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Title of Journal: Ann Oper Res

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Abbravation: Annals of Operations Research

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Springer US

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DOI

10.1016/0167-9317(91)90105-m

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1572-9338

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Redesigning product lines in a period of economic

Authors: Stelios Tsafarakis
Publish Date: 2015/10/09
Volume: 247, Issue: 2, Pages: 617-633
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Abstract

The optimal product line design is an NPhard optimization problem in marketing that involves a number of decisions such as product line length and configuration Simulated annealing constitutes the best performing approach so far but with extremely large running times In the current study simulated annealing is hybridized with an evolutionary algorithm to improve its search efficiency and alleviate its performance dependence on the selection of the parameters related to its cooling schedule The presented approach outperforms genetic algorithms and classic simulated annealing through the use of crossover as a neighborhood operator along with the restricted tournament selection as the replacement strategy of the evolutionary algorithm’s population Moreover the paper describes the way that the proposed hybrid metaheuristic can be used for redesigning a firm’s product line The issue of redesigning product lines becomes even more important in periods of economic crisis as firms must adapt their offerings to new evolving patterns of consumer buying behavior and reduced levels of consumer’s purchasing power The applicability of the proposed approach is illustrated through the case of the 2008 automotive industry crisis by showing how the North American car manufacturers could have redesigned their lines on time based on the configuration of the competitive products in the market as well as the new customer preferences emerged during the economic recessionIn the optimal product line design problem every product is represented as a bundle of features attributes with each attribute taking a number of specific levels from a predefined range Using market research and conjoint analysis consumer preferences can be measured Through the evaluation of a small group of hypothetical product profiles customers implicitly reveal their interests for each of the various levels of the different attributes For example a personal computer consists of monitor processor hard disc and memory considering these as attributes which may take the levels 17” 21” or 23” dualcore or quadcore 500GB 750GB or 1T and 4GB or 8GB respectively Levels of attributes are being chosen by customers according to their needs and preferences A computer engineer for instance probably prefers a fast processor whereas an architect may prefer a large monitor With the use of conjoint analysis a partworth matrix is constructed for every consumer that contains the value associated with each level of each attribute The sum of the corresponding partworths provides the utility of a product profile Using a choice model product utilities for each customer are converted to choice probabilities for each product the aggregation of which results in simulated hypothetical market shares

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