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Product expiration is an important problem in the consumer packaged goods (CPG) industry costing l%-2% of gross retail sales and eroding industry profits substantially. It can be caused by several factors related to store operations, supply chain practices, and product characteristics. Existing methods used in the industry are inadequate to identify the causes of expiration, leading to inadequate efforts to reduce expiration. Using retail data for 768 SKUs and 10,000 stores (745,638 store-SKU-level observations), as well as upstream supply chain data from a CPG manufacturer, we show the extent to which expiration of products in retail stores is driven by case size, inventory aging in the supply chain, minimum order rules, manufacturers' incentive programs for the sales force, replenishment workload, and many control variables. A counterfactual analysis based on the model shows that our subject manufacturer can reduce expiration by up to $38.82 million per year by implementing four selected initiatives involving case size, supply chain aging, minimum order rules, and sales incentives. Further, targeted initiatives can be designed using combinations of these variables for subsets of products with the highest occurrence of expiration. History: Accepted by Serguei Netessine, operations management. Funding: The authors thank the industry collaborator AlphaCo for providing $60,000 of financial support to this study. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2018.3051. Keywords: retail operations * consumer packaged goods * product expiration. food waste * empirical * perishable inventory * supply chain management * marketing/operations interface * sustainable operations management * zero-inflated models * direct store delivery, 1. Introduction Consumer packaged goods (CPG) products, such as soft drinks, shelf-stable dry food, and health and beauty aids, that turn into waste at retail stores were estimated to cost [...]

Retail store managers may not follow order advices generated by an automated inventory replenishment system if their incentives differ from the cost-minimization objective of the system or if they perceive the system to be suboptimal. We study the ordering behavior of retail store managers in a supermarket chain to characterize such deviations in ordering behavior, investigate their potential drivers, and thereby devise a method to improve automated replenishment systems. Using orders, shipments, and point-of-sale data for 19,417 item-store combinations over five stores, we show that (i) store managers consistently modify automated order advices by advancing orders from peak to nonpeak days, and (ii) this behavior is explained significantly by product characteristics such as case pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our regression results suggest that store managers improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. Based on these results, we construct a method to modify automated order advices by learning from the behavior of store managers. Motivated by the management coefficients theory, our method is efficient to implement and outperforms store managers by achieving a more balanced handling workload with similar average days of inventory. Key words: retail operations; inventory management; behavioral operations History: Received November 9, 2007; accepted December 12, 2009, by Ananth lyer, operations and supply chain management. Published online in Articles in Advance March 23, 2010., 1. Introduction An automated inventory replenishment system, implemented in a retail store, recommends order quantities to the store manager every order cycle, typically once a day. Store managers can be [...]

Retail inventory is a statistic that is closely watched by retailers as well as their investors, lenders, and suppliers. Retailers not only benefit from inventory, but also bear the cost of excess inventory. Investors, lenders, and suppliers interpret this statistic for signs of the retailer's health, future sales prospects, and impending costs. This article synthesizes the perspectives of investors, lenders, and suppliers on inventory. Moreover, the article shows that inventory turns, a commonly used metric to identify excess inventory, has important limitations that reduce its utility for all these stakeholders. It then presents a new metric, adjusted inventory turns, which can be effectively utilized by all stakeholders to assess whether a retailer is carrying too much or too little inventory. [ABSTRACT FROM AUTHOR]

Problem definition: Banks commonly use asset-based lending (ABL) to provide loans collateralized by a borrower firm’s inventory. We study the implications of ABL by examining how banks should determine asset-based loan terms based on firms’ operational characteristics (e.g., inventory salvage value and demand uncertainty) and how firms should make inventory stocking and capital structure decisions under asset-based borrowing constraints. Academic/practical relevance: Despite its widespread use for lending to small firms and those with large inventory investments, such as retailers, ABL has not been well studied in the literature. Methodology: We analyze ABL using a stylized single-period screening game of incomplete information between a business owner and a bank. The bank offers a menu of loans, where each loan offer is characterized by an interest rate and inventory advance rate. The owner then decides the inventory level and the mix of debt and equity with which to finance the firm’s operations. Our model captures the practical features of ABL, for example, the bank does not have the full knowledge of the firm’s demand prospects, and the inventory advance rate leads to a credit limit that increases in the firm’s inventory investment. Results: We show that ABL enables the bank to mitigate information asymmetry by screening firms and thereby controlling each firm type’s order quantity and leverage. We also obtain hypotheses describing the equilibrium loan contracts, capital structure, inventory investment, and bankruptcy outcomes. For instance, leverage leads to overinvestment resulting from the equity decision being endogenous to the model. Moreover, inventory advance rates are more sensitive to firms’ operational characteristics than interest rates. Managerial implications: Our study sheds light on the operational and financial implications of ABL by demonstrating how managers should make inventory and capital structure decisions while interacting with asset-based lenders. The online appendix is available at https://doi.org/10.1287/msom.2017.0670. [ABSTRACT FROM AUTHOR]

