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OPTIMIZING AND SATISFICING IN STOCHASTIC COST‐VOLUME‐PROFIT ANALYSIS
Author(s) -
Ismail Badr E.,
Louderback Joseph G.
Publication year - 1979
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1979.tb00019.x
Subject(s) - profit maximization , maximization , satisficing , probability density function , profit (economics) , mathematical optimization , function (biology) , economics , volume (thermodynamics) , econometrics , computer science , mathematical economics , microeconomics , mathematics , statistics , physics , quantum mechanics , evolutionary biology , biology
This paper examines some of the implications of introducing penalties for output not equalling demand by employing a general stochastic model for a firm facing an uncertain demand with a known probability density function. Several alternative objectives of the firm are considered: (1) maximization of expected profits; (2) maximization of the probability of achieving a particular target level of profits; and (3) maximization of target profits, given a target level of the probability of their being achieved. It is shown that the resulting probability density function of profits is not well defined. The shape and location of the function depend on the relative magnitudes of the model parameters and the output decision. Several important implications of this result for cost‐volume‐profit analysis are discussed.