The cost concept of prime importance for decision making is that of incremental costs. Incremental costs are those that are incurred as a result of the decision under consideration. To calculate incremental costs, however, the decision maker must consider a variety of other cost concepts, such as direct and indirect, explicit and implicit, opportunity and historic, and relevant and sunk costs. Each of these cost concepts was illustrated with reference to a particular business example.
Contribution analysis seeks to ascertain the contribution to overheads and profits, or the excess of incremental revenues over incremental costs, that is expected to follow a particular decision. To calculate the contribution of a decision the decision maker must consider the present-period explicit costs and revenues arising from the decision, plus the opportunity costs and revenues associated with the decision, plus
300 Production and Cost Analysis
the expected present value of future costs and revenues that are subsequent to t e decision. The expected present value of contribution (EPVC) of each decision alternative is then compared with the EPVC of the other decision alternatives. When uncertainty is involved, as we should expect in multiperiod contribution analysis, the risk attitude of the decision maker must be considered. A risk-neutral decision maker will simply select the decision alternative with the greatest EPVC. A decision maker who is risk averse (or risk preferring) should utilize criteria that adjust for risk, such as the coefficient of variation, certainty equivalent, and maximin.
As before, we should be careful to conduct sensitivity analysis before plunging ahead with a decision based on EPVC calculations. Many of the consequences of a decision may be subject to a high degree of uncertainty, and our best estimates may not be especially reliable. Other issues may be assumed away, on the basis of a presumption that they will not occur. Sensitivity analysis should be undertaken to see whether or not the decision alternative selected would remain the optimal one if our underlying assumptions turn out to be in error. The assumptions most often made that cry out for sensitivity analysis fall under two broad headings, namely, data accuracy and ceteris paribus. Data-accuracy assumptions include the following: accuracy of the figures used and the lack of any other current-period, future-period, or opportunity incremental costs or revenues. Ceteris paribus assumptions include the lack of any impact on labor, customer, and supplier relations (with subsequent current or future cost or revenue implications), as well as the lack of any adverse impact on the firm s product quality, financial stability, public image, and so on.