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Inventory Auditing and Replenishment Using Point-of-Sales Data

36 Pages Posted: 28 Oct 2014 Last revised: 13 Dec 2017

Date Written: October 28, 2017


We study how inventory managers can fully utilize point-of-sales (POS) data for the design of inventory auditing and replenishment policies that account for the existence of phantom inventories. Events such as spoilage, expiration, employee theft, and customer shoplifting reduce inventories in retail stores without these reductions being reflected in inventory records. As a result, inventory records often include phantom inventories (i.e., units of goods present in inventory records that are not actually available for sale). These phantom inventories cause replenishment delays and stockouts, which ultimately hurt service levels. The optimal auditing and replenishment policy in the presence of phantom inventories is complex. We analyze the structure of the problem using tools from discrete mathematics and dynamic programming to derive a pseudo-polynomial algorithm for computing the optimal policy in our setting, as well as a simple policy that performs close to optimally. We propose a simple auditing and replenishment policy based on a threshold on the estimated fraction of demand to be met on a given day conditional on the POS data up to that day, a statistic that we refer to as the daily expected service level. We compare that policy with the optimal policy in our setting, which we compute algorithmically, and show it performs comparably. Our simple policy is easy to compute and interpret, and thus offers an attractive solution for inventory managers facing the challenge of phantom inventories.

Keywords: shelf availability, inventory-record inaccuracy, POMDP, optimal replenishment, retail analytics

JEL Classification: C11, D81

Suggested Citation

Bassamboo, Achal and Moreno, Antonio and Stamatopoulos, Ioannis, Inventory Auditing and Replenishment Using Point-of-Sales Data (October 28, 2017). Available at SSRN: or

Achal Bassamboo

Northwestern University - Department of Managerial Economics and Decision Sciences (MEDS) ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

Antonio Moreno

Harvard University - Technology & Operations Management Unit ( email )

Boston, MA 02163
United States


Ioannis Stamatopoulos (Contact Author)

University of Texas at Austin - McCombs School of Business ( email )

2110 Speedway B6000
Austin, TX 78705
United States

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