We consider a single-period inventory model in which a risk-averse retailer faces uncertain customer demand and makes a purchasing-order-quantity and a selling-price decision with the objective of maximizing expected utility. This problem is similar to the classic newsvendor problem, except: (a) the distribution of demand is a function of the selling price, which is determined by the retailer; and (b) the objective of the retailer is to maximize his/her expected utility. We consider two different ways in which price affects the distribution of demand. In the first model, we assume that a change in price affects the scale of the distribution. In the second model, a change in price only affects the location of the distribution. We present methodology by which this problem with two decision variables can be simplified by reducing it to a problem in a single variable. We show that in comparison to a risk-neutral retailer, a risk-averse retailer in the first model will charge a higher price and order less; where as, in the second model a risk-averse retailer will charge a lower price. The implications of these findings for supply-chain strategy and channel design are discussed. Our research provides a better understanding of retailers' pricing behavior that could lead to improved price contracts and channel-management policies.pricing, demand uncertainty, risk aversion, inventory

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

This paper demonstrates that an important role of intermediaries in supply chains is to reduce the ®nancial risk faced by retailers. It is well known that risk averse retailers when faced by the classical single-period inventory (newsvendor) problem will order less than the expected value maximizing (newsboy) quantity. We show that in such situations a risk neutral distributor can oer a menu of mutually bene®cial contracts to the retailers. We show that a menu can be designed to simultaneously: (i) induce every risk averse retailer to select a unique contract from it; (ii) maximize the distributor's expected pro®t; and (iii) raise the order quantity of the retailers to the expected value maximizing quantity. Thus ineciency created due to risk aversion on part of the retailers can be avoided. We also investigate the in¯uence of product/market characteristics on the oered menu of contracts.

We study an inventory system under periodic review in the presence of two suppliers (or delivery modes). The emergency supplier has a shorter lead-time than the regular supplier, but the unit price he offers is higher. Excess demand is backlogged. We show that the classical "Lost Sales inventory problem" is a special case of this problem. Then, we generalize the recently studied class of Dual Index policies (Veeraraghavan and Scheller-Wolf (2007)) by proposing two classes of policies. The first class consists of policies that have an orderup-to structure for the emergency supplier. We provide analytical results that are useful for determining optimal or near-optimal policies within this class. This analysis and the policies that we propose leverage the connections we make between our problem and the lost sales problem. The second class consists of policies that have an order-up-to structure for the combined orders of the two suppliers. Here, we derive bounds on the optimal order quantity from the emergency supplier, in any period, and use these bounds for finding effective policies within this class. Finally, we undertake an elaborate computational investigation to compare the performance of the policies we propose with that of Dual Index policies. One of our policies provides an average cost-saving of 1.1 % over the Best Dual Index policy and has the same computational requirements. Another policy that we propose has a cost performance similar to the Best Dual Index policy but its computational requirements are lower.

Given a finite set of products with varying prices and costs, stochastic demand and customer preferences, we consider the problem of determining the optimal assortment and inventory levels in order to maximize expected profit in a single-period. We model customer preferences through the definition of customer types, where a type is a ranking of the potential products by order of preference. A customer purchases the highest ranked product available (if any) in the assortment at the time of his visit to the store (dynamic substitution). The total demand comprises of a fixed proportion of customers of each type. We show that an efficient dynamic programming algorithm of pseudo-polynomial complexity O(8 n) can be used to determine the optimal assortment. Our algorithm also gives a heuristic for the general case, i.e., when the proportion of customers of each type is random. In numerical tests, this heuristic performs better and faster than previously known heuristics, especially when the average demand is high and customers have large sets of preferred products.