IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v24y1978i10p1043-1054.html
   My bibliography  Save this article

Predicting Demand from Sales Data in the Presence of Stockouts

Author

Listed:
  • William E. Wecker

    (University of Chicago)

Abstract

The effect of stockouts on prediction accuracy is analyzed. The forecasting bias that results and the effect on the prediction error variance are explored and are seen to depend on the frequency of stockouts, the coefficient of variation of demand, and the serial correlation of demand.

Suggested Citation

  • William E. Wecker, 1978. "Predicting Demand from Sales Data in the Presence of Stockouts," Management Science, INFORMS, vol. 24(10), pages 1043-1054, June.
    • Handle: RePEc:inm:ormnsc:v:24:y:1978:i:10:p:1043-1054
    DOI: 10.1287/mnsc.24.10.1043
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.24.10.1043
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.24.10.1043?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
    ---><---

    Citations

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


    Cited by:

    1. Anna‐Lena Sachs & Michael Becker‐Peth & Stefan Minner & Ulrich W. Thonemann, 2022. "Empirical newsvendor biases: Are target service levels achieved effectively and efficiently?," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1839-1855, April.
    2. Steven Nahmias, 1994. "Demand estimation in lost sales inventory systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(6), pages 739-757, October.
    3. Gen Sakoda & Hideki Takayasu & Misako Takayasu, 2019. "Data Science Solutions for Retail Strategy to Reduce Waste Keeping High Profit," Sustainability, MDPI, vol. 11(13), pages 1-30, June.
    4. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    5. Lau, Hon-Shiang & Hing-Ling Lau, Amy, 1996. "Estimating the demand distributions of single-period items having frequent stockouts," European Journal of Operational Research, Elsevier, vol. 92(2), pages 254-265, July.
    6. Vasudaven, M. & Nair, M. G. & Sithole, M. M., 1996. "On estimation for censored autoregressive data," Statistics & Probability Letters, Elsevier, vol. 31(2), pages 97-105, December.
    7. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    8. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    9. Opher Baron & Iman Hajizadeh & Joseph Milner, 2011. "Now Playing: DVD Purchasing for a Multilocation Rental Firm," Manufacturing & Service Operations Management, INFORMS, vol. 13(2), pages 209-226, April.
    10. Georgia Perakis & Melvyn Sim & Qinshen Tang & Peng Xiong, 2023. "Robust Pricing and Production with Information Partitioning and Adaptation," Management Science, INFORMS, vol. 69(3), pages 1398-1419, March.
    11. Hakan Uslu & Larry Teeter, 2017. "Shutdown Decision of Firms Based on Variable Costs and Demand," The American Economist, Sage Publications, vol. 62(1), pages 43-65, March.
    12. Gah-Yi Ban, 2020. "Confidence Intervals for Data-Driven Inventory Policies with Demand Censoring," Operations Research, INFORMS, vol. 68(2), pages 309-326, March.
    13. Narendra Agrawal & Stephen A. Smith, 1996. "Estimating negative binomial demand for retail inventory management with unobservable lost sales," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(6), pages 839-861, September.
    14. Aditya Jain & Nils Rudi & Tong Wang, 2015. "Demand Estimation and Ordering Under Censoring: Stock-Out Timing Is (Almost) All You Need," Operations Research, INFORMS, vol. 63(1), pages 134-150, February.
    15. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    16. Adam J. Mersereau, 2015. "Demand Estimation from Censored Observations with Inventory Record Inaccuracy," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 335-349, July.

    More about this item

    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:inm:ormnsc:v:24:y:1978:i:10:p:1043-1054. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.