C onsider a retailer who sells perishable seasonal products with uncertain demand. Due to the short sales season and long replenishment lead times associated with such products, the retailer is unable to update demand forecasts by using actual sales data generated from the early part of the season and to respond by replenishing stocks during the season. To overcome this limitation, we examine the case in which the retailer develops a program called the "advance booking discount" (ABD) program that entices customers to commit to their orders at a discount price prior to the selling season. The time between placement and fulfillment of these precommitted orders provides an opportunity for the retailer to update demand forecasts by utilizing information generated from the precommitted orders and to respond by placing a cost-effective order at the beginning of the selling season. In this paper, we evaluate the benefits of the ABD program and characterize the optimal discount price that maximizes the retailer's expected profit.

We study competition between two multiproduct firms with distinct production technologies in a market where customers have heterogeneous preferences on a single taste attribute. The mass customizer (MC) has a perfectly flexible production technology and thus can offer any variety within a product space, represented by Hotelling's linear city. The mass producer (MP) has a more focused production technology and therefore offers a finite set of products in the same space. The MP can invest in more flexible technology, which reduces its cost of variety and hence allows it to offer a larger set of products; in the extreme, the MP can emulate the MC's technology and offer infinite variety. The firms simultaneously decide whether to enter the market, and the MP chooses its degree of product-mix flexibility on entry. Next, the MP designs its product line--i.e., the number and position of its products--the MC's perfectly flexible technology makes this unnecessary. Finally, both firms simultaneously set prices. We analyze the subgame-perfect Nash equilibrium in this three-stage game, allowing firm-specific fixed and variable costs that together characterize their production technology. We find that an MP facing competition from an MC offers lower product variety than an MP monopolist to reduce the intensity of price competition. We also find that the MP can survive this competition, even if it has higher fixed cost of production technology, higher marginal cost of production, or both.product variety management, mass customization, operations-marketing interface, discrete consumer choice, competitive product strategy, pricing

No abstract

We investigate the use of a canonical version of a discrete choice model due to Daganzo (1979) [Daganzo C (1979) Multinomial Probit: The Theory and Its Application to Demand Forecasting (Academic Press, New York).] in optimal pricing and assortment planning. In contrast to multinomial and nested logit (the prevailing choice models used for optimizing prices and assortments), this model assumes a negatively skewed distribution of consumer utilities, an assumption we motivate by conceptual arguments as well as published work. The choice probabilities in this model can be derived in closed form as an exponomial (a linear function of exponential terms). The pricing and assortment planning insights we obtain from the exponomial choice (EC) model differ from the literature in two important ways. First, the EC model allows variable markups in optimal prices that increase with expected utilities. Second, when prices are exogenous, the optimal assortment may exhibit leapfrogging in prices, i.e., a product can be skipped in favor of a lower-priced one depending on the utility positions of neighboring products. These two plausible pricing and assortment patterns are ruled out by multinomial logit (and by nested logit within each nest). We provide structural results on optimal pricing for monopoly and oligopoly cases, and on the optimal assortments for both exogenous and endogenous prices. We also demonstrate how the EC model can be easily estimated—by establishing that the log-likelihood function is concave in model parameters and detailing an estimation example using real data.

The literature on mass customization generally focuses on the tradeoff between higher revenues from better matching customer preferences with product specifications, and higher costs of offering a broader--possibly fully customized--product line. Less well understood is the tradeoff between the increased ability to precisely meet customer preferences and the increased leadtime from order placement to delivery often associated with customized products. In this paper, we use a locational customer choice model to formulate a firm's integrated product line design problem that involves variety, leadtime (or inventory), and pricing decisions. We propose a dynamic programming based solution procedure that amounts to solving a shortest path problem on an acyclic network, and derive some structural results on the optimal product line design. We find that unimodal preferences generally result in hybrid product lines, with standard products clustering around the mode and custom products covering the tails, in contrast with the all-custom or all-standard product lines that are optimal under uniform preferences. We also numerically examine how the firm should adjust its leadtime and variety in response to changes in parameters such as customer dispersion and operational scale. We find that the tradeoff between leadtime and variety is sometimes nonintuitive and complex.product variety, mass customization, make-to-order, make-to-stock, congestion, leadtime, custom and standard products, product differentiation

I nventory pooling is at the root of many celebrated ideas in operations management. Postponement, component commonality, and resource flexibility are some examples. Motivated by our experience in the aftermarket services industry, we propose a model of inventory pooling to meet differentiated service levels for multiple customers. Our central research question is the following: What are the minimum inventory level and optimal allocation policy when a pool of inventory is used in a single period to satisfy individual service levels for multiple customers? We measure service by the probability of fulfilling a customer's entire demand immediately from stock. We characterize the optimal solution in several allocation policy classes; provide some structural results, formulas, and bounds; and also make detailed interpolicy comparisons. We show that the pooling benefit is always strictly positive, even when there are an arbitrary number of customers with perfectly positively correlated demands.

M otivated by the proliferation of multifunction products, we investigate product portfolio decisions of a single firm by analyzing the impact of three major factors. First, because multifunction products provide complete or partial functionalities of single-function products, we incorporate substitution or cannibalization effects between the potential products. Second, we explicitly model the variable costs of manufacturing the singlefunction and multifunction products. Third, we examine the firm's pricing decisions because of their impact on the degree of cannibalization between the multifunction product and one or more single-function products. Using an economic model, we first characterize the firm's optimal product portfolio (through a quantity-based decision), which in turn determines the market equilibrium prices for each product in its portfolio. Some of the unique insights stemming from our analysis are: (a) the optimal product portfolio choice is driven primarily by maximum profit margins for the single-function products weighted by the demand substitution effects; and (b) from a product design perspective, the complete functionality of the base single-function product is always included in the optimal product offering, but this is not necessarily the case with the complete functionality of the nonbase single-function product.

We study competition between two multi-product firms with distinct production technologies in a market where customers have heterogeneous preferences on a single taste attribute. The mass customizer (MC) has a perfectly flexible production technology, thus can offer any variety within a product space, represented by Hotelling's (1929) linear city.The mass producer (MP) has a more focused production technology, and therefore, it offers a finite set of products in the same space. MP can invest in more flexible technology, which reduces its cost of variety and hence allows it to offer a larger set of products; in the extreme, MP can emulate MC's technology and offer infinite variety. The firms simultaneously decide whether to enter the market, and MP chooses its degree of product-mix flexibility upon entry. Next, MP designs its product line, i.e., the number and position of its products; MC's perfectly flexible technology makes this unnecessary. Finally, both firms simultaneously set prices. We analyze the sub-game perfect Nash equilibrium in this three-stage game, allowing firm-specific fixed and variable costs that together characterize their production technology.We find that an MP facing competition from an MC offers lower product variety compared to an MP monopolist, in order to reduce the intensity of price competition. We also find that MP can survive this competition even if it has higher fixed cost of production technology or higher marginal cost of production or both.