1 Chapter 8: Sensitivity and Breakeven Analysis Analyzing project risks by making mechanical trial and error changes to forecast values of selected variables.

2 Introduction Analyzing the risks of investment projects, by changing the values of forecasted variables. Finding the values of particular variables which give the project a Breakeven NPV of zero.

3 Process of Analysis Identification of those variables which will have significant impacts on the NPV, if their future values vary around the forecast values. The variables having significant impacts on the NPV are known as ‘sensitive variables’. The variables are ranked in the order of their monetary impact on the NPV. The most sensitive variables are further investigated by management.

4 Management Use of Sensitivity and Breakeven Analysis Sensitive variables are investigated and managed in two ways: (1) Ex ante; in the planning phase; more effort is used to create better forecasts of future values. If management decides the project is too risky, it is abandoned at this stage. Using Sensitivity:

5 Management Use of Sensitivity and Breakeven Analysis (2) Ex post; in the project execution phase; management monitors the forecasted values. If the project is performing poorly, it is abandoned or sold off prior to its planned termination. Using Sensitivity: Sensitive variables are investigated and managed in two ways:

6 Management Use of Sensitivity and Breakeven Analysis Using Breakeven: Forecasted calculated Breakeven values of variables are continuously compared against actual outcomes during the execution phase.

7 Terminology Within the Analysis Sensitivity and Breakeven analyses are also known as: ‘scenario analysis’, and ‘what-if analysis’. Point values of forecasts are known as: ‘optimistic’, ‘most likely’, and ‘pessimistic’. Respective calculated NPVs are known as: ‘best case’, ‘base case’ and ‘worst case’. Variables giving a ‘breakeven’ value, return an NPV of zero for the project.

8 Selection Criteria For Variables in the Analysis Degree of management control. Management's confidence in the forecasts. Amount of management experience in assessing projects. Extrinsic variables more problematic than intrinsic variables. Time and cost of analysis.

9 Real Life Examples of Forecast Errors Large blowouts in initial construction costs for Sydney Opera House, Montreal Olympic Stadium. Big budget films are shunned by critics and public alike; e.g ‘Waterworld’: whilst cheap films become classics; eg.‘Easy Rider’. High failure rate of rockets used to launch commercial satellites.

10 Developing Optimistic and Pessimistic Forecasts ( a) Use forecasting –error information from the forecasting methods: eg - upper and lower bounds; prediction interval; expert opinion; physical constraints, are applied to the variables. This method is formalized, but arguable, slow and expensive.

11 Developing Optimistic and Pessimistic Forecasts (b) Use ad hoc percentage changes: a fixed percentage, such as 20%,or 30%, is added to and subtracted from the most likely forecast value. This method is vague and informal, but fast, popular, and cheap. ? +20% -20%

12 Outputs and Uses Each forecast value is entered into the model,and one solution is given. Solutions can be summarized automatically, or individually by hand. Variables are ranked in order of the monetary range of calculated NPVs. Management investigates the sensitive variables. More forecasting is done, or the project is accepted or rejected as is.

13 Strengths and Weaknesses of Analysis Easy to understand. Forces planning discipline. Helps to highlight risky variables. Relatively cheap Relatively unsophisticated. May not capture all information. Limited to one variable at a time. Ignores interdependencies.