Aydin Alptekinoglu

A model for analyzing multi-channel distribution systems

To capture customers in different market segments, many firms distribute their products using multiple channels involving in-store, mail-order and electronic mediums. Nevertheless, the business literature is replete with examples where the management of multiple distribution channels becomes unwieldy. As a way to evaluate different distribution strategies arising from multiple channels, we develop a model of a general multi-channel distribution system subject to stochastic demand. Since the exact analysis of our model is intractable, we propose a decomposition scheme that enables us to develop a near-optimal distribution policy with minimum total expected distribution cost. We utilize our analysis of this multi-channel system in two specific applications arising from multi-channel distribution. The first application deals with a store-based retailer expanding into a direct sales channel. The retailer can fill the orders generated from this additional sales channel by using the existing stores or the existing depots. For this application, we evaluate the trade-off between these two options. The second application intends to find a cost-effective way to distribute products by utilizing the combined distribution system of two retailers after a potential merger-and-acquisition (M&A). For this application, we quantify the benefit of an M&A in terms of distribution efficiency.

Leadtime-Variety Tradeoff in Product Differentiation

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 firms 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.

The Exponomial Choice Model: A New Alternative for Assortment and Price Optimization

We investigate the use of a canonical version of a discrete choice model due to Daganzo (1979) 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 loglikelihood function is concave in model parameters and detailing an estimation example using real data.

Flexible Products for Dynamic Preferences

Consumers often have needs that change in a dynamic fashion over time due to physiological, mental or environmental variations. We develop a model to address a product strategy question on how to satisfy dynamic consumer preferences: Should a firm offer multiple standard products, each designed for a specific purpose (e.g., several specialized golf clubs), or a flexible product that can be reconfigured by consumers as their preferences change (e.g., one adjustable golf club)? Often the latter approach is fueled by new technology, like the dial-a-dose system that Novo Nordisk invented and perfected over the years for its insulin pens. We find that products that deliver a high utility to consumers are ideal candidates for flexible designs, as higher utility encourages reconfiguration and justifies a higher flexibility premium. This offers an explanation as to why flexible products are typically sold at significant premiums over their ‘inflexible’ counterparts. We also discover a non-obvious relationship between optimal product strategy and dynamic consumer preferences. Intuition suggests that product flexibility would be more valuable when consumer preferences are more dynamic in the sense of changing more often. On the contrary, we find that a flexible product may lead to higher profits than a portfolio of standard products when consumer preferences are more stable. While higher stability in preferences reduces the frequency of reconfigurations, it also increases the value of each reconfiguration when preference shifts do occur. Finally, we also investigate the impact of heterogeneity in consumer preferences. We find that it can lead to a hybrid product strategy being optimal, where offering a mix of flexible and standard products enables efficient price discrimination between high- and low-end consumers.

Is Assortment Selection a Popularity Contest

Should retailers take product returns into account when choosing their assortments? And, when doing so, should they consider assortment selection as a popularity contest – by carrying products that they think will be popular among consumers? Or, is there ever a case for carrying eccentric products – those that are least likely to be purchased by a typical consumer? In search of answers to these questions, we explore in this chapter the interactions between product assortment, return policy, and pricing decisions of a retailer. We consider a category of horizontally differentiated products delivered in two alternative supply modes: make-to-order (MTO) and make-to-stock (MTS). In the MTO mode, products are supplied after demand materializes, whereas in the MTS mode, the retailer stocks products prior to the selling season. Underlying our demand model, consumer choice behavior follows a nested multinomial logit model, with the first stage involving a product choice, and the second stage involving a keep-or-return decision. We show that the structure of the optimal assortment strongly depends on both the return policy, which we parameterize by refund fraction (percentage of price refunded upon return) and the supply mode (MTO vs. MTS). For relatively strict return policies with a sufficiently low refund fraction, it is optimal for the retailer to offer most eccentric products in the MTO mode, and a mix of most popular and most eccentric products in the MTS mode. For relatively lenient return policies, on the other hand, conventional thinking applies: the retailer selects most popular products. We also numerically study three extensions of our base model to incorporate: (1) endogenous price, (2) endogenous refund fraction, and (3) multiple periods. We demonstrate that interesting aspects of our results regarding strict return policies prevail under all of these extensions.

Positioning and Pricing of Horizontally Differentiated Products

We provide structural results and a solution method for designing horizontally differentiated product lines – optimizing product positions and prices – under fairly general consumer choice behavior. Our choice model is a generalization of the basic Hotelling-Lancaster locational choice model: Consumer tastes (ideal products) follow a general distribution; substitution disutility (transportation cost) can be an asymmetric convex function of product-spatial distance; and the market may not be fully covered. We formalize the notion that a shift of consumer tastes toward one end of the product space cannot result in a shift of the optimal product line in the opposite direction. For a unimodal taste distribution, we show that with respect to the product that covers the mode (or one of two products adjacent to it) prices and market shares drop toward the tails. Hence, higher popularity is always associated with higher price – although pricing, positioning, relative market share and popularity of products are all endogenous in our model. Our solution method is exact for discrete consumer taste distributions. Whereas, for continuous distributions, it requires lower and upper bounds, which can be computed efficiently using shortest path formulations and they asymptotically converge to the optimal profit.

Heteroscedastic Exponomial Choice

We investigate analytical and empirical properties of the Heteroscedastic Exponomial Choice (HEC) model to lay the groundwork for its use in theoretical and empirical research that build demand models on a discrete choice foundation. The HEC model generalizes the Exponomial Choice (EC) model by including choice-specific variances for the random components of utility (the error terms). We show that the HEC model inherits some of the properties found in the EC model: closed-form choice probabilities, demand elasticities and consumer surplus; optimal monopoly prices that are increasing with ideal utilities in a hockey-stick pattern; and unique equilibrium oligopoly prices that are easily computed using a series of single-variable equations. However, the HEC model has several key differences with the EC model that show variances matter: the choice probabilities (market shares) as well as equilibrium oligopoly prices are not necessarily increasing with ideal utilities; and the new model can include choices with deterministic utility or choices with zero probability. However, because the HEC model uses more parameters, it is harder to estimate. To justify its use, we apply HEC to grocery purchase data for thirty product categories and find that it significantly improves model fit and generally improves out-of-sample prediction compared to EC. We go on to investigate the more nuanced impact of the variance parameters on oligopoly pricing. We find that the individual and collective incentives differ in equilibrium: Firms individually want lower error variability for their own product, but collectively prefer higher error variability for all products – including their own – because higher error variability softens the price competition.

Advance selling to strategic consumers

Advance selling of goods and services is a form of separating purchase from consumption. It is often employed when consumers are uncertain about their consumption utilities until a short time period before consumption. A book to be released, a concert to attend, or a cruise to take are some examples. Invariably, in consumers’ mind inventory availability (of copies, seats, or rooms) is a concern. In this paper we study a retailer’s inventory and pricing decisions in an advance selling scenario that involves consumers who are strategic. Some consumers not only consider advance and spot prices, but also the uncertainty in future availability of the product (during the spot period) and in their consumption utility from it. We characterize the optimal inventory management and pricing policies, and discuss several interesting aspects of the solution. For example, it can be optimal for the retailer to limit advance sales even if there is more demand for it, and it can be optimal for the retailer to limit its inventory even though there is more capacity to keep it, but not both.