T his paper considers a manufacturer selling to a newsvendor retailer that possesses superior demand-forecast information. We show that the manufacturer's expected profit is convex in the retailer's forecasting accuracy: The manufacturer benefits from selling to a better-forecasting retailer if and only if the retailer is already a good forecaster. If the retailer has poor forecasting capabilities, then the manufacturer is hurt as the retailer's forecasting capability improves. More generally, the manufacturer tends to be hurt (benefit) by improved retailer forecasting capabilities if the product economics are lucrative (poor). Finally, the optimal procurement contract is a quantity discount contract.

I n countries that bear the heaviest burden of malaria, most patients seek medicine for the disease in the private sector. Because the availability and affordability of recommended malaria drugs provided by the private-sector distribution channel is poor, donors (e.g., the Global Fund) are devoting substantial resources to fund subsidies that encourage the channel to improve access to these drugs. A key question for a donor is whether it should subsidize the purchases and/or the sales of the private-sector distribution channel. We show that the donor should only subsidize purchases and should not subsidize sales. We characterize the robustness of this result to four key assumptions: the product's shelf life is long, the retailer has flexibility in setting the price, the retailer is the only level in the distribution channel, and retailers are homogeneous.

This paper studies two important variants of the dynamic economic lot-sizing problem that are applicable to a wide range of real-world situations. In the first model, production in each time period is restricted to a multiple of a constant batch size, where backlogging is allowed and all cost parameters are time varying. Several properties of the optimal solution are discussed. Based on these properties, an efficient dynamic programming algorithm is developed. The efficiency of the dynamic program is further improved through the use of Monge matrices. Using the results developed for the first model, an O(n3log n) algorithm is developed to solve the second model, which has a general form of product acquisition cost structure, including a fixed charge for each acquisition, a variable unit production cost, and a freight cost with a truckload discount. This algorithm can also be used to solve a more general problem with concave cost functions.

We consider a large original equipment manufacturer (OEM) who relies on a contract manufacturer (CM) to produce her product. In addition to the OEM's product, the CM also produces for a smaller OEM. Both the larger OEM and the CM can purchase the component from the supplier, but their purchase prices may differ and remain unknown to each other. The main question we address is whether the larger OEM should retain component procurement by purchasing components from the supplier and reselling to the CM (buy–sell), or outsource component procurement by letting the CM purchase directly from the supplier (turnkey). We show that, under buy–sell, the larger OEM's optimal strategy is to resell components at the highest possible component purchase price of the CM (i.e., the street price). By comparing buy–sell and turnkey, we find that a CM with low component price is better off under turnkey, even though under buy–sell he receives more profits through the products sold to the smaller OEM. Furthermore, the larger OEM's preference between buy–sell and turnkey depends on her component price, the volatility of the CM's component price and substitutability between the two products.

T his paper studies a manufacturer that sells to a newsvendor retailer who can improve the quality of her demand information by exerting costly forecasting effort. In such a setting, contracts play two roles: providing incentives to influence the retailer's forecasting decision and eliciting information obtained by forecasting to inform production decisions. We focus on two forms of contracts that are widely used in such settings and are mirror images of one another: a rebates contract, which compensates the retailer for the units she sells to end consumers, and a returns contract, which compensates the retailer for the units that are unsold. We characterize the optimal rebates contracts and returns contracts. Under rebates, the retailer, manufacturer, and total system may benefit from the retailer having inferior forecasting technology; this never occurs under returns. Although one might conjecture that returns would be inferior because its provision of "insurance" would discourage the retailer from forecasting, we show that returns are superior.

We consider the problem of dynamically cross-selling products (e.g., books) or services (e.g., travel reservations) in the e-commerce setting. In particular, we look at a company that faces a stream of stochastic customer arrivals and may offer each customer a choice between the requested product and a package containing the requested product as well as another product, what we call a "packaging complement. " Given consumer preferences and product inventories, we analyze two issues: (1) how to select packaging complements, and (2) how to price product packages to maximize profits.We formulate the cross-selling problem as a stochastic dynamic program blended with combinatorial optimization. We demonstrate the state-dependent and dynamic nature of the optimal package selection problem and derive the structural properties of the dynamic pricing problem. In particular, we focus on two practical business settings: with (the Emergency Replenishment Model) and without (the Lost-Sales Model) the possibility of inventory replenishment in the case of a product stockout. For the Emergency Replenishment Model, we establish that the problem is separable in the initial inventory of all products, and hence the dimensionality of the dynamic program can be significantly reduced. For both models, we suggest several packaging/pricing heuristics and test their effectiveness numerically. AbstractWe consider the problem of dynamically cross-selling products (e.g., books) or services (e.g., travel reservations) in the e-commerce setting. In particular, we look at a company that faces a stream of stochastic customer arrivals and may offer each customer a choice between the requested product and a package containing the requested product as well as another product, "packaging complement". Given consumer preferences and product inventories, two issues are analyzed: (1) how to select packaging complements and (2) how to price product packages to maximize profits.We formulate the cross-selling problem as a stochastic dynamic program blended with combinatorial optimization. We demonstrate the state-dependent and dynamic nature of the optimal package selection problem and derive structural properties of the dynamic pricing problem. In particular, we focus on two practical business settings: with (the Emergency Replenishment model) and without (the Lost Sales model) the possibility of inventory replenishment in the case of a product stock-out. For the Emergency Replenishment model, we establish that the problem is separable in the initial inventory of all products and hence the dimensionality of the dynamic program can be significantly reduced. For both models several packaging/pricing heuristics are suggested and their effectiveness is tested numerically.

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This paper investigates the impact of royalty revision on incentives and profits in a twostage (R&D stage and marketing stage) alliance with a marketer and an innovator. The marketer offers royalty contracts to the innovator. We find the potential for royalty revision leads to more severe distortions in the optimal initial royalty contracts offered by the marketer. We show if the innovator plays a significant role in the marketing stage, the marketer should offer a low royalty rate initially and then revise the royalty rate up later. Otherwise, she should do the opposite. We identify two major effects of royalty revision. First, royalty revision provides the marketer with a flexibility to dynamically adjust royalty rates across the two stages of the alliance to better align the innovator's incentives. This incentive realigning effect improves the marketer's profit. Second, royalty revision makes it harder for the marketer to obtain private information from the innovator because the innovator worries the marketer will take advantage of the information to revise the initial contract to a more favorable one to herself later. This information revealing effect hurts the marketer's profit. We characterize in what kind of alliances marketers would benefit the most from royalty revision so that managers should clearly establish the expectation for royalty revision, and in what kind of alliances markerters would not benefit from royalty revision so that managers should commit not to revise the initial royalty contract. With royalty contracts that are contingent on the R&D outcome of the R&D stage, we find that contingent contract structure could be either substitutable (by fully capturing the incentive realigning effect) or complementary (by weakening the information revealing effect) to royalty revision depending on whether the innovator plays a significant role of the marketing stage. Managers may need to use contingent contract (if possible) either to replace or with royalty revision accordingly to improve profits.