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The optimal resource portfolio under consumer choice and demand risk for vertically differentiated products

Author

Listed:
  • Ebru K Bish

    (Virginia Tech, Grado Department of Industrial and Systems Engineering, Blacksburg, VA, United States)

  • Weiping Chen

    (Accenture Analytics, Chicago, IL, United States)

Abstract

We study the optimal resource portfolio of a firm that sells two vertically differentiated products and utilizes resource flexibility and responsive pricing. We model this decision problem as a two-stage stochastic programming problem with recourse: In the first stage, the firm determines its resource mix and capacities so as to maximize the expected profit under demand uncertainty; in the second stage, uncertainty is resolved and the firm determines its production and pricing decision, constrained by its investment decision. We show that the objective function of this decision problem is not well-behaved (ie, it may have multiple local maxima). Using the concept of Pareto dominance, we reduce the feasible investment region, without loss of optimality, to one in which the objective function is well-behaved everywhere. This reduction allows us to derive the necessary and sufficient conditions for the optimal capacity decision and to gain insights.

Suggested Citation

  • Ebru K Bish & Weiping Chen, 2016. "The optimal resource portfolio under consumer choice and demand risk for vertically differentiated products," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(1), pages 87-97, January.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:1:p:87-97
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    Cited by:

    1. Jalali, Hamed & Van den Broeke, Maud & Van Nieuwenhuyse, Inneke, 2022. "Platform and product design for markets with quality and feature sensitive customers," International Journal of Production Economics, Elsevier, vol. 244(C).
    2. Jalali, Hamed & Carmen, Raïsa & Van Nieuwenhuyse, Inneke & Boute, Robert, 2019. "Quality and pricing decisions in production/inventory systems," European Journal of Operational Research, Elsevier, vol. 272(1), pages 195-206.

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