Business Administration and Economics
The MAEKAS research project is concerned with “management of project-based alliances between local and supra-regional railway companies for customer-specific acquisition strategies. Its starting point is the considerable growth in the market for industrial freight in recent years. The role of railway carriers in this market has been disproportionately small: from the customers perspective, road transport often offers more convenient services at viable prices and greater flexibility than its rail-based counterpart.
The joint MAEKAS project takes up this point and proves that railway carriers can indeed accommodate individual customer requests and compete with road-based carriers on price. The projects bundling strategy offers regional railway carriers a way of organising individual consignments according to quantity, time and destination to allow economical transportation.
Industrial partners to this project are SBB Cargo GmbH as project coordinator, Mülheimer VerkehrsGesellschaft mbH, Neuss-Düsseldorfer Häfen GmbH & Co. KG, and Wanne-Herner Eisenbahn und Hafen GmbH. Scientific responsibility for the project lies with the Institute of Production and Industrial Information Management (PIM) at the Faculty of Business Administration and Economics under Prof. Stephan Zelewski. This institute develops concepts to improve the competitiveness of the participating logistics companies and sees the concepts through to their implementation.
MAEKAS is another good example of the linkage between various disciplines. For example, PIM is developing software for intelligent bundling of single wagon freight transport. These complex mathematical optimisation problems are mainly solved using quantitative techniques from the field of Operations Research.
The research and university-industry crossover for this project are being funded for a three-year period by the Federal Ministry of Economics and Technology within a framework concept entitled “Intelligente Logistik im Güter- und Wirtschaftsverkehr.