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Risk analysis is about supporting decisions by answering questions about risk. We attempt to provide qualitative, and where time and knowledge permit, quantitative information to decision-makers that are pertinent to their questions. Inevitably, decision-makers must deal with other factors that may not be quantified in a risk analysis, which can be frustrating for a risk analyst when they see their work being 'ignored'. Don't let it be: the best risk analysts remain professionally neutral to the decisions that are made from their work.

 

The first step to designing a good model is to put yourself in the position of the decision-maker by understanding how the information you might provide connects to the questions they are asking. A decision-maker often does not appreciate all that comes with asking a question in a certain way, and may not initially have worked out all the possible options for handling the risk (or opportunity).

 

When you believe that you properly understand the risk question or questions that need(s) answering, it is time to brainstorm with colleagues, stakeholders, and the managers about how you might put an analysis together that satisfies the managers' needs. Effort put into this stage pays back ten fold: everyone is clear on the purpose of your analysis; the participants will be more co-operative in providing information and estimates; and you can discuss the feasibility of any risk analysis approach. We recommend you think of mapping out your ideas with Venn diagrams and event trees. Then look at the data (and perhaps expertise for subjective estimates) you believe are available to populate the model. If there are data gaps (there usually are), consider whether you will be able to get the necessary data to fill the gap, and quickly enough to be able to produce an analysis within the decision-maker's time frame.  If the answer is 'no', look for other ways to produce an analysis that will meet the decision maker's needs, or perhaps a sub-set of those needs. But whatever you do, don't embark on a risk analysis where you know that data gaps will remain and your decision maker is left with no useful support. Some scientists argue that risk analysis can also be for research purposes - to determine where the data gaps lie. We see the value in that determination, of course, but if that is your purpose, state it clearly and don't leave any expectation from the managers that will be unfulfilled.

 

The following section offers some quality control tips to help you produce an efficient model that is the most easy to understand, modify and check:

 

Building models that are easy to check and modify

Building models that are efficient

Colour coding model elements for clarity

Using range names for model clarity

Writing distribution parameters in your spreadsheet and "Dynamic Referencing"

 

 

 

 


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