[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/h/spr/lnechp/978-3-319-13305-8_3.html
   My bibliography  Save this book chapter

Safety Stock Planning Under Causal Demand Forecasting

In: Retail Analytics

Author

Listed:
  • Anna-Lena Sachs

    (University of Cologne)

Abstract
Mainstream inventory management approaches typically assume a given theoretical demand distribution and estimate the required parameters from historical data. A time series based framework uses a forecast (and a measure of forecast error) to parameterize the demand model. However, demand might depend on many other factors rather than just time and demand history. Inspired by a retail inventory management application where customer demand, among other factors, highly depends on sales prices, price changes, weather conditions, this chapter presents two data-driven frameworks to set safety stock levels when demand depends on several exogenous variables. The first approach uses regression models to forecast demand and illustrates how estimation errors in this framework can be utilized to set required safety stocks. The second approach uses (Mixed-Integer) Linear Programming under different objectives and service level constraints to optimize a (linear) target inventory function of the exogenous variables. We illustrate the approaches using a case example and compare the two methods with respect to their ability to achieve target service levels and the impact on inventory levels in a numerical study. We show that considerable improvements of the overly simplifying method of moments are possible and that the ordinary least squares approach yields better performance than the LP-method, especially when the data sample for estimation is small and the objective is to satisfy a non-stockout probability constraint. However, if some of the standard assumptions of ordinary least squares regression are violated, the LP approach provides more robust inventory levels.

Suggested Citation

  • Anna-Lena Sachs, 2015. "Safety Stock Planning Under Causal Demand Forecasting," Lecture Notes in Economics and Mathematical Systems, in: Retail Analytics, edition 127, chapter 0, pages 13-33, Springer.
    • Handle: RePEc:spr:lnechp:978-3-319-13305-8_3
    DOI: 10.1007/978-3-319-13305-8_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:lnechp:978-3-319-13305-8_3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.