Page 7 - CIMA MCS Workbook May 2019 - Day 2 Suggested Solutions

SUGGESTED SOLUTIONS

There would also be issues with the probabilities that we would use. How would we estimate
them? How much reliance could we put on them? If they too are subject to much uncertainty
then we should not place too much reliance on calculations that use them.

Identify best and worst case scenarios

One way of highlighting the risk levels involved is to run appraisals on both the best case (e.g.
highest growth) and worst case (e.g. lowest growth) scenarios.

The difference between the results of the two appraisals will indicate the overall risk levels
involved.

Apply more difficult targets

When performing investment appraisals, comparison will be made of the project results to a
target figure. In NPV calculations the target cost of capital is incorporated into the calculations
themselves. For other appraisal tools such as payback or accounting rate of return (ARR), an
arbitrary target is set by management.

Projects that meet or beat these targets are deemed to be acceptable. If we know that the
figures used in the appraisal are uncertain, one way of addressing this is to use a more difficult
target, so add a couple of percentage points onto the cost of capital figure used or target ARR
figure, or to shorten the payback period. By making it more difficult for the projects to be
accepted we are automatically incorporating an element of a buffer to deal with the uncertainty
faced.

Sensitivity analysis

Once a project appraisal such as an NPV calculation has been done, we can do extra calculations
that allow us to identify figures critical to the success of the project. We could also, for instance,
increase all of our costs by 5% and look at how it affects the results, or reduce our sales prices by
10% and see the effects.

One way of using this sensitivity analysis is to look at a particular variable and calculate by how
much it would need to alter for us to change our minds about the decision to go ahead with the
project or not. Variables whose calculations produced a low sensitivity figure would be deemed
to be critical ones, as only small changes in these variables would affect the overall feasibility of
the project.

For instance, if it was found that there was a 10% sensitivity to the planned sales volumes and a
1% sensitivity to the transport costs between factory and site then our main concern would be
that it only needs a 1% increase in transport costs compared to the numbers we’ve predicted for
the project to fail. We would then need to investigate this further, aiming to either reduce the
transport costs or get more certainty about its potential costs.

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