IDEAS home Printed from https://ideas.repec.org/p/zbw/bayism/57.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/111290/1/826707769.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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, April.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Goodwin, Paul & Lawton, Richard, 1999. "On the asymmetry of the symmetric MAPE," International Journal of Forecasting, Elsevier, vol. 15(4), pages 405-408, October.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    2. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    3. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.
    4. K Nikolopoulos & A A Syntetos & J E Boylan & F Petropoulos & V Assimakopoulos, 2011. "An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 544-554, March.
    5. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    6. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    7. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    8. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    9. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    10. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    11. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    12. Mariusz Doszyn, 2020. "Accuracy of Intermittent Demand Forecasting Systems in the Enterprise," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 912-930.
    13. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886, July.
    14. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
    15. Moons, Karen & Waeyenbergh, Geert & Pintelon, Liliane, 2019. "Measuring the logistics performance of internal hospital supply chains – A literature study," Omega, Elsevier, vol. 82(C), pages 205-217.
    16. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    17. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    18. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    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.
    20. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.

    More about this item

    Keywords

    Prognose- und Dispositionsentscheidungen; Krankenhausbereich; Simulationstool;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:bayism:57. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/rwbayde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.