IDEAS home Printed from https://ideas.repec.org/a/abk/jajeba/ajebasp.2010.90.102.html
   My bibliography  Save this article

On Replenishing Items with Seasonal Intermittent Demand

Author

Listed:
  • Gary Mitchell
  • Meike Niederhausen

Abstract

Problem statement: There are numerous difficulties associated with replenishing intermittent demand items and these are compounded when the demand distribution(s) vary seasonally. Excess inventories during an “off” season are typical, while shortages frequently occur during the “in” season, especially at the transition points between “seasons”. Approach: Evaluate the extent to which items characterized by non-stationary (seasonal) intermittent demand can be managed with commonly used forecasting and replenishment methods, including existing “intermittent demand” methods. Extensive simulation studies were conducted using combinations of two commonly used forecasting methods and two replenishment policies to evaluate the impact of non-stationary intermittent demand on key inventory performance measures, including average inventory and net profits, including the extent to which combinations of forecasting and replenishment models is adversely impacted by the non-stationary demand distributions. Results: No combination of forecasting and replenishment methods tested consistently outperformed the others and all methods demonstrated a propensity to replace demanded units an average of 10 weeks before the inventory was required to avert a shortage. Conclusion: The inventory performance of the policies tested was consistent with our expectations and offered evidence of the need for further development of forecasting and replenishment models addressing the special characteristics of items with non-stationary intermittent demand.

Suggested Citation

  • Gary Mitchell & Meike Niederhausen, 2010. "On Replenishing Items with Seasonal Intermittent Demand," American Journal of Economics and Business Administration, Science Publications, vol. 2(1), pages 90-102, March.
  • Handle: RePEc:abk:jajeba:ajebasp.2010.90.102
    DOI: 10.3844/ajebasp.2010.90.102
    as

    Download full text from publisher

    File URL: https://thescipub.com/pdf/ajebasp.2010.90.102.pdf
    Download Restriction: no

    File URL: https://thescipub.com/abstract/ajebasp.2010.90.102
    Download Restriction: no

    File URL: https://libkey.io/10.3844/ajebasp.2010.90.102?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Syntetos, A. A. & Boylan, J. E., 2001. "On the bias of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 457-466, May.
    2. Rob J. Hyndman & Lydia Shenstone, 2005. "Stochastic models underlying Croston's method for intermittent demand forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 389-402.
    3. A H C Eaves & B G Kingsman, 2004. "Forecasting for the ordering and stock-holding of spare parts," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(4), pages 431-437, April.
    4. Boylan, J.E. & Syntetos, A.A., 2007. "The accuracy of a Modified Croston procedure," International Journal of Production Economics, Elsevier, vol. 107(2), pages 511-517, June.
    5. Syntetos, Aris A. & Boylan, John E., 2006. "On the stock control performance of intermittent demand estimators," International Journal of Production Economics, Elsevier, vol. 103(1), pages 36-47, September.
    6. Z S Hua & B Zhang & J Yang & D S Tan, 2007. "A new approach of forecasting intermittent demand for spare parts inventories in the process industries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 52-61, January.
    7. Carl R. Schultz, 1989. "Replenishment Delays for Expensive Slow-Moving Items," Management Science, INFORMS, vol. 35(12), pages 1454-1462, December.
    8. Gutierrez, Rafael S. & Solis, Adriano O. & Mukhopadhyay, Somnath, 2008. "Lumpy demand forecasting using neural networks," International Journal of Production Economics, Elsevier, vol. 111(2), pages 409-420, February.
    9. 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.
    10. Porras, Eric & Dekker, Rommert, 2008. "An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods," European Journal of Operational Research, Elsevier, vol. 184(1), pages 101-132, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Edward J. Lusk & Michael Halperin & Atanas Tetikov & Niya Stefanova, 2010. "Forecasting Financial Market Annual Performance Measures: Further Evidence +," American Journal of Economics and Business Administration, Science Publications, vol. 2(3), pages 300-306, September.
    2. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.

    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. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    2. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.
    3. 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.
    4. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    5. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    6. Pennings, Clint L.P. & van Dalen, Jan & van der Laan, Erwin A., 2017. "Exploiting elapsed time for managing intermittent demand for spare parts," European Journal of Operational Research, Elsevier, vol. 258(3), pages 958-969.
    7. Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
    8. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    9. Altay, Nezih & Rudisill, Frank & Litteral, Lewis A., 2008. "Adapting Wright's modification of Holt's method to forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 111(2), pages 389-408, February.
    10. Jože Martin Rožanec & Blaž Fortuna & Dunja Mladenić, 2022. "Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    11. Hasni, M. & Babai, M.Z. & Aguir, M.S. & Jemai, Z., 2019. "An investigation on bootstrapping forecasting methods for intermittent demands," International Journal of Production Economics, Elsevier, vol. 209(C), pages 20-29.
    12. Syntetos, Aris A. & Zied Babai, M. & Gardner, Everette S., 2015. "Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping," Journal of Business Research, Elsevier, vol. 68(8), pages 1746-1752.
    13. Syntetos, A.A. & Babai, M.Z. & Davies, J. & Stephenson, D., 2010. "Forecasting and stock control: A study in a wholesaling context," International Journal of Production Economics, Elsevier, vol. 127(1), pages 103-111, September.
    14. Hasni, M. & Aguir, M.S. & Babai, M.Z. & Jemai, Z., 2019. "On the performance of adjusted bootstrapping methods for intermittent demand forecasting," International Journal of Production Economics, Elsevier, vol. 216(C), pages 145-153.
    15. Li, Chongshou & Lim, Andrew, 2018. "A greedy aggregation–decomposition method for intermittent demand forecasting in fashion retailing," European Journal of Operational Research, Elsevier, vol. 269(3), pages 860-869.
    16. Pierre Dodin & Jingyi Xiao & Yossiri Adulyasak & Neda Etebari Alamdari & Lea Gauthier & Philippe Grangier & Paul Lemaitre & William L. Hamilton, 2023. "Bombardier Aftermarket Demand Forecast with Machine Learning," Interfaces, INFORMS, vol. 53(6), pages 425-445, November.
    17. Babai, M.Z. & Dallery, Y. & Boubaker, S. & Kalai, R., 2019. "A new method to forecast intermittent demand in the presence of inventory obsolescence," International Journal of Production Economics, Elsevier, vol. 209(C), pages 30-41.
    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. Wallström, Peter & Segerstedt, Anders, 2010. "Evaluation of forecasting error measurements and techniques for intermittent demand," International Journal of Production Economics, Elsevier, vol. 128(2), pages 625-636, December.
    20. A A Syntetos & M Z Babai & Y Dallery & R Teunter, 2009. "Periodic control of intermittent demand items: theory and empirical analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 611-618, May.

    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:abk:jajeba:ajebasp.2010.90.102. 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: Jeffery Daniels (email available below). General contact details of provider: https://thescipub.com .

    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.