In my previous blog posts on Sustainability (ProjectManagement.com’s theme for April), I addressed how to overcome barriers to achieving some level of sustained PM capability, both from pressures internal and external to the team. This week I thought I’d take a step back, and ask: why are there pressures working against advancement of Project Management capability in the first place, not to mention energy working against its sustainment in the event your organization even gets to an advanced PM state? I mean, think about it: are there pressures against more advanced engineering precision? More advanced talent recruitment? Better communications? When such pressures are discovered, they’re almost always immediately recognized as clear symptoms of some sort of business model pathology. But not Project Management, no siree. Engineers, HR recruiters, and even communications specialists can (and do) rail against certain aspects of an advanced PM capability, and can do so with relative impunity. Why is that?
Could one reason be that they have a point, that there are several aspects of what is considered to be a more advanced PM capability that are truly wastes of time and money? I sort-of touched on this last week, but laid much of the we’re-doing-this-to-ourselves blame on nefarious but unnamed guidance-generating organizations, and much of the blame does, indeed, belong with them. But in many cases our own organizations aren’t being forced to take this guidance at face value. We PM-types could, for example, perform an actual management science-based eval of the management information streams within our PMOs, and discontinue those that don’t truly add value.
The problem with performing an experiment on the efficacy of competing management information streams is that, even if the results point to a clear winner, the losing MIS stream’s advocates can always dispute the findings (usually with some sort of word salad), or ignore them altogether. However, bets are a little harder to ignore. So, I’m proposing a series of bets, starting with Earned Value versus risk management. Here’s how it could work.
For you PMO Directors out there, pick a medium-to-large project within your organization’s portfolio. Call a meeting with any Project Controls Specialist and your risk managers, and give them the following task: given a list of the Control Accounts (or even Work Packages) that are at least 20% complete within the project, forecast which tasks will overrun their original budgets or go past their original baseline dates, and which will not. Take the lists and compare them to each other, and archive the agreements (for overall accuracy rates). You will be left with a list that the EV analysts say will overrun/come in late, and a different set of tasks that the risk analysts predict will do the same. Then, when those tasks are actually complete, compare the late/overrun list with the predictions.
But, before we get all the way to comparing the predictive capabilities of risk management systems with basic Earned Value, I’d like to point out the data set each specialist will need to make their predictions. The EV specialist will need:
- Original beginning and ending dates,
- Original budget (total – it doesn’t even need to be time-phased),
- Cumulative actual costs,
- An estimate of each tasks’ percent complete,
- Today’s date (the date of the ask),
…and that’s it. Conversely, the risk management specialist will need:
- Original beginning and ending dates.
- Original budget (total – it doesn’t even need to be time-phased)
- Description of the original scope.
- A subject matter expert who can tell them:
- Possible ways (at least three) the work can unfold in a way that’s different from the way it’s described in the scope statement.
- The estimated cost of each alternative.
- The estimated duration of each alternative.
- The odds of the alternate scenario unfolding.
- Whether or not any of the alternate scenarