A Simple Proposal for Qualitative and Quantitative Risks Analyses Integration (Part 3)
This is a third article in a series. In the last article, I introduced an example—Project A—and showed how to implement our methodology for qualitative and quantitative risk analyses integration (covered in the first article). Now we continue the example, doing an analysis of the results and proposing mitigation plans that could yield a lower dispersion for the final date. Is carrying out these plans a good idea? Let’s see how it works out, using Monte Carlo simulations to understand the impacts and measure the losses and gains in our model.
Project A: How We Left Off
Our project leader has some tough decisions to make. The risk profile for his project is, well, challenging to say the least. On the other hand, he was able to remove the veil that made him blind and considered his project “business as usual.” Let’s reproduce the curves for the base case (red) and the one considering the risk events (blue) below:
Although committed to July 7th, our PM knows it is probable to have a delay of some five months. In fact, his probability to achieve the goal in normal conditions was really slim. Why did this happen? It could be because:
- Someone determined that the project should be finished by July 7th and programmed it backward to the start.
- No contingencies were allowed, or they were unrealistic.
- Instead of using the
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