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Retail Supply Chain Management pp 79–112Cite as

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

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 223))

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.

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Notes

  1. 1.

    The result is difficult to prove generally. Song (1994) includes a detailed analysis of the conditions required to rank newsvendor stocking quantities for different probability distributions, and these conditions are difficult to verify here.

  2. 2.

    Here we depart slightly from DeHoratius and Ton (2015) in terminology. DeHoratius and Ton (2015) define “inventory record inaccuracy” as the difference between a store’s recorded inventory position and the physical inventory in the store. Misplaced inventory, which is physically present in the store, does not contribute to inventory record inaccuracy in this definition. In our discussion, we will liberally use the term “inventory record inaccuracy” to refer to the difference between customer-available inventory and recorded inventory. That is, we consider misplaced inventory to be part of inventory record inaccuracy.

References

  • Agrawal, N., & Smith, S. (1996). Estimating negative binomial demand for retail inventory management with unobservable lost sales. Naval Research Logistics, 43, 839–861.

    Google Scholar 

  • Anupindi, R., Dada, M., & Gupta, S. (1998). Estimation of consumer demand with stock-out based substitution: An application to vending machine products. Marketing Science, 17(4), 406–423.

    Google Scholar 

  • Atali, A., Lee, H., & Özer, Ö. (2011). If the inventory manager knew: Value of visibility and RFID under imperfect inventory information. Stanford University, Working Paper.

    Google Scholar 

  • Avrahami, A., Tzimerman, A., Herer, Y. T., & Shtub, A. (2012). The value of inventory accuracy in supply chain management. Technion: Israel Institute of Technology, Working Paper.

    Google Scholar 

  • Azoury, K. S. (1985). Bayes solution to dynamic inventory models under unknown demand distribution. Management Science, 31(9), 1150–1160.

    Google Scholar 

  • Bensoussan, A., Cakanildirim, M., Sethi, S. P., & Shi, R. (2011a). Calculation of approximate optimal policies in a partially observed inventory model with rain checks. Automatica, 47, 1589–1604.

    Google Scholar 

  • Bensoussan, A., Cakanyildirim, M., Li, M., & Sethi, S. P. (2011b). Inventory control with a cash register: Sales recorded but not demand or shrinkage. University of Texas at Dallas, Working Paper.

    Google Scholar 

  • Bensoussan, A., Cakanyildirim, M., Minjárez-Sosa, j. A., Sethi, S. P., & Shi, R. (2008). Partially observed inventory systems: The case of rain checks. SIAM Journal on Control and Optimization, 47(5), 2490–2519.

    Google Scholar 

  • Bensoussan, A., Cakanyildirim, M., & Sethi, S. (2005). On the optimal control of partially observed inventory systems. Comptes Rendus Mathematique, 341, 419–426.

    Google Scholar 

  • Bensoussan, A., Cakanyildirim, M., & Sethi, S. (2007a). A multi-period newsvendor problem with partially observed demand. Mathematics of Operations Research, 32(2), 322–344.

    Google Scholar 

  • Bensoussan, A., Cakanyildirim, M., & Sethi, S. (2007b). Partially observed inventory systems: The case of zero balance walk. SIAM Journal on Control and Optimization, 46(1), 176–209.

    Google Scholar 

  • Bensoussan, A., Cakanyildirim, M., & Sethi, S. (2009). A note on ‘the censored newsvendor and the optimal acquisition of information’. Operations Research, 57(3), 791–794.

    Google Scholar 

  • Besbes, O., & Muharremoglu, A. (2013). On implications of demand censoring in the newsvendor problem. Management Science, 59(6), 1407–1424.

    Google Scholar 

  • Bisi, A., & Dada, M. (2007). Dynamic learning, pricing, and ordering by a censored newsvendor. Naval Research Logistics, 54, 448–461.

    Google Scholar 

  • Bisi, A., Dada, M., & Tokdar, S. (2011). A censored-data multiperiod inventory problem with newsvendor demand distributions. Manufacturing Service Operations Management, 37(11), 1390–1405.

    Google Scholar 

  • Braden, D. J., & Freimer, M. (1991). Informational dynamics of censored observations. Management Science, 37(11), 1390–1405.

    Google Scholar 

  • Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing. MIT Sloan Management Review, 54(4), 23–29.

    Google Scholar 

  • Burnetas, A., & Smith, C. (2000). Adaptive ordering and pricing for perishable products. Operations Research, 48(3), 436–443.

