When we undertake risk analyses, we are subject to our curses and nightmares. I would like to highlight one of them: the moving steep mountain! In general, our projects (at least the ones I’ve been working on) have some characteristics:
In the light of everything I listed, what do you (usually) do? You compress the schedule! You start doing crashing and fast tracking like crazy. And if you do a schedule risk analysis, you’ll see that the probability of meeting the dates tend to be very low.
In addition, you are a victim (by your own doing!) of the merge bias! This was detailed by Mr. Hulett in his book “Practical Schedule Risks Analysis” (Gower, 2009). It happens when you have a lot of parallel paths that meet in a given task of your schedule.
Suppose you have three tasks that take 5 days each in series and you “fast track” them into three tasks (of six days each) in parallel. Suppose uncertainty is a triangular distribution with the lower point at 70% of the base value and the upper point at 150% of the same value. The most likely value is the base value. When you simulate both cases, you end up with something like this:
This simulation was done using @RISK. We can see that the probability of having a value lower than the planned one is over 25 percent for the original (series) project, whereas the “fast tracked” one (parallel) has a little over 5 percent for the same situation. The parallel paths hold a larger chance of failure, and the waterfall can accommodate a larger task with a shorter one in sequence.
When we don’t consider the risk events in the simulation and we use small ranges on the variation of tasks, we end up with a very unlikely and steep distribution. That’s when the unfeasible schedule takes its toll: when the inevitable reality happens, the risks start occurring and the milestones are missed, and our planning becomes impossible.
But never fear! The management has a solution for that as well! You shift the schedule and move the mountain a little to the right. And that small probability still remains, but it is less and less credible. Eventually the project will be completed, but what is risk management doing to bring value to the table? And the answer is… NOTHING!
It would be much better to have a wide distribution considering events and broader dispersions, which we could slice into different regions and analyze for determinant factors. See below the comparison between the “moving mountains” and the “big hill”.
Let us go for the big hill, then! Let us embed the events in our analyses. Let us shed some light and free ourselves from the curse of the moving mountain and the habits that make management look like zombies.
PS: This post was inspired, of course, by Halloween but, ironically enough, came to life a bit too late! Thank you for reading! Looking forward for your feedback!
Recently I posted a poll right here in projectmanagement.com (here) concerning how you prepare a schedule to undertake a schedule risks analysis.
My idea was to understand how you out there see the question of getting a schedule ready for the simulation exercise. I gave you five possible answers:
My answer was number four, “I reduce tasks AND check the links. 17% of the respondents (139 in total) were with me on this. Now let me explain why.
In my case, I usually start with the schedule we use to monitor the progress. It tends to be quite detailed, and have a lot of information that we use to control progress, like issuing reports, preparing for meetings, doing governance, etc. All this tasks go away. Some procurement packages, for instance, have fourty steps and others have ten. I try to harmonize this, so the tasks have some similarity. This reduces a lot of work.
And of course I check the links between tasks, which is a simulation killer, one of the favorite GIGO drivers and a strong sponsor to terrible decision making. With this done, I move on to doing stress analysis and other tests to see which tasks are worthy to model with a distribution. Going further forward, I start consulting experts and doing data crunching to know how exactly I am going to model that. At last, but not surely least, I add events and their mitigation. You can check my series of articles on Qualitative and Quantitative Risk Analyses Integration, starting with this one.
Moving back to our poll, I always thought my answer would win by a landslide, but I understand all the other answers and I will develop a rationale for them, if you allow me:
Anyway, thank you for responding to my poll, thank you for reading, and please post comments whether you agree or not with what I said. See you all next time!