Traditional inventory models, with a few exceptions, do not account for the existence of inventory record inaccuracy (IRI), and those that do treat IRI as random. This study explores IRI observed both within and across product categories and retail stores. Examining nearly 370,000 inventory records from 37 stores of one retailer, we find 65% to be inaccurate. We characterize the distribution of IRI and show, using hierarchical linear modeling (HLM), that 26.4% of the total variance in IRI lies between product categories and that 2.7% lies between stores. We identify several factors that mitigate record inaccuracy, such as inventory auditing practices, and several factors that exacerbate record inaccuracy, such as the complexity of the store environment and the distribution structure. Collectively, these covariates explain 67.6% and 69.0% of the variance in IRI across stores and product categories, respectively. Our findings underscore the need to design processes to reduce the occurrence of IRI and highlight factors that can be incorporated into inventory planning tools developed to account for its presence.execution, information technology, inventory control, record inaccuracy, retail, supply chains

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Inventory record inaccuracy is a significant problem for retailers using automated inventory management systems. In this paper, we consider an intelligent inventory management tool that accounts for record inaccuracy using a Bayesian belief of the physical inventory level. We assume that excess demands are lost and unobserved, in which case sales data reveal information about physical inventory levels. We show that a probability distribution on physical inventory levels is a sufficient summary of past sales and replenishment observations, and that this probability distribution can be efficiently updated in a Bayesian fashion as observations are accumulated. We also demonstrate the use of this distribution as the basis for practical replenishment and inventory audit policies and illustrate how the needed parameters can be estimated using data from a large national retailer. Our replenishment policies avoid the problem of "freezing," in which a physical inventory position persists at zero while the corresponding record is positive. In addition, simulation studies show that our replenishment policies recoup much of the cost of inventory record inaccuracy, and that our audit policy significantly outperforms the popular "zero balance walk" audit policy.retail execution, inventory control, record inaccuracy, inventory shrinkage, Bayes rule

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Store managers perform multiple tasks within a store, and the way in which they are evaluated and rewarded for these tasks affects their behavior. Using empirical data from multiple stores of a consumer electronics retailer, Tweeter Home Entertainment Group, we highlight the extent to which store manager incentive design impacts store manager behavior and, consequently, retail performance. More specifically, we describe the shift in store manager behavior resulting from a change in incentives, which, in part, altered the importance of sales relative to inventory shrinkage in the store manager compensation plan. Store managers, following this change, directed less attention to the prevention of inventory shrinkage and more toward sales-generating activities and made different process choices within the store. We observed increases in the level of inventory shrinkage and sales within these stores. Controlling for alternative drivers of sales and inventory shrinkage, we find this change in incentive design to be associated with a profit improvement of 4.2% of sales. This work indicates that altering how store managers are compensated impacts retail performance. Moreover, our findings underscore the importance of balancing the rewards given for different types of activities in contexts where agents face multiple competing tasks.incentives, multitasking agent, retail operations, inventory shrinkage, quasi-experimental, store management