    Google Scholar 

  • Camdereli, A. Z., & Swaminathan, J. M. (2010). Misplaced inventory and radio-frequency identification (RFID) technology: Information and coordination. Production Operations Management, 19(1), 1–18.

    Google Scholar 

  • Chen, L. (2010). Bounds and heuristics for optimal Bayesian inventory control with unobserved lost sales. Operations Research, 58(2), 396–413.

    Google Scholar 

  • Chen, L. (2013a). Bayesian dynamic pricing with two-sided censored customer willingness-to-pay data. Duke University, Working Paper.

    Google Scholar 

  • Chen, L. (2013b). Fixing phantom stockouts: Optimal data-driven shelf inspection policies. Duke University, Working Paper.

    Google Scholar 

  • Chen, L., & Plambeck, E. L. (2008). Dynamic inventory management with learning about the demand distribution and substitution probability. Manufacturing Service Operations Management, 10(2), 236–256.

    Google Scholar 

  • Chuang, H., & Oliva, R. (2013). Empirical modeling of inventory record audit policies. Texas A&M University, Working Paper.

    Google Scholar 

  • Chuang, H., Oliva, R., & Liu, S. (2012). On-shelf availability, retail performance, and external audits: A field experiment. Texas A&M University, Working Paper.

    Google Scholar 

  • Conlon, C. T., & Mortimer, J. H. (2013). Demand estimation under incomplete product availability. American Economic Journal: Microeconomics, 5(4), 1–30.

    Google Scholar 

  • Conrad, S. A. (1976). Sales data and the estimation of demand. Operational Research Quarterly, 27(1), 123–127.

    Google Scholar 

  • DeGroot, M. (1970). Optimal statistical decisions. New York: McGraw-Hill.

    Google Scholar 

  • DeHoratius, N., Mersereau, A., & Schrage, L. (2008). Retail inventory management when records are inaccurate. Manufacturing Service Operations Management, 10(2), 257–277.

    Google Scholar 

  • DeHoratius, N., & Raman, A. (2008). Inventory record inaccuracy: An empirical analysis. Management Science, 54(4), 627–641.

    Google Scholar 

  • DeHoratius, N., & Ton, Z. (2015). The role of execution in managing product availability. In N. DeHoratius & Z. Ton (Eds.), Retail supply chain management. Quantitative models and empirical studies. New York: Springer.

    Google Scholar 

  • Ding, X., Puterman, M., & Bisi, A. (2002). The censored newsvendor and the optimal acquisition of information. Operations Research, 50(3), 517–527.

    Google Scholar 

  • Fisher, M., & Raman, A. (2010). The new science of retailing. Boston: Harvard Business Press.

    Google Scholar 

  • Gaukler, G. M., Seifert, R. W., & Hausman, W. H. (2007). Item-level RFID in the retail supply chain. Production and Operations Management, 16(1), 65–76.

    Google Scholar 

  • Godfrey, G., & Powell, W. (2001). An adaptive, distribution-free algorithm for the newsvendor problem with censored demands, with applications to inventory and distribution. Management Science, 47(8), 1101–1112.

    Google Scholar 

  • Gruen, T. W., & Corsten, D. (2008). A comprehensive guide to retail out-of-stock reduction in the fast-moving consumer goods industry. Arlington (VA): Food Marketing Institute.

    Google Scholar 

  • Hardgrave, B. C., Aloysius, J. A., & Goyal, S. (2013). RFID-enabled visibility and retail inventory record inaccuracy: Experiments in the field. Production and Operations Management, 22(4), 843–856.

    Google Scholar 

  • Harpaz, G., Lee, W., & Winkler, R. (1982). Learning, experimentation, and the optimal output decisions of a competitive firm. Management Science, 28, 589–603.

    Google Scholar 

  • Heese, H. S. (2007). Inventory record inaccuracy, double marginalization, and RFID adoption. Production Operations Management, 16(5), 542–553.

    Google Scholar 

  • Heyman, D., & Sobel, M. (1984). Stochastic models in operations research, volume II: Stochastic optimization. New York: McGraw-Hill.

    Google Scholar 

  • Huh, W. T., Levi, R., Rusmevichientong, P., & Orlin, J. B. (2011). Adaptive data-driven inventory control with censored demand based on Kaplan-Meier estimator. Operations Research, 59(4), 929–941.

