Dynamic Programming: Models and Applications
Dynamic Programming: Models and Applications
Competition and Outsourcing with Scale Economies
Management Science
Make to Order or Make to Stock: Model and Application
Management Science
Product Differentiation and Capacity Cost Interaction in Time and Price Sensitive Markets
Manufacturing & Service Operations Management
On the Benefits of Pooling in Production-Inventory Systems
Management Science
Assortment Planning and Inventory Decisions Under a Locational Choice Model
Management Science
Retail Assortment Planning in the Presence of Consumer Search
Manufacturing & Service Operations Management
Mass Customization vs. Mass Production: Variety and Price Competition
Manufacturing & Service Operations Management
Standard vs. Custom Products: Variety, Lead Time, and Price Competition
Marketing Science
Optimal Algorithms for Assortment Selection Under Ranking-Based Consumer Choice Models
Manufacturing & Service Operations Management
Learning Consumer Tastes Through Dynamic Assortments
Operations Research
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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.