A Regret Minimization Approach in Product Portfolio Management with respect to Customers’ Price-sensitivity

Document Type: Original Manuscript


Department of Industrial Engineering, College of Engineering, Alzahra University, Tehran, Iran.



In an uncertain and competitive environment, product portfolio management (PPM) becomes more challenging for manufacturers to decide what to make and establish the most beneficial product portfolio. In this paper, a novel approach in PPM is proposed in which the environment uncertainty, competitors’ behavior and customer’s satisfaction are simultaneously considered as the most important criteria in achieving a successful business plan. In terms of uncertainty, the competitors’ product portfolios are assumed as different scenarios with discrete occurrence probabilities. In order to consider various customer preferences, three different market segments are assumed in which the sensitivity of customers towards the products price are considered as high, medium and low and modeled by means of a modified utility functions. The best product portfolio with minimum risk of loss and maximum customer satisfaction is then established by means of a novel regret minimization index. The proposed index aims at finding the best product portfolio which minimizes the total possible loss and regret of the manufacturer, with respect to the other competitors of the market. To better illustrate the practicality of the approach, a numerical example is presented. The results show that the selected products in the suggested portfolio have the highest utility value in all market segments and also they are expected to achieve the highest expected payoff in each possible marketing scenario.

Graphical Abstract

A Regret Minimization Approach in Product Portfolio Management with respect to Customers’ Price-sensitivity


  • Developing an approach in which the environment uncertainty, competitors and customers preferences are simultaneously taken intoaccount.
  • Considering three market segments to enhance the customer satisfaction in terms of price-sensitivity.
  • Using utility functions to model the customers preferences towards product price in different market segments.
  • Developing a regret minimization approach in which the best product portfolio with minimum risk of loss is suggested to the manufacturer.



Main Subjects

BELLONI, A., FREUND, R., SELOVE, M. & SIMESTER, D. 2008. Optimizing Product Line Designs: Efficient Methods and Comparisons. Management Science, 54, 1544-1552.

BOERI, M. & MASIERO, L. 2014. Regret minimisation and utility maximisation in a freight transport context. Transportmetrica A: Transport Science, 10, 548-560.

BULMUŞ, T. & ÖZEKICI, S. 2012. Portfolio selection with hyperexponential utility functions. OR Spectrum, 1-21.

ÇANAKOĞLU, E. & ÖZEKICI, S. 2010. Portfolio selection in stochastic markets with HARA utility functions. European Journal of Operational Research, 201, 520-536.

COOPER, R. G., EDGETT, S. J. & KLEINSCHMIDT, E. J. 1999. New product portfolio management: practices and performance. Journal of Product Innovation Management, 16, 333-351.

DANTZIG, G. B. 1955. Linear programming under uncertainty. Management Science, 1, 197-206.

DAY, G. S. 1977. Diagnosing the product portfolio. Journal of Marketing, 41, 29-38.

GIRAUD, G. 2003. Strategic market games: an introduction. Journal of Mathematical Economics, 39, 355-375.

GLEIBNER, W., HELM, R. & KREITER, S. 2013. Measurement of competitive advantages and market attractiveness for strategic controlling. J Manag Control, 24, 53-73.

HALPERN, J. Y. & PASS, R. 2012. Iterated regret minimization: A new solution concept. Games and Economic Behavior, 74, 184-207.


HYAFIL, N. & BOUTILIER, C. 2004. Regret minimizing equilibria and mechanisms for games with strict type uncertainty. In: Proc. Twentieth Conference on Uncertainty in Artificial Intelligence (UAI 2004), 268-277.

JIAO, J. & ZHANG, Y. 2005. Product portfolio planning with customer-engineering interaction. IIE Transactions, 37, 801-814.

JIAO, J., ZHANG, Y. & WANG, Y. 2007. A heuristic genetic algorithm for product portfolio planning. Computers & Operations Research, 34, 1777-1799.

LI, X., SHOU, B. & QIN, Z. 2012. An expected regret minimization portfolio selection model. European Journal of Operational Research, 218, 484-492.

LIU, X., DU, G. & XIA, Y. Year. A stackelberg game theoretic approach to competitive product portfolio management. In:  12th International Symposium on Operations Research and its Applications in Engineering, Technology and Management (ISORA 2015), 21-24 Aug. 2015 2015. 1-7.

