Devavrat Purohit

Strategic Decentralization and Channel Coordination

In this paper, we show that under certain conditions, strategic decentralization through the addition of a retailer in the distribution channel can increase a manufacturers profits. The specific case on which we focus is the quantity coordination (double marginalization) problem for a manufacturer selling durable goods in a two-period setting. We show that the standard solution that coordinates a channel for non-durables does not coordinate the channel for durables. In particular, even though a manufacturer can achieve channel coordination by offering per-period, two-part fees, the equilibrium wholesale price in the first period is strictly above the manufacturers marginal cost. This is in stark contrast to the two-part solution for non-durables where the equilibrium wholesale price is equal to marginal cost. We also identify a strategy that solves both the channel coordination and the Coase problem associated with durable goods. In this strategy, at the beginning of period 1, the manufacturer writes a contract with the retailer specifying a fixed fee and wholesale prices covering both periods. We show that by adding a retailer and using this contract, the manufacturer makes higher profits than it could if it were to sell directly to consumers.

Leveraging the Global Information Revolution for Economic Development: Singapore's Evolving Information Industry Strategy

Despite her small size and late industrialization, Singapore has managed to capture a significant share of the global information industry over the last three decades. This paper analyzed the structure and growth dynamics of Singapores information industries using an analytical framework that integrates the four key components of an information economy: ICT goods industry, content industry, network infrastructure, and informatization. The paper identifies four generic stages in the development path of Singapores information economy and highlights policy implications for other small, open economies.

Research Note---The Role of Production Lead Time and Demand Uncertainty in Marketing Durable Goods

Firms often have to make their production decisions under conditions of demand uncertainty. This is especially true for product categories such as automobiles and technology goods where the lead time needed for manufacturing forces firms to make production decisions well in advance of the selling season. Once the firm has produced the goods, the available production volume affects the firms subsequent marketing decisions. In this paper, we study the relationship between the firms production and marketing decisions for a durable goods manufacturer. We develop a dynamic model of a durable product market in which the demand functions are developed from a micromodeling of consumer utility functions and an equilibrium analysis of consumer strategies. After taking into account the demand uncertainty as well as the potential for cannibalization of future sales, the manufacturer makes its production and sales decisions. We find that the firms optimal inventory level is U-shaped in the durability of the product and that the firm suffers a larger loss due to uncertainty when it is leases rather than sells its products. Furthermore, unlike the case for nondurables, for durable goods we find that the effect of uncertainty persists even after the uncertainty has been resolved.

Forward Buying by Retailers

Conventional wisdom in marketing holds that (1) retailer forward buying is a consequence of manufacturer trade promotions and (2) stockpiling units helps the retailer but hurts the manufacturer. This article provides a deeper understanding of forward buying by analyzing it within the context of manufacturer trade promotions, competition, and demand uncertainty. The authors find that regardless of whether the manufacturer offers a trade promotion, allowing the retailer to forward buy and hold inventory for the future can, under certain conditions, be beneficial for both parties. Disallowing forward buying by the retailer may lead the manufacturer to lower merchandising requirements and change the depth of the promotion. In competitive environments, there are situations in which retailers engage in forward buying because of competitive pressures in a prisoners-dilemma situation. Finally, when the authors consider the case of uncertain demand, they find further evidence of strategic forward buying. In particular, the authors find cases in which the retailer orders a quantity that is higher than what it expects to sell in even the most optimistic demand scenario.

The Strategic Role of Exchange Promotions

An exchange promotion allows consumers to turn in an old good and receive a discount toward the purchase of a new product. The old good that is turned in can either be within the same category as the new good or it may be in a different category. For example, one can turn in an old CD player to count toward a new CD player (a within-category exchange or traditional trade-in) or toward a new television (a cross-category exchange). This paper studies both within-category and multicategory exchange promotions and analyzes their similarities and differences. In a competitive setting with two firms, we model exchange promotions and establish the equilibrium outcomes. We find that categories in which consumers have a high level of waste aversion are more likely to have multicategory exchange promotions rather than within-category or no promotions. Multicategory exchange promotions can increase both consumers’ replacement purchases and their new purchases. Interestingly, we also find that strategic considerations can lead to a prisoner’s dilemma outcome in which neither firm offers any kind of exchange promotion. However, waste aversion and multicategory exchange promotions can give firms stronger incentives to get out of the prisoner’s dilemma outcome.

Turn-and-Earn Incentives with a Product Line

When manufacturers do not have sufficient capacity to meet demand and cannot increase prices, they have to determine other methods to allocate goods among retailers. A common allocation mechanism is based on a retailers sales history: a retailer that has ordered larger quantities in the past should get a greater allocation than a retailer that has historically ordered smaller quantities. This mechanism, known as a turn-and-earn allocation rule, is commonly used in many industries such as automobiles, microprocessors, video game consoles, etc. The existing literature has considered the effect of turn-and-earn allocation rules when a manufacturer sells a single product. However, when we consider a product line, it is not clear whether the manufacturer is better off basing its allocation on the sales history of the entire product line or solely on the sales history of the product in short supply. In particular, a shortage of one product can lead retailers and consumers to move toward other products in the line. This, in turn, can have an effect on the manufacturers optimal allocation mechanism. We examine this issue by developing a model of a supplier selling two substitutable goods through two retailers. Within this setup, we introduce a general turn-and-earn allocation rule that allows the entire sales history to influence allocation levels. Counter to previous work, we show that certain turn-and-earn rules not only help the manufacturer but can also help the retailer and increase total supply chain profits. This paper was accepted by Martin Lariviere, operations management.

Level-Crossing Properties of the Risk Process

For the classical risk process R(t) that is linear increasing with slope 1 between downward jumps of i.i.d. random sizes at the points of a homogeneous Poisson process we consider the level-crossing process C(x) = (L(x), (Ai(x), Bi(x))1≥i≥L(x)), where L(x) is the number of jumps from (x, infinity) to (-infinity, x] and Ai(x)(Bi(x)) are the distances from x to R(t) after (before) the ith jump of this kind. It is shown that if R(.) has a drift toward infinity, C(.) is a stationary Markov process; its transition probabilities are determined. As an application we derive the expected value E(L(x)L(x + y)).