Abstract
The product life cycle of cars is becoming shorter and carmakers constantly introduce new or revised models in their lines, tailored to their customer needs. At the same time, new car model design decisions may have a substantial effect on the cost and revenue drivers. For example, although a new car model configuration with component commonality may lower manufacturing cost, it also hinders increased revenues that could have been achieved through product differentiation. This paper applies a state of the art, nature inspired approach to design car lines that optimize the degree of differentiation vs commonality among models in the line. Our swarm intelligence mechanism is applied to stated preference data derived from a large-scale conjoint experiment that measures consumer preferences for passenger cars in a sample of 1,164 individuals. Our approach provides interesting insights on how new and existing car models can be combined in a product line and suggests that differentiation among models within a product line elevates customer satisfaction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Albritton, M.D., McMullen, P.R.: Optimal Product Design Using a Colony of Virtual Ants. European Journal of Operational Research 176(1), 498–520 (2007)
Beck, N.: Time-series-cross-section data: What have we learned in the past few years? Annual Review of Political Science 4, 271–293 (2001)
Beck, N., Katz, J.: Random coefficient models for time-series–cross-section data: Monte carlo experiments. Political Analysis 15(2), 182–195 (2007)
Belloni, A., Freund, R., Selove, M., Simester, D.: Optimizing Product Line Designs: Efficient Methods and Comparisons. Management Science 54(9), 1544–1552 (2008)
Desai, P., Kekre, S., Radhakrishnan, S., Srinivasan, K.: Product differentiation and commonality in design: Balancing revenues and cost drivers. Management Science 47(1), 37–51 (2001)
Green, P., Krieger, A., Wind, Y.: Thirty years of conjoint analysis: Reflections and prospects. Interfaces 31, S56–S73 (2001)
Heese, H.S., Swaminathan, J.M.: Product Line Design with Component Commonality and Cost-Reduction Effort. Manufacturing and Service Operations Management 8(2), 206–219 (2006)
Hsiao, C.: Analysis of Panel Data. Cambridge University Press, Cambridge (1995)
Jan, T.S., Hsiao, T.: A four-role model of the automotive industry development in developing countries: A case in Taiwan. Journal of the Operational Research Society 55, 1145–1155 (2004)
Kennedy, J.: The Particle Swarm: Social Adaptation of Knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308. IEEE Service Center, Indianapolis (1997)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Orlando, FL, USA, pp. 4104–4108 (1997)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Kohli, R., Sukumar, R.: Heuristics for product line design using conjoint analysis. Management Science 36(12), 1464–1478 (1990)
Nair, S.K., Thakur, L.S., Wen, K.: Near optimal solutions for product line design and selection: Beam Search heuristics. Management Science 41(5), 767–785 (1995)
Robertson, D., Ulrich, K.: Planning for product platforms. Sloan Management Review 39(4), 19–31 (1998)
Steiner, W., Hruschka, H.: Generic Algorithms for product design: how well do they really work? International Journal of Market Research 45(2), 229–240 (2003)
Western, B.: Causal heterogeneity in comparative research: A bayesian hierarchical modelling approach. American Journal of Political Science 42, 1233–1259 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Saridakis, C., Tsafarakis, S., Baltas, G., Matsatsinis, N. (2012). Designing Lines of Cars That Optimize the Degree of Differentiation vs. Commonality among Models in the Line: A Natural Intelligence Approach. In: Casillas, J., MartÃnez-López, F., Corchado RodrÃguez, J. (eds) Management Intelligent Systems. Advances in Intelligent Systems and Computing, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30864-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-30864-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30863-5
Online ISBN: 978-3-642-30864-2
eBook Packages: EngineeringEngineering (R0)