We demonstrate the value of utility-based choice models to estimate demand and plan inventory for new and used textbooks in the presence of consumer choice and stockout-based substitution at a university textbook retailer. Demand information is censored, the exact time of stockout is not observed, and the short selling season often does not allow for replenishment. Using data for 26,749 book titles from 2007 to 2011 and a simulation experiment calibrated on real data, we show that an attribute-based choice model generates accurate demand estimates (mean absolute percentage error less than 1%) even when nearly 90% of the textbooks in the fit sample experience stockout. This performance is driven by the heterogeneity of product attributes and is robust to the occurrence of product returns. We implement this model at the bookstore in a controlled field experiment and obtain over 10% increase in profit. The results show that accounting for asymmetric and stockout-based substitution in demand estimation and inventory planning enables us to make systematic corrections in inventory mix and inventory level compared to the existing process. [ABSTRACT FROM AUTHOR]

We find that inventory productivity strongly predicts future stock returns among a sample of publicly listed U.S. retailers during the period from 1985 to 2010. A zero-cost portfolio investment strategy, which consists of buying from the two highest and selling from the two lowest quintiles formed on inventory turnover, earns more than 1% average monthly abnormal return benchmarked to the Fama–French–Carhart four-factor model. Our results are robust to different measures of inventory productivity, distinct from the well-known firm characteristics known to generate abnormal returns, and not driven by a particular subsample period. A longitudinal analysis of portfolio returns over longer holding periods shows that although inventory productivity is predictive of stock returns, its information dissipates about one to two years after release. [ABSTRACT FROM AUTHOR]

Firm-level sales forecasts for retailers can be improved if we incorporate cost of goods sold, inventory, and gross margin (defined by us as the ratio of sales to cost of goods sold) as three endogenous variables. We construct a simultaneous equations model, estimated using public financial and nonfinancial data, to provide joint forecasts of annual cost of goods sold, inventory, and gross margin for retailers using historical data. We show that sales forecasts from this model are more accurate than consensus forecasts from equity analysts. Further, the residuals from this model for one fiscal year are used to predict retailers for whom the relative advantage of model forecasts over consensus forecasts would be large in the next fiscal year. Our results show that historical inventory and gross margin contain information useful to forecast sales, and that equity analysts do not fully utilize this information in their sales forecasts. [ABSTRACT FROM AUTHOR]

We develop a model of consumer learning and choice behavior in response to uncertain service in the marketplace. Learning could be asymmetric, that is, consumers may associate different weights with positive and negative experiences. Under this consumer model, we characterize the steady-state distribution of demand for retailers given that each retailer holds a constant in-stock service level. We then consider a noncooperative game in steady state between two retailers competing on the basis of their service levels. The demand distributions of retailers in this game are modeled using a multiplicative aggregate market-share model in which the mean demands are obtained from the steady-state results for individual purchases, but the model is simplified in other respects for tractability. Our model yields a unique pure strategy Nash equilibrium. We show that asymmetry in consumer learning has a significant impact on the optimal service levels, market shares, and profits of the retailers. When retailers have different costs, it also determines the extent of competitive advantage enjoyed by the lower-cost retailer. [ABSTRACT FROM AUTHOR]

In this paper we study how the time-series structure of the demand process affects the value of information sharing in a supply chain. We consider a two-stage supply chain model in which a retailer serves auto-regressive moving-average (ARMA) demand and a manufacturer fills the retailer's orders. We characterize three types of situations based on the parameters of the demand process: (i) the manufacturer benefits from inferring demand information from the retailer's orders; (ii) the manufacturer cannot infer demand, but benefits from sharing demand information; and (iii) the manufacturer is better off neither inferring nor sharing, but instead uses only the most recent orders in its production planning. Using the example of ARMA (1,1) demand, we find that sharing or inferring retail demand leads to a 16.0% average reduction in the manufacturer's safety-stock requirement in cases (i) and (ii), but leads to an increase in the manufacturer's safety-stock requirement in (iii). Our results apply not only to two-stage but also to multistage supply chains. [ABSTRACT FROM AUTHOR]

Inventory turnover varies widely across retailers and over time. This variation undermines the usefulness of inventory turnover in performance analysis, benchmarking, and working capital management. We develop an empirical model using financial data for 311 publicly listed retail firms for the years 1987-2000 to investigate the correlation of inventory turnover with gross margin, capital intensity, and sales surprise (the ratio of actual sales to expected sales for the year). The model explains 66.7% of the within-firm variation and 97.2% of the total variation (across and within firms) in inventory turnover. It yields an alternative metric of inventory productivity, adjusted inventory turnover, which empirically adjusts inventory turnover for changes in gross margin, capital intensity, and sales surprise, and can be applied in performance analysis and managerial decision making. We also compute time trends in inventory turnover and adjusted inventory turnover, and find that both have declined in retailing during the 1987-2000 period. [ABSTRACT FROM AUTHOR]

No abstract available. [ABSTRACT FROM AUTHOR]