Sensitivity and Breakeven Analysis pptx

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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
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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.
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Process of Analysis
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Identification of those variables which will
have significant impacts on the NPV, if their
future values vary around the forecast values.
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The variables having significant impacts on the
NPV are known as ‘sensitive variables’.
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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
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:
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Management Use of Sensitivity and
Breakeven Analysis
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(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:
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Management Use of Sensitivity
and Breakeven Analysis
Using Breakeven:
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Forecasted calculated Breakeven values of

variables are continuously compared against
actual outcomes during the execution phase.
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Terminology Within the Analysis
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Sensitivity and Breakeven analyses are also known as:
‘scenario analysis’, and ‘what-if analysis’.
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Point values of forecasts are known as: ‘optimistic’,
‘most likely’, and ‘pessimistic’.
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Respective calculated NPVs are known as: ‘best case’,
‘base case’ and ‘worst case’.
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Variables giving a ‘breakeven’ value, return an NPV of
zero for the project.
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Selection Criteria For Variables
in the Analysis
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Degree of management control.
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Management's confidence in the forecasts.
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Amount of management experience in assessing
projects.
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Extrinsic variables more problematic than
intrinsic variables.
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Time and cost of analysis.
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Real Life Examples
of Forecast Errors
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Large blowouts in initial construction
costs for Sydney Opera House,
Montreal Olympic Stadium.
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Big budget films are shunned by critics
and public alike; e.g ‘Waterworld’:
whilst cheap films become classics;
eg.‘Easy Rider’.
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High failure rate of rockets used to
launch commercial satellites.
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Developing Optimistic and
Pessimistic Forecasts
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(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

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

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Easy to understand.
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Forces planning discipline.
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Helps to highlight risky variables.
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Relatively cheap.
•

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Relatively unsophisticated.
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May not capture all information.
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Limited to one variable at a time.
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Ignores interdependencies.