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.
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References
Wiendahl, H.P.: Betriebsorganisation für Ingenieure. Carl Hanser, München (2010)
Günther, H.O., Tempelmeier, H.: Produktion und Logistik. Springer, Berlin (2005)
Scholz-Reiter, B., Kück, M.: Auswahl von Prognoseverfahren für Kundenbedarfe - Erstellung einer Datenbank mit Handlungsempfehlungen zur Auswahl geeigneter Prognoseverfahren. In: Industrie Management, vol. 28, pp. 61–65. GITO, Berlin (2012)
Scholz-Reiter, B., Kück, M., Toonen, C.: Improved demand forecasting using local models based on delay time embedding. In: International Journal of Systems Applications Engineering& Development, vol. 6, pp. 17–27. University Press (2012)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1976)
Wedekind, H.: Ein Vorhersagemodell für sporadische Nachfragemengen bei der Lagerhaltung. In: Ablauf- und Planungsforschung, vol. 9, pp. 1–11. Oldenbourg, München (1968)
Altay, N., Litteral, L.A.: Service Parts Management—Demand Forecasting and Inventory Control. Springer, London (2011)
Croston, J.D.: Forecasting and stock control for intermittent demands. Oper. Res. Q. 23, 289–303 (1972)
Mertens, P., Rässler, S.: Prognoserechnung. Physica, Heidelberg (2005)
Alicke, K.: Planung und Betrieb von Logistiknetzwerken: Unternehmensübergreifendes Supply Chain Management. Springer, Berlin (2005)
Schlittgen, R., Streitberg, B.H.J.: Zeitreihenanalyse. Oldenbourg, München (1995)
Scholz-Reiter, B., Freitag, M., Schmieder, A., Müller, S.: A dynamical concept for production planning & control. In: Proceedings of 16th International Conference Production Research, pp. 27–40. Prague (2001)
Papakostas, N., Efthymiou, K., Mourtzis, D., Chryssolouris, G.: Modeling the complexity of manufacturing systems using non-linear dynamics approaches. CIRP Ann. Manuf. Technol. 58, 437–440 (2009)
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. University Press, Cambridge (2004)
Takens, F.: Detecting strange attractors in turbulence. In: Rand, D.A., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Lecture Notes in Mathematics. vol. 898, pp. 366–381. Springer, Warwick (1981)
Kennel, M., Brown, R., Abarbanel, H.D.I.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45, 3403–3411 (1992)
Fraser, A.M., Swinney, H.L.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33, 1134–1140 (1986)
Buzug, T., Pfister, G.: Optimal delay time and embedding dimension for delay-time coordinates by analysis of the global static and local dynamical behavior of strange attractors. Phys. Rev. Lett. A 45, 7073–7084 (1992)
Sauer, T.: Time series prediction by using delay coordinate embedding. In: Weigend, A.S., Gershenfeld, N.A. (eds) Time Series Prediction: Forecasting the Future and Understanding the Past. pp. 175–193. Addison-Wesley, Harlow (1994)
Wallström, P., Segerstedt, A.: Evaluation of forecasting error measurements and techniques for intermittent demand. Int. J. Prod. Econ. 128, 625–636 (2010)
Scholz-Reiter, B., Kück, M., Toonen, C.: Simulation-based generation of time series representing customer demands in networked manufacturing systems. In: Proceedings of 16th Annual International Conference Industrial Engineering Theory, Applications and Practice, pp. 488–493, Stuttgart (2011)
Sugihara, G., May, R.M.: Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734–741 (1990)
Acknowledgments
This research has been funded by German Research Foundation (DFG) under the reference number SCHO 540/21-1.
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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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