Simulation  

For evaluation of more complex managerial decisions involving risk, simulation is often used. This involves use of a computer programme that explicitly and simultaneously considers all the interactions among variables of the model to determine the expected outcome and risk of a particular business strategy.

A firm might construct a model for the strategy of expanding the output of a commodity. The model would specify in mathematical (i.e., equational) form, the relationship between the output of the commodity and its price; output, input prices, and costs of production; output and depreciation; output, selling costs, and revenue; output revenues, and taxes; and so on. The manager could then substitute likely values or best estimates for each variable into the model and estimate the firm's profit. By then varying the value of each variable substituted into the model, the firm can get an estimate of the effect of the change in the variable on the output of the model or profit of the firm. This simplest type of simulation is often referred to as sensitivity analysis.

In full fledged simulation models, the model builder needs to estimate or specify the probability distribution of each variable in the model. Randomly selected values of each variable of the model are then fed into the computer programme to generate the present value of the firm's profits. This process is then repeated a large number of times. Each time a new randomly selected value of each variable is fed into the computer programme, and the net present value of the firm's profit is recorded. The probability distribution of the firm's profits so generated can then be used to calculate the expected profit of the firm and the standard deviation of the distribution of profits. Finally, the firm can use this information to determine the optimal strategy to adopt. 

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