    Google Scholar 

  • Huh, W. T., Olvera-Cravioto, M., & Özer, Ö. (2010). Joint audit and replenishment decisions for an inventory system with unrecorded demands. Columbia University, Working Paper.

    Google Scholar 

  • Huh, W. T., & Rusmevichientong, P. (2009). A non-parametric asymptotic analysis of inventory planning with censored demand. Mathematics of Operations Research, 34(1), 103–123.

    Google Scholar 

  • Iglehart, D. L., & Morey, R. C. (1972). Inventory systems with imperfect asset information. Management Science, 18(8), B338–B394.

    Google Scholar 

  • Jain, A., Rudi, N., & Wang, T. (2014). Demand estimation and ordering under censoring: Stockout timing is (almost) all you need. Operations Research (forthcoming).

    Google Scholar 

  • Kang, Y., & Gershwin, S. (2005). Information inaccuracy in inventory systems: Stock loss and stockout. IIE Transactions, 37, 843–859.

    Google Scholar 

  • Kaplan, E., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.

    Google Scholar 

  • Kök, A. G., & Shang, K. (2007). Inspection and replenishment policies for systems with inventory record inaccuracy. Manufacturing Service Operations Management, 9(2), 185–205.

    Google Scholar 

  • Kök, A. G., & Shang, K. (2014). Evaluation of cycle-count policies for supply chains with inventory inaccuracy and implications on RFID investments. European Journal of Operational Research, 237(1), 91–105 (Note: This reference was formerly titled “Evaluation of supply chains with inventory record inaccuracy and implications on RFID investments”).

    Google Scholar 

  • Lariviere, M., & Porteus, E. (1999). Stalking information: Bayesian inventory management with unobserved lost sales. Management Science, 45(3), 346–363.

    Google Scholar 

  • Lee, H., & Özer, Ö. (2007). Unlocking the value of RFID. Production and Operations Management, 16(1), 40–64.

    Google Scholar 

  • Lovejoy, W. (1990). Myopic policies for some inventory models with uncertain demand distributions. Management Science, 36(6), 724–738.

    Google Scholar 

  • Lu, X., Song, J.-S., & Zhu, K. (2005). On ‘the censored newsvendor and the optimal acquisition of information’. Operations Research, 53(6), 1024–1027.

    Google Scholar 

  • Lu, X., Song, J.-S., & Zhu, K. (2007). Inventory control with unobservable lost sales and Bayesian updates. Duke University, Working Paper.

    Google Scholar 

  • Lu, X., Song, J.-S., & Zhu, K. (2008). Analysis of perishable-inventory systems with censored demand data. Operations Research, 56(4), 1034–1038.

    Google Scholar 

  • Lu, Y., Musalem, A., Olivares, M., & Schilkrut, A. (2013). Measuring the effect of queues on customer purchases. Management Science, 59(8), 1743–1763.

    Google Scholar 

  • Mani, V., Kesavan, S., & Swaminathan, J. M. (2014). Estimating the impact of understaffing on sales and profitability in retail stores. Production and Operations Management (forthcoming).

    Google Scholar 

  • McKinsey Global Institute. (2011). Big data: The next frontier for innovation, competition and productivity. Accessed April 9, 2013, from www.mckinsey.com/mgi

  • Mersereau, A. J. (2013). Information-sensitive replenishment when inventory records are inaccurate. Production and Operations Management, 22(4), 792–810.

    Google Scholar 

  • Mersereau, A. J. (2014). Demand estimation from censored observations with inventory record inaccuracy. Manufacturing and Service Operations Management (forthcoming).

    Google Scholar 

  • Morey, R. C. (1985). Estimating service level impacts from changes in cycle count, buffer stock, or corrective action. Journal of Operations Management, 5(4), 411–418.

    Google Scholar 

  • Morey, R. C., & Dittman, D. A. (1986). Optimal timing of account audits in internal control. Management Science, 32(3), 272–282.

    Google Scholar 

  • Motorola Retail. (2008). The next revolution in retail technology. Accessed July 23, 2013, from www.motorolasolutions.com

  • Musalem, A., Olivares, M., Bradlow, E. T., Terwiesch, C., & Corsten, D. (2010). Structural estimation of the effect of out-of-stocks. Management Science, 56(7), 1180–1197.

