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Heuristics for Product-Line Design Using Conjoint Analysis

Heuristics for Product-Line Design Using Conjoint Analysis Recently proposed methods for product-line selection use the total utilities of candidate items to construct product lines maximizing seller's return or buyers' welfare. For conjoint (hybrid conjoint) data, enumerating the utilities of candidate items can be computationally infeasible if the number of attributes and attribute levels is large and most multi-attribute alternatives are feasible. For such problems, constructing product lines directly from part-worths data is preferable. We propose such methods, extending Kohli and Krishnamurti's (1987) dynamic-programming heuristic for selecting a single item maximizing share to structure product lines maximizing share, seller's return, or buyers' (utilitarian) welfare. The computational performance of the heuristics and their approximation of product-line solutions is evaluated using simulated data. Across problem instances, the dynamic-programming heuristics identify solutions that are no worse, in terms of approximating optimal solutions, to the solutions of heuristics for the current two-step approaches to product-line design. An application using hybrid-conjoint data for a consumer-durable product is described. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management Science INFORMS

Heuristics for Product-Line Design Using Conjoint Analysis

Kohli, Rajeev; Sukumar, R.
Management Science , Volume 36 (12): 15 – Dec 1, 1990
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15 pages

 
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References (21)

Publisher
INFORMS
Copyright
Copyright © INFORMS
Subject
Research Article
ISSN
0025-1909
eISSN
1526-5501
DOI
10.1287/mnsc.36.12.1464
Publisher site
See Article on Publisher Site

Abstract

Recently proposed methods for product-line selection use the total utilities of candidate items to construct product lines maximizing seller's return or buyers' welfare. For conjoint (hybrid conjoint) data, enumerating the utilities of candidate items can be computationally infeasible if the number of attributes and attribute levels is large and most multi-attribute alternatives are feasible. For such problems, constructing product lines directly from part-worths data is preferable. We propose such methods, extending Kohli and Krishnamurti's (1987) dynamic-programming heuristic for selecting a single item maximizing share to structure product lines maximizing share, seller's return, or buyers' (utilitarian) welfare. The computational performance of the heuristics and their approximation of product-line solutions is evaluated using simulated data. Across problem instances, the dynamic-programming heuristics identify solutions that are no worse, in terms of approximating optimal solutions, to the solutions of heuristics for the current two-step approaches to product-line design. An application using hybrid-conjoint data for a consumer-durable product is described.

Journal

Management Science – INFORMS

Published: Dec 1, 1990

Keywords: Keywords : marketing: product policy ; conjoint analysis ; programming: heuristics

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