Data-Driven Order Policies with Censored Demand and Substitution in Retailing

Anna-Lena Sachs
Additional contact information
Anna-Lena Sachs: University of Cologne

Chapter Chapter 5 in Retail Analytics, 2015, pp 57-78 from Springer

Abstract: Abstract We extend the data-driven inventory model with censored demand to the two-product case with stockout-based substitution. If one product stocks out, a fraction of the demand that cannot be satisfied is shifted to the substitute. As a result, sales of the substitute are inflated by the additional demand. The amount of substituted demand as well as unobservable lost sales are estimated based on the timing of stockout events. Similarly to the model in Chap. 4, we establish sales patterns based on the hourly sales observations before a stockout occurs. Our numerical study and data from a large European retail chain shows that the data-driven model achieves higher average profits than an existing approach from the literature. Investigating the trade-off between learning about substitution behavior from highly censored data versus learning about demand from little censored data, we find that more learning about substitution yields slightly better results in terms of profits.

Keywords: Inventory Level; Optimal Order Quantity; Perishable Product; Inventory Optimization; Product Stock (search for similar items in EconPapers)
Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-319-13305-8_5

Ordering information: This item can be ordered from
http://www.springer.com/9783319133058

DOI: 10.1007/978-3-319-13305-8_5

Access Statistics for this chapter

More chapters in Lecture Notes in Economics and Mathematical Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().