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

In: Retail Supply Chain Management

Author

Listed:
  • Li Chen

    (Duke University)

  • Adam J. Mersereau

    (University of North Carolina)

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.

Suggested Citation

  • Li Chen & Adam J. Mersereau, 2015. "Analytics for Operational Visibility in the Retail Store: The Cases of Censored Demand and Inventory Record Inaccuracy," International Series in Operations Research & Management Science, in: Narendra Agrawal & Stephen A. Smith (ed.), Retail Supply Chain Management, edition 2, chapter 0, pages 79-112, Springer.
    • Handle: RePEc:spr:isochp:978-1-4899-7562-1_5
    DOI: 10.1007/978-1-4899-7562-1_5
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    Citations

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    Cited by:

    1. Li Chen, 2021. "Fixing Phantom Stockouts: Optimal Data‐Driven Shelf Inspection Policies," Production and Operations Management, Production and Operations Management Society, vol. 30(3), pages 689-702, March.
    2. Achal Bassamboo & Antonio Moreno & Ioannis Stamatopoulos, 2020. "Inventory Auditing and Replenishment Using Point‐of‐Sales Data," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1219-1231, May.
    3. Gah-Yi Ban, 2020. "Confidence Intervals for Data-Driven Inventory Policies with Demand Censoring," Operations Research, INFORMS, vol. 68(2), pages 309-326, March.
    4. Hickman, William & Mortimer, Julie Holland, 2016. "Demand Estimation with Availability Variation," SocArXiv qe69j, Center for Open Science.
    5. Yiangos Papanastasiou, 2020. "Newsvendor Decisions with Two-Sided Learning," Management Science, INFORMS, vol. 66(11), pages 5408-5426, November.
    6. 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.
    7. 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.
    8. Li Chen & Adam J.Mersereau & Zhe (Frank) Wang, 2017. "Optimal Merchandise Testing with Limited Inventory," Operations Research, INFORMS, vol. 65(4), pages 968-991, August.
    9. Weißhuhn, Sandria & Hoberg, Kai, 2021. "Designing smart replenishment systems: Internet-of-Things technology for vendor-managed inventory at end consumers," European Journal of Operational Research, Elsevier, vol. 295(3), pages 949-964.
    10. John S. Jatta & Krishna Kumar Krishnan, 2016. "An empirical assessment of a univariate time series for demand planning in a demand-driven supply chain," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(3), pages 269-290.
    11. Harun Avci & Kagan Gokbayrak & Emre Nadar, 2020. "Structural Results for Average‐Cost Inventory Models with Markov‐Modulated Demand and Partial Information," Production and Operations Management, Production and Operations Management Society, vol. 29(1), pages 156-173, January.
    12. Rong Li & Jing‐Sheng Jeannette Song & Shuxiao Sun & Xiaona Zheng, 2022. "Fight inventory shrinkage: Simultaneous learning of inventory level and shrinkage rate," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2477-2491, June.

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