Risk Management: A practical toolkit for identifying, analyzing, and coping with project risks

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Further improvements to specifying the impacts
There are a number of improvements to this basic idea, which make it more communicative and credible. Here is a brief summary of them :

Uncertainty of Impact : you can specify a range of values rather than a single value.

Evidence for Impact Assertion : you can state the basis for making your estimate.

For example: "Strategy B was used for 5 projects last year in our company, and the percentage improvement for Training Times was always 60% to 80%".

Source of Evidence for Impact Assertion : Of course, some skeptic might like to check your assertion and evidence out, so you should give them a source reference, e.g. "Company Research Report ABR-017, pages 23-24."

Credibility Rating of the Impact Assertion : We have found it very useful to establish a numeric 'credibility' for an estimate, based on the credibility of the evidence and the source. We use a scale of 0.0 to 1.0 (because it can then be used later to modify estimates in a conservative direction). See Table 2.

TABLE 2

Credibility Rating Meaning
0.0 wild guess, no credibility
0.1 we know it has been done somewhere
0.2 we have one measurement somewhere
0.3 there are several measurements in the estimated range
0.4 the measurements are relevant to our case
0.5 the method of measurement is considered reliable
0.6 we have used the method in-house
0.7 we have reliable measurements in-house
0.8 reliable in-house measurements correlate to independent
external measurements
0.9 we have used the idea on this project and measured it
1.0 perfect credibility, we have rock solid, contract-
guaranteed, long-term, credible experience with this idea on this project and, the results are unlikely to disappear

Further Analysis of the IE data
Once you have completed filling in all the impacts, there are a number of calculations, using the percentage impact estimates (%Impact), that help you understand the risks involved with your proposed solution.

Let me stress that these are only rough, practical calculations. Adding impacts of different independent estimates for different strategies, which are part of the same overall architecture, is dubious in terms of accuracy. But, as long as this is understood, you will find them very powerful when considering such matters as whether a specific quality goal is likely to be met or which is the most effective strategy. The insights gained are frequently of use in generating new strategies.

Impact on a Quality : For each individual quality or resource attribute, sum all the percentage impacts for the different strategies.This gives us an understanding of whether we are likely to make the planned level for each quality or cost. Very small quality impact sums like '4%' indicate high risk that the architecture is probably not capable of meeting the goals. Large numbers like 400% indicate that we might have enough design, or even a 'safety margin'.

TABLE 3
Example: Adding the percentage impacts for a set of strategies on a single quality or cost can give some impression of how the strategies are contributing overall to the objectives. Note Strategies A, B and C are independent and complementary.

 

Strategy A

Strategy B

Strategy C

Sum of Strategy Impacts

Sum Uncertainty

Reliability
900->1000
hours MTBF

0+/-10%

10+/-20%

50+/-40%

60%

+/-70%

Impact of a Strategy : For each individual strategy, sum all the percentage impacts it achieves across all the qualities to get an estimate of its overall effectiveness in delivering the qualities. The resulting estimates can be used to help select amongst the strategies. It is a

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