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Potentials of Nonlinear Dynamics Methods to Predict Customer Demands in Production Networks

Part of the Lecture Notes in Production Engineering book series (LNPE)

Abstract

Nowadays, markets are characterized by increasing dynamics and complexity. In particular, customer demands are often highly volatile. These conditions complicate demand forecasting and reduce the average accuracy of forecasting data. Nevertheless, manufacturing companies have to predict customer demands precisely, in order to achieve a well-founded production planning and control. The paper at hand deals with methods to predict customer demands in application scenarios of production logistics. Firstly, forecasting methods for smooth customer demand are described with a particular emphasis on nonlinear dynamics methods. Subsequently, a new algorithm to predict intermittent demand is introduced. In both cases of demand evolution, different methods are applied to predict demand data generated by a discrete-event simulation of a production network. Forecasting results are interpreted and the different methods are rated regarding their applicability. The research displays that an application of nonlinear dynamics methods can lead to improved forecasting accuracy.

Keywords

  • Demand forecasting
  • Intermittent demand
  • Nonlinear dynamics
  • Time series analysis

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Acknowledgments

This research has been funded by German Research Foundation (DFG) under the reference number SCHO 540/21-1.

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Correspondence to Mirko Kück .

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Scholz-Reiter, B., Kück, M. (2013). Potentials of Nonlinear Dynamics Methods to Predict Customer Demands in Production Networks. In: Windt, K. (eds) Robust Manufacturing Control. Lecture Notes in Production Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30749-2_3

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  • DOI: https://doi.org/10.1007/978-3-642-30749-2_3

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