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Entwicklung eines Simulationstools zur Analyse von Prognose- und Dispositionsentscheidungen im Krankenhausbereich

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  • Jussim, Maxim

Abstract

In der heutigen Zeit steht der Krankenhausbereich unter einem enormen Kostendruck. Viele Initiativen diesem entgegenzuwirken sind auf das Bestandsmanagement gerichtet, da hier Einsparpotentiale vermutet werden. Entscheidungen über die Wahl eines Prognoseverfahrens sowie einer effektiven Materialdisposition, spielen hierbei eine zentrale Rolle. Zu diesem Zweck wird in der vorliegenden Arbeit ein Simulationstool entwickelt, mit dem Entscheidungen in diesen beiden Bereichen simuliert werden können. Es werden die Prognoseverfahren der exponentiellen Glättung, sowie das Verfahren von Croston mit seinen Erweiterungen, implementiert. Bezüglich der Materialdisposition wurden, neben heuristischen Nachschubstrategien, Verfahren zur Optimierung von Bestellpunkt und Bestellmenge implementiert. Nach einer Validierung wird das Simulationsprogramm anhand realer Daten eines Logistikdienstleisters im medizinischen Bereich praktisch evaluiert. Dabei wurde die Konsistenz der Simulationsergebnisse mit den zugrundeliegenden Modellen bestätigt. Außerdem deuten die Simulationsergebnisse darauf hin, dass der Logistikdienstleister mit der richtigen Kombination aus Prognosemethoden und Nachschubstrategie eine Lagerkostensenkung von bis zu 37% realisieren kann.

Suggested Citation

  • Jussim, Maxim, 2014. "Entwicklung eines Simulationstools zur Analyse von Prognose- und Dispositionsentscheidungen im Krankenhausbereich," Bayreuth Reports on Information Systems Management 57, University of Bayreuth, Chair of Information Systems Management.
  • Handle: RePEc:zbw:bayism:57
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    1. A A Syntetos & J E Boylan & J D Croston, 2005. "On the categorization of demand patterns," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 495-503, May.
    2. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    3. Barry L. Nelson, 2013. "Foundations and Methods of Stochastic Simulation," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-6160-9, September.
    4. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.
    5. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    6. Mohammaditabar, Davood & Hassan Ghodsypour, Seyed & O'Brien, Chris, 2012. "Inventory control system design by integrating inventory classification and policy selection," International Journal of Production Economics, Elsevier, vol. 140(2), pages 655-659.
    7. de Vries, Jan, 2011. "The shaping of inventory systems in health services: A stakeholder analysis," International Journal of Production Economics, Elsevier, vol. 133(1), pages 60-69, September.
    8. Carles Gríful-Miquela, 2001. "Activity-based costing methodology for third-party logistics companies," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 7(1), pages 133-146, February.
    9. Aris Syntetos & John Boylan & Ruud Teunter, 2011. "Classification for Forecasting and Inventory," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 20, pages 12-17, Winter.
    10. Manuel D. Rossetti & Nebil Buyurgan & Edward Pohl, 2012. "Medical Supply Logistics," International Series in Operations Research & Management Science, in: Randolph Hall (ed.), Handbook of Healthcare System Scheduling, chapter 0, pages 245-280, Springer.
    11. Nicholson, Lawrence & Vakharia, Asoo J. & Selcuk Erenguc, S., 2004. "Outsourcing inventory management decisions in healthcare: Models and application," European Journal of Operational Research, Elsevier, vol. 154(1), pages 271-290, April.
    12. M Bijvank & I F A Vis, 2012. "Inventory control for point-of-use locations in hospitals," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(4), pages 497-510, April.
    13. Teunter, Ruud & Sani, Babangida, 2009. "On the bias of Croston's forecasting method," European Journal of Operational Research, Elsevier, vol. 194(1), pages 177-183, April.
    14. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    15. Bernd Scholz‐Reiter & Jens Heger & Christian Meinecke & Johann Bergmann, 2012. "Integration of demand forecasts in ABC‐XYZ analysis: practical investigation at an industrial company," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 61(4), pages 445-451, April.
    16. McKenzie, Eddie & Gardner Jr., Everette S., 2010. "Damped trend exponential smoothing: A modelling viewpoint," International Journal of Forecasting, Elsevier, vol. 26(4), pages 661-665, October.
    17. Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
    18. Rob J. Hyndman, 2006. "Another Look at Forecast Accuracy Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 43-46, June.
    19. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
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    Keywords

    Prognose- und Dispositionsentscheidungen; Krankenhausbereich; Simulationstool;
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