    Google Scholar 

  • Nahmias, S. (1994). Demand estimation in lost sales inventory systems. Naval Research Logistics, 41, 739–757.

    Google Scholar 

  • Papadimitriou, C., & Tsitsiklis, J. N. (1987). The complexity of Markov decision processes. Mathematics of Operations Research, 12, 441–450.

    Google Scholar 

  • Perdikaki, O., Kesavan, S., & Swaminathan, J. M. (2012). Effect of traffic on sales and conversion rates of retail stores. Manufacturing Service Operations Management, 14(1), 145–162.

    Google Scholar 

  • Powell, W., Ruszczynski, A., & Topaloglu, H. (2004). Learning algorithms for separable approximations of discrete stochastic optimization problems. Mathematics of Operations Research, 29(4), 814–836.

    Google Scholar 

  • Rekik, Y., Sahin, E., & Dallery, Y. (2008). Analysis of the impact of RFID technology on reducing product misplacement errors at retail stores. International Journal of Production Economics, 112(1), 264–278.

    Google Scholar 

  • Rekik, Y., Sahin, E., & Dallery, Y. (2009). Inventory inaccuracy in retail stores due to theft: An analysis of the benefits of RFID. International Journal of Production Economics, 118, 189–198.

    Google Scholar 

  • Sahin, E., & Dallery, Y. (2009). Assessing the impact of inventory inaccuracies within a newsvendor framework. European Journal of Operational Research, 197, 1108–1118.

    Google Scholar 

  • Scarf, H. (1959). Bayes solution of the statistical inventory problem. Annals of Mathematical Statistics, 30, 490–508.

    Google Scholar 

  • Scarf, H. (1960). Some remarks on bayes solutions to the inventory problem. Naval Research Logistics, 7, 591–596.

    Google Scholar 

  • Silver, E. (1993). Bayesian updating of an arbitrary discrete distribution under a special case of partial information. Communications in Statistics Stochastic Models, 9(4), 615–638.

    Google Scholar 

  • Song, J.-S. (1994). Leadtime uncertainty in a simple stochastic inventory model. Management Science, 40(5), 603–613.

    Google Scholar 

  • Vulcano, G., van Ryzin, G., & Ratliff, R. (2012). Estimating primary demand for substitutable products from sales transaction data. Operations Research, 60(2), 313–334.

    Google Scholar 

  • Waller, M. A., Cromhout, D. B., Patton, J. B., Williams, B. D., & Hardgrave, B. C. (2011). An empirical study of potential uses of RFID in the apparel retail supply chain. University of Arkansas Information Technology Research Institute, Working Paper ITRI-WP156-0111.

    Google Scholar 

  • Wecker, W. E. (1978). Predicting demand from sales data in the presence of stockouts. Management Science, 24(10), 1043–1054.

    Google Scholar 

  • Yano, C. A., & Lee, H. L. (1995). Lot sizing with random yields: A review. Operations Research, 43(2), 311–334.

    Google Scholar 

  • Zipkin, P. (2000). Foundations of inventory management. Boston: McGraw-Hill.

    Google Scholar 

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Acknowledgements

The authors thank the editors and an anonymous reviewer for constructive comments that greatly improved the chapter. They thank Jan Davis, Nicole DeHoratius, Saravanan Kesavan, Marcelo Olivares, Ariel Schilkrut, and participants in the 2013 Consortium for Operational Excellence in Retailing annual conference and in the 2013 UNC Retail Conference for valuable discussions and input.

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Authors and Affiliations

  1. Fuqua School of Business, Duke University, Durham, NC, USA

    Li Chen

  2. Kenan-Flagler Business School, University of North Carolina, Chapel Hill, NC, USA

    Adam J. Mersereau

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  1. Li Chen
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Correspondence to Adam J. Mersereau .

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Editors and Affiliations

  1. Department of Operations and Management and Information Systems, Leavey School of Business, Santa Clara University, Santa Clara, California, USA

    Narendra Agrawal

  2. Department of Operations and Management and Information Systems, Leavey School of Business, Santa Clara University, Santa Clara, California, USA

    Stephen A. Smith

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Chen, L., Mersereau, A.J. (2015). Analytics for Operational Visibility in the Retail Store: The Cases of Censored Demand and Inventory Record Inaccuracy. In: Agrawal, N., Smith, S. (eds) Retail Supply Chain Management. International Series in Operations Research & Management Science, vol 223. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7562-1_5

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