Leadtime-Variety Tradeoff in Product Differentiation

Abstract

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

Learning Consumer Tastes Through Dynamic Assortments

“…Lancaster's model involves a uniform density of consumers in a continuous attribute space. Gaur and Honhon (2006) and Alptekinoglu and Corbett (2010) use the model with a continuous and nonuniform distribution of consumer tastes in operations management settings. In contrast, we take a finitely countable set of discrete points in the attribute space as consumer locations, which are known to the firm, and assume a general discrete probability distribution (possibly nonuniform) over it.…”

“…Chen et al (1998) and Alptekinoglu and Corbett (2010) use a similar solution method. Unlike our model, both papers use a continuous attribute space for products and consumers, and neither paper accounts for the possibility of not covering some portions of the market.…”

When to Carry Eccentric Products? Optimal Retail Assortment under Consumer Returns

“…A growing body of work explores relevant operational concerns such as inventory (van Ryzin and Mahajan 1999, Smith and Agrawal 2000, Aydın and Ryan 2000, Gaur and Honhon 2006, joint inventory-pricing decisions (Maddah and Bish 2007), substitution upon stockout (Honhon et al 2010), delivery leadtime (Alptekinoglu and Corbett 2010), and modularity in product design (Hopp and Xu 2005). Another line of research tackles strategic and competitive aspects such as entry deterrence (Bayus and Putsis 1999), channel coordination (Aydın and Hausman 2009), and competitive implications of consumer search (Cachon et al 2008), basketshopping behavior (Cachon and Kök 2007), brand preference (Kök and Xu 2011) and product satiation (Caro and Martinez-de-Albeniz 2012).…”

An analytical framework for evaluating the value of enhanced customisation: an integrated operations-marketing perspective

“…In this paper the model is extended to particularly accommodate a wider range of customisation level. Finally, Alptekinoglu and Corbett (2009) analyse the trade-off between the enhanced ability to meet individual preferences and the longer lead time associated with customised products.…”