Cvp vs. Stocastic Analysis
The subject of cost-volume profit analysis under uncertainty has had extensive literature collected in recent years even though the topic has been ignored in most textbooks. In many cases, entire chapters are devoted to cost-volume profit analysis but they ignore the possibility that one or more parameters of the problem are completely random and therefore the future values are unknown at the time the decision is made. The reluctance of textbook authors to discuss stochastic CVP models can be attributed to the diversity of the literature published. Since numerous models have been proposed and examined, such as single product versus multi-product, it doesn’t really matter the model you use because it is likely to be complicated because it involves various concepts from mathematical statistics. It is a well known fact that real world decision making takes place under conditions of uncertainty and the basic cost-volume profit model helps to create a clearer picture of the variables by generalizing the model to any uncertain situation. In this paper I will present, analyze and apply a stochastic CVP model that can be used as a gateway to decision-making under uncertainty.
While
the full mathematical derivations of the results shown herein are probably too complicated for most undergraduates, the results themselves are fairly straightforward, and they facilitate a precise focus on such fundamental concepts in decision-making under uncertainty as the tradeoff between expected profits and breakeven probability.
There is an inevitable tradeoff between the comprehensiveness and realism of a model (which tend to generate mathematical complexity) and its practicality and ease-of-use (the extent to which it can readily provide definite answers to specific questions). The model presented here attempts to strike an appropriate balance between these two competing criteria. It is a much simpler model than many of those found in the research literature. For example, the
While
the full mathematical derivations of the results shown herein are probably too complicated for most undergraduates, the results themselves are fairly straightforward, and they facilitate a precise focus on such fundamental concepts in decision-making under uncertainty as the tradeoff between expected profits and breakeven probability.
There is an inevitable tradeoff between the comprehensiveness and realism of a model (which tend to generate mathematical complexity) and its practicality and ease-of-use (the extent to which it can readily provide definite answers to specific questions). The model presented here attempts to strike an appropriate balance between these two competing criteria. It is a much simpler model than many of those found in the research literature. For example, the