Store Manager Incentive Design and Retail Performance: An Exploratory Investigation

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

Why Retailers Fail to Adopt Advanced Data Analytics

Advanced analytics have been available to businesses for years and are getting better all the time, but with a few big exceptions most retailers still use very basic tools. They do this even though they understand the advantages that analytics have given their competitors. What is holding them back from more fully embracing analytics? To find out, the authors interviewed 24 global retail executives in the Americas, Europe, and Asia and found that six factors are the primary sticking points. In this article they discuss those six factors and offer retailers some suggestions for how to move forward and profit from what advanced analytics have to offer

Why Retailers Fail to Adopt Advanced Data Analytics

Advanced analytics have been available to businesses for years and are getting better all the time, but with a few big exceptions most retailers still use very basic tools. They do this even though they understand the advantages that analytics have given their competitors. What is holding them back from more fully embracing analytics? To find out, the authors interviewed 24 global retail executives in the Americas, Europe, and Asia and found that six factors are the primary sticking points. In this article they discuss those six factors and offer retailers some suggestions for how to move forward and profit from what advanced analytics have to offer

The past, present, and future of retail analytics: Insights from a survey of academic research and interviews with practitioners

We document the evolution of academic research through a bibliometric analysis of 123 retail analytics articles published in top operations management journals from 2000 to 2020. We isolate nine decision areas via manual coding that we verify using automated text analysis (topic modeling). We track variation across decision areas and method-usage evolution per analytics type, featuring the degree to which big data (e.g., clickstream, social media, product reviews) and analytics suited for these new data sources (e.g., machine learning) are used. Our analysis reveals a rapidly growing field that is evolving in terms of content (decisions, retail sector), data, and methodology. To determine the state of practice, we interviewed global practitioners on the current use of retail analytics. These interviews shed light on the barriers and enablers of adopting advanced analytics in retail. They also highlight what sets companies on the frontier (e.g., Amazon, Alibaba, Walmart) apart from the rest. Combining the insights from our survey of academic research and interviews with practitioners, we provide directions for future academic research that take advantage of the availability of big data

The Impact of Supplier Reliability on Retailer Demand

To set inventory service levels, firms must understand how changes in service level affect customer demand. While the effects of service level changes have been studied empirically at the level of the end consumer, relatively little is known about the interaction between a retailer and a supplier. Using data from a manufacturer of branded apparel, we show increases in service level to be associated with statistically significant increases in retailer orders (i.e., demand, not just sales). Controlling for other factors that might affect demand, we find a 1 percent increase in historical service level to be associated with a 12 percent increase in demand from retailers, where historical service level is the type 1 service level performance of the apparel manufacturer over the prior year. Further, we find that retailers that order frequently exhibit a more substantial reaction to changes in service level, an outcome that is consistent with retailers learning about and reacting to changes in supplier service level. Our study not only provides the first empirical evidence of the impact of changes in service level on demand from retailers but also illustrates a method for estimating this relationship in practice.

Evaluating Count Prioritization Procedures for Improving Inventory Accuracy in Retail Stores

We compare several approaches for generating a prioritized list of products to be counted in a retail store, with the objective of detecting inventory record inaccuracy and unknown out-of-stocks. Our study evaluates these approaches using data from inventory audits we conducted at European home and personal care retailer dm-drogerie markt. We consider both "rule-based" approaches, which sort products based on heuristic indices, and "model-based" approaches, which maintain probability distributions for the true inventory levels updated based on sales and replenishment observations. Our results support arguments for both rule-based and model-based approaches. We find that model-based approaches provide versatile visibility into inventory states and are useful for a broad range of objectives, but that rule-based approaches are also effective as long as they are matched to the retailer's goal. We find that "high-activity" rule-based policies that favor items with high sales volumes, inventory levels, and past errors are more effective at detecting inventory discrepancies. A "low-activity" rule-based policy based on low recorded inventory levels, on the other hand, is more effective at detecting unknown out-of-stocks. Our approach can be replicated at other retailers interested in customized optimization of their counting programs

Retail Inventory Management When Records Are Inaccurate

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