Chapter 8: Sensitivity and Breakeven Analysis

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Chapter 8 Sensitivity and Breakeven Analysis

• Analyzing project risks by making mechanical
  trial and error changes to forecast values of
  selected variables.

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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.

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Management Use of Sensitivity and Breakeven
Analysis
Using Sensitivity

• 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.

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Management Use of Sensitivity and Breakeven
Analysis
Using Sensitivity
Sensitive variables are investigated and managed
in two ways

• (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.

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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.

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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.

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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.

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Real Life Examplesof 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.

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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.

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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.
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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.

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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.