Dennis Horstkemper

Adaption of the discrete rate-based simulation paradigm for tactical supply chain decisions

The relative novel discrete rate-based simulation paradigm combines the advantages of the discrete event-based and the system dynamics simulation paradigms. Although its applicability is generally acknowledged in the context of supply chain management, no research works exist, that allow for a direct modeling and simulation of supply chain planning decisions within this paradigm. This paper therefore presents necessary adaptations of the discrete rate-based simulation paradigm for tactical supply chain planning decisions. Our main research contribution lies in extending the discrete rate-based simulation with modeling and material flow controlling mechanisms for enabling a simple implementation and simulation of tactical supply chain planning tasks. To evaluate different planning decisions in this context a multitude of simulation runs are necessary, creating a need for fast simulation approaches. Thus, we show formally, that discrete rate-based simulation models can generally be computed faster than commonly used discrete event-based simulation models.

Automated scalable modeling for population microsimulations

The propagation of diseases within the population is an ever-reappearing hot topic in news stories. Mutations of known diseases repeatedly infected large portions of the population. In order to support the select appropriate mechanisms to help taming the spread of an epidemic, several simulation approaches have been developed to forecast the propagation behavior of diseases. Agent-based micro simulations promise to create the most detailed and accurate forecasts, but require a high modeling effort. In this paper we propose an approach to lessen this modeling effort by introducing a method that automatically creates agents for representing groups within the population based on multiple data sources (e.g. census data, vaccinations records, etc.). Our approach also facilitates combining these heterogeneous data with geographic information systems as well as dealing with incomplete data for enabling automatic and scalable creation of epidemics models for different simulation purposes in epidemiology. Two test cases were used to assess the proposition.

Using Capability-Based Planning for Designing Cooperation Between Automotive Manufacturers and Logistics Service Providers

Automotive Original Equipment Manufacturers (OEMs) are constantly establishing new manufacturing plants in various countries. The establishment of new plants offers plenty of opportunities to the OEM but at the same time the problem to integrate these new plants into its global production and logistics network is posed. One way of handling this problem is to outsource some of the needed capabilities (macro level business functions, e.g. warehouse management) to Logistics Service Providers (LSPs). The OEM is then faced with the question, which capabilities to outsource when establishing a new plant. The LSP on the other hand has to evaluate which products and services to offer to an OEM. The purpose of this paper is to outline the development of a methodology based on techniques from Enterprise Architecture (EA) and more specifically Capability-based Planning (CBP) that helps answer these questions.