The increase in recent years in the sophistication, power, and user-friendliness of spreadsheets such as Microsoft Excel has meant that more decision makers are building their own spreadsheet models to help them in their decision making. These models however are only as good as two things. Firstly, the data that goes into the model, and secondly, the logic of the relationships built into the model. Inaccurate input data values or faulty logic can only produce bad results, especially if the inaccurate data values are the key or critical variables (Beaman, Ratnatunga, Krueger, and Mudalige, 2002). Obviously, more care should be taken when estimating an input variable to which the output is highly sensitive and conversely, less time and care taken in estimating an input variable to which the output is less sensitive (Beaman et al., 2002). But, how model builder can identify which variables are key or critical? Performing sensitivity analysis is the answer to this question. Sensitivity analysis enables the model builder to identify the key variables. In other words to identify those input variables to which the output is most sensitive.…
