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.benchmarking, inventory turnover, retail operations, performance measures

We address the problem of hedging inventory risk for a short life cycle or seasonal item when its demand is correlated with the price of a financial asset. We show how to construct optimal hedging transactions that minimize the variance of profit and increase the expected utility for a risk-averse decision maker. We show that for a wide range of hedging strategies and utility functions, a risk-averse decision maker orders more inventory when he or she hedges the inventory risk. Our results are useful to both risk-neutral and risk-averse decision makers because (1) the price information of the financial asset is used to determine both the optimal inventory level and the hedge, (2) this enables the decision maker to update the demand forecast and the financial hedge as more information becomes available, and (3) hedging leads to lower risk and higher return on inventory investment. We illustrate these benefits using data from a retailing firm.demand forecasting, financial hedging, newsboy model, real options, risk aversion

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.sales forecasting, retail, inventory, empirical

We consider a single-period assortment planning and inventory management problem for a retailer, using a locational choice model to represent consumer demand. We first determine the optimal variety, product location, and inventory decisions under static substitution, and show that the optimal assortment consists of products equally spaced out such that there is no substitution among them regardless of the distribution of consumer preferences. The optimal solution can be such that some customers prefer not to buy any product in the assortment, and such that the most popular product is not offered.We then obtain bounds on profit when customers dynamically substitute, using the static substitution for the lower bound, and a retailer-controlled substitution for the upper bound. We thus define two heuristics to solve the problem under dynamic substitution, and numerically evaluate their performance. This analysis shows the value of modeling dynamic substitution and identifies conditions in which the static substitution solution serves as a good approximation.

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 and investigate their potential drivers. Using orders, shipments, and POS data for 19, 417 item-store combinations over 5 stores, we find that store managers systematically modify automated order advices by advancing orders from peak to non-peak days. We show that order advancement is explained significantly by hypothesized product characteristics, such as case-pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our results suggest that store managers add value. They improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. We test a heuristic procedure, based on our regression results, to modify order advices to mimic the behavior of store managers. Our method performs better than the store managers by achieving a more balanced handling workload with similar average days of inventory.