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

To set inventory service levels, suppliers must understand how changes in inventory service level affect demand. We build on prior research, which uses analytical models and laboratory experiments to study the impact of a supplier's service level on demand from retailers, by testing this relationship in the field. We analyze a field experiment at the supplier Hugo Boss to determine how the supplier's inventory service level affects demand from its retailer customers. We find increases in historical fill rate to be associated with statistically significant and managerially substantial increases in current retailer orders (i.e., demand, not just sales). Specifically, a one percentage point increase in fill rate, measured over the prior year, is associated with a statistically significant 11 % increase in current retailer demand, controlling for other factors that might affect retailer demand. We explore the drivers of this demand increase, including changes in retailer assortment and order frequency. We discuss features of a retail buyer's decision context identified through our field work that may explain the magnitude of the relationship we observe.

Queue abandonment has a significant impact on system performance. However, the key drivers for abandonment, particularly in observable systems, are not well understood. To better inform our understanding of abandonment behavior, we study the effect of three operational drivers of abandonment from a hospital emergency department (ED), namely, waiting time, queue length, and observed service rate. We confirm that all three factors affect a patient's propensity for leaving the waiting area without being seen by a physician (LWBS), that is, abandoning the queue. Further, these factors interact with each other in a nonlinear fashion. Both ED crowding and observed service rate influence a patient's perception of waiting time. Moreover, patients are not homogenous in their abandonment response, and we observe behavior that is distinct for patients with severe conditions. Specifically, patients who report to a congested ED with more severe conditions are more inclined to abandon the ED early in the process compared to patients with less severe conditions. Further, we observe that patients with severe conditions who elect to remain in the crowded ED exhibit less sensitivity to waiting time and observed service rate than other patient types. We discuss the implications of this observed abandonment behavior on ED management. K E Y W O R D S abandonment, emergency department crowding, empirical study, health-care operations, left without being seen

a b s t r a c tPurchase orders specify many aspects of a fulfillment process, including item quantity, delivery time, carton labeling, bar coding, electronic data interchange, retail ticketing, and others. These fulfillment terms are instrumental for highly optimized retail supply chains employing automation and techniques such as pack-by-store. When fulfilling a purchase order, a supplier may commit a fulfillment error, i.e., the supplier may fail to adhere to the terms specified by the retailer. The retailer may then penalize the supplier for the fulfillment error via a chargeback deduction, which reduces the supplier's revenue. We present a study of the fulfillment errors and chargebacks that occur in practice using data collected from a major retailer's distribution center. While fulfillment errors involving incorrect product quantities and delivery times have received the most attention in the literature, we find that the majority of fulfillment errors in the context we study involve documentation, bar coding, and retail ticketing. We refer to these as correctable fulfillment errors, since they are amended at the retailer's distribution center through rework. We develop a model of inventory management with correctable fulfillment errors and use the retailer's data to assess the cost of these correctable fulfillment errors to the retailer's inventory system. Our research provides guidance to managers in identifying products and suppliers that impose large fulfillment error costs as well as in setting appropriate chargebacks for fulfillment errors.