LOOMES, G. & SUGDEN, R. 1982. Regret Theory: An Alternative Theory of Rational Choice Under Uncertainty. The Economic Journal, 92, 805-824.

LUO, L. 2011. Product Line Design for Consumer Durables: An Integrated Marketing and Engineering Approach. Journal of Marketing Research, 48, 128-139.

MA, S. 2016. A nonlinear bi-level programming approach for product portfolio management. SpringerPlus, 5, 727.

MCNALLY, R. C., DURMUSOGLU, S. S., CALANTONE, R. J. & HARMANCIOGLU, N. 2009. Exploring new product portfolio management decisions: The role of managers' dispositional traits. Industrial Marketing Management, 38, 127-143.

MICHALEK, J. J. E., PETER , ADIGUZEL, F., FEINBERG, F. M. & PAPALAMBROS, P. Y. 2011. Enhancing marketing with engineering: Optimal product line design for heterogeneous markets. International Journal of Research in Marketing (IJRM), 28, 1-12.

NICOL, #242, CESA-BIANCHI, GENTILE, C. & MANSOUR, Y. 2013. Regret minimization for reserve prices in second-price auctions. Proceedings of the twenty-fourth annual ACM-SIAM symposium on Discrete algorithms. New Orleans, Louisiana: Society for Industrial and Applied Mathematics.

OH, J., YANG, J. & LEE, S. 2012. Managing uncertainty to improve decision-making in NPD portfolio management with a fuzzy expert system. Expert Systems with Applications, 39, 9868-9885.

OTTEN, S., SPRUIT, M. & HELMS, R. 2015. Towards decision analytics in product portfolio management. Decision Analytics, 2, 4.

RENOU, L. & SCHLAG, K. H. 2010. Minimax regret and strategic uncertainty. Journal of Economic Theory, 145, 264-286.

SADEGHI, A., ALEM-TABRIZ, A. & ZANDIEH, M. 2011. Product portfolio planning: a metaheuristic-based simulated annealing algorithm. International Journal of Production Research, 49, 2327-2350.

SADEGHI, A. & ZANDIEH, M. 2011. A game theory-based model for product portfolio management in a competitive market. Expert Systems with Applications, 38, 7919-7923.

SCHÖN, C. 2010. On the Optimal Product Line Selection Problem with Price Discrimination. Management Science, 56, 896-902.

SIMPSON, T. W. 2004. Product platform design and customization: Status and promise. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 18, 3-20.

SONG, Z. & KUSIAK, A. 2009. Optimising product configurations with a data-mining approach. International Journal of Production Research, 47, 1733-1751.

TAKAMI, M. A., SHEIKH, R. & SANA, S. S. 2016. Product portfolio optimisation using teaching–learning-based optimisation algorithm: a new approach in supply chain management. International Journal of Systems Science: Operations & Logistics, 3, 236-246.

VAN CRANENBURGH, S., GUEVARA, C. A. & CHORUS, C. G. 2015. New insights on random regret minimization models. Transportation Research Part A: Policy and Practice, 74, 91-109.

WARD, J., ZHANG, B., JAIN, S., FRY, C., OLAVSON, T., MISHAL, H., AMARAL, J., BEYER, D., BRECHT, A., CARGILLE, B., CHADINHA, R., CHOU, K., DENYSE, G., FENG, Q., PADOVANI, C., RAJ, S., SUNDERBRUCH, K., TARJAN, R., VENKATRAMAN, K., WOODS, J. & ZHOU, J. 2010. HP Transforms Product Portfolio Management with Operations Research. Interfaces, 40, 17-32.

YAGER, R. R. 2004. Decision making using minimization of regret. International Journal of Approximate Reasoning, 36, 109-128.

YANG, P., TANG, G. & NEHORAI, A. 2013. A Game-Theoric Approach for Optimal Time-of-Use Electricity Pricing. IEEE Transactions on Power Systems, 28, 884-892.

ZAPATA, J. C., VARMA, V. A. & REKLAITIS, G. V. 2008. Impact of tactical and operational policies in the selection of a new product portfolio. Computers & Chemical Engineering, 32, 307-319.