for application in business management. It examines the support possibilities of operational decision-making processes through data mining methods. First, a formal 'construction kit' for the development of new data mining methods is introduced, which includes the design possibilities of data mining model types and search methods as well as the evaluation of the interestingness of economic models. A general scheme for supporting decision-making processes by data mining is derived from the consideration of economic data mining applications. The model type of the decision model is considered in more detail and a data mining procedure for generating decision models is developed. Finally, the method is evaluated on test data and applied to a problem for the selection of customers for a direct marketing campaign in the insurance market.
(Dissertation project Dr Christoph Tillmanns)
Decision Support Pontetials of Neural and Fuzzy-Based Approaches in Operational Materials Management
Computational intelligence (CI) methods are commonly associated with special artificial intelligence (AI) procedures that are not based on symbol processing like classical AI approaches and are also able to deal with the phenomenon of uncertainty. Neural and fuzzy-based approaches are well-known representatives of CI. However, the papers on their areas of application lack a well-founded analysis of the prerequisites for use as well as a consistent use of terms. Due to its variety of tasks and the differentiated nature of its decision problems, materials management represents a suitable field of research. The project analyses materials management and computational intelligence in terms of scientific theory and describes the possible applications of neural and fuzzy-based approaches in operational materials management. For this purpose, the term decision support is defined and a basic model is derived for this purpose. The theoretical treatises on the applicability of neuronal and fuzzy-based approaches in disposition, procurement, warehousing, transport and disposal tasks are practically tested by means of two case studies.
(Dissertation project Dr. Dagmar Mack)
Conception and development of a procedure for bottleneck-oriented material planning and scheduling in PPS systems.
MRP-based PPS systems are subject to increasing criticism. Although quantity planning is still sufficiently good, scheduling often leads to unsatisfactory results. This poor quality of results is expressed especially in the realisation phase, i.e. production control, through high workshop stocks, long lead times, low adherence to schedules, etc. Based on these points of criticism, the author develops in this paper a procedure for bottleneck-oriented production planning and control that is based on the MRP-based planning concept. The developed concept primarily includes the functions of production planning, and here in particular parts of material planning and scheduling and capacity planning. Although the successive planning idea is still followed within the procedure, a strict bottleneck orientation is integrated into production planning. This is characterised by the fact that within the planning steps of the PPS, all decisions are measured against their capacity and deadline effects and discarded in the event of possible violations. This approach is made possible by the explicit inclusion of the resources and procedural rules required for production execution. This means that the central decisions of production planning, such as order formation and scheduling of orders, are not made in relation to the product, but in relation to the workplace and the operation. This procedure is implemented by a heuristic, bottleneck-oriented method for scheduling and lot formation. The developed concept was implemented as a prototype. The results of concrete planning runs were compared with those of conventional PPS procedures. Overall, it was shown that bottleneck-oriented material planning and scheduling in the form proposed here leads to more favourable planning results and can be usefully integrated into the production planning of MRP-based PPS systems.
(Dissertation project Dr. Guido Schnödt)