nonlinear dynamics

Due to dynamics and complexity within production and delivery networks, customer demands are often highly volatile. In order to achieve a well-founded production planning and control, future customer demands have to be predicted precisely. Classical statistical forecasting methods are often easy to apply but are not able to react on dynamic effects within the data. Methods of nonlinear dynamics consider qualitative in addition to quantitative information within past order data to find possible deterministic structures and, as a result, to achieve better forecasts of the future. This article deals with the development of a data base containing recommendations to choose suitable prediction methods in different situations.

Very often material flows within or between companies seem to behave in an irregular manner, which can cause inventory levels to become unpredictable and poorly manageable. This behaviour can especially be observed at high utilisation ratios of means of production or logistics. Commonly, it is assumed to be caused by small disturbances, which can never be completely excluded in practice. For that reason often large efforts are made to minimise disturbances, but the question arises, if it always has to be disturbances that make predictions of e.g. inventory levels difficult for the near future and practically impossible in the long run. Using a small, but re-entrant manufacturing system, this article presents in-vestigations on how even relatively simple networks can develop irregular dynamics.