• DocumentCode
    613629
  • Title

    Bayesian updating on price elasticity of uncertain demand

  • Author

    Zhengwei Sun ; Abbas, Ali E.

  • Author_Institution
    Dept. of Ind., Enterprise & Syst. Eng., Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA
  • fYear
    2013
  • fDate
    15-18 April 2013
  • Firstpage
    222
  • Lastpage
    228
  • Abstract
    One of the most important aspects in the job of a design engineer is to choose the design alternative with the highest expected utility for the firm. In a profit-maximizing setup, the expected utility at a given price is determined by the demand for the product at that price as well as the utility function over profit. This paper provides a mechanism for characterizing the uncertainty about demand as a function of its price. We characterize the demand distribution using a scaled Poisson process. We then show how to estimate demand by fitting its expectation and variance at a given price to the Poisson process. Finally, we provide an approximate analytic solution for the optimal price of a product that offers the highest expected utility for the design firm. Our results indicate that the more risk averse firm has a lower optimal price than the less risk averse firm. We also calculate the expected value of perfect information on the demand at any price via Bayesian updating.
  • Keywords
    belief networks; design engineering; organisational aspects; pricing; profitability; risk analysis; stochastic processes; Bayesian updating; approximate analytic solution; demand distribution; design engineer; design firm; optimal price; price elasticity; product demand; profit-maximizing setup; risk averse firm; scaled Poisson process; uncertain demand; utility function; Bayes methods; Demand forecasting; Equations; Estimation; Fitting; Standards; Stochastic processes; Bayesian update; Poisson process; risk aversion; value of information;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems Conference (SysCon), 2013 IEEE International
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    978-1-4673-3107-4
  • Type

    conf

  • DOI
    10.1109/SysCon.2013.6549886
  • Filename
    6549886