Analytics for Operational Visibility in the Retail Store: The Cases of Censored Demand and Inventory Record Inaccuracy

Li Chen and Adam J. Mersereau ()
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Li Chen: Duke University
Adam J. Mersereau: University of North Carolina

Chapter Chapter 5 in Retail Supply Chain Management, 2015, pp 79-112 from Springer

Abstract: Abstract Armed with a number of modern and emerging visibility technologies and facing increased competition from the internet channel, retail managers are seeking ever deeper visibility into store operations. We review two established streams of operations management research that try to overcome shortcomings of common retail data sources. The first is demand estimation and inventory optimization in the presence of data censoring, where imperfect data may cause significant estimation biases and inventory cost inefficiencies. The second is inventory record inaccuracy, where intelligent replenishment and inspection policies may be able to reduce inventory management costs even without real-time tracking technologies like radio frequency identification (RFID). Common themes of these literatures are that lack of visibility can be costly if not properly accounted for, that intelligent analytical approaches can potentially substitute for visibility provided by technology, and that understanding the best possible policy without visibility is needed to properly evaluate visibility technologies. We include a survey of modern and emerging visibility technologies and a discussion of several new avenues for analytical research.

Keywords: Inventory control; Bayesian models; Censored demand; Inventory record inaccuracy; Partially observed Markov decision process; Radio frequency identification; Store visibility technology (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (13)

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DOI: 10.1007/978-1-4899-7562-1_5

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