As an example, the Risk Portfolio model is provided. The model estimates the impact of a set of risks that may impinge on a project.
In this model the total cost of a project is being estimated. Seven uncertain elements have been modeled:
The base project cost;
The potential impact of five identified risks: Health and Safety Executive intervention; a strike; bad weather; sub-contractor insolvency and a change in the ruling political party;
The rate of inflation
The base project cost is modeled by a simple Triangular distribution. The inflation rate is also modeled with a Triangular distribution. The selection of a Triang or PERT to express uncertainty given a three point estimate (minimum, most_likely, maximum) is discussed elsewhere.
The links to the Risk Portfolio software specific models are provided here:
Here is a screenshot of the model:
The point of this model is really to illustrate a way of modeling inter-related risk events. H&S, bad bad weather, and political change risks have 10%, 30% and 2% probability of occurring. The risk of strike, however, has a 15% chance of occurring unless the H&S risk occurs. When it is considered, the probability increases to 30%. The insolvency probability is 5%, but goes up to 75% if the H&S and the strike risks both occur. A column was set up that generates a '1' if the risk occurs, and a '0' if not allows us to build a logic using Excel's IF function that alters the probability of these two risks accordingly. Column Distribution models the risk impact: a range of 80% to 150% of the most likely risk impact is modeled using a Triangular distribution (80% and 150% is for the convenience of illustration: we recommend that you review each risk separately).
The effect of this model is to recognize that the H&S risk has a much more significant impact than one might suppose when reviewing it in isolation. It is extremely common for risks to be inter-connected: for example, a certain risk occurring might draw resources to manage it that are no longer available to prevent another risk. The occurrence of a risk might also affect the size of an impact of another risk. It is simply modeled by using the same IF logic on the Most Likely (M L) value column.
Here is a screenshot of the model:
The point of this model is really to illustrate a way of modeling inter-related risk events. H&S, bad bad weather, and political change risks have 10%, 30% and 2% probability of occurring. The risk of strike, however, has a 15% chance of occurring unless the H&S risk occurs. when it is considered the probability increases to 30%. The insolvency probability is 5%, but goes up to 75% if the H&S and the strike risks both occur. Setting up Column E that generates a '1' if the risk occurs, and a '0' if not allows us to build a logic using Excel's IF function that alters the probability of these two risks accordingly. Column F models the risk impact: a range of 80% to 150% of the most likely risk impact is modeled using a Triang distribution (80% and 150% is for the convenience of illustration: we recommend that you review each risk separately).
The effect of this model is to recognize that the H&S risk has a much more significant impact than one might suppose when reviewing it in isolation. It is extremely common for risks to be inter-connected: for example, a certain risk occurring might draw resources to manage it that are no longer available to prevent another risk. The occurrence of a risk might also affect the size of an impact of another risk. We haven't shown it here, but it is simply modeled by using the same IF logic on the Most Likely (M L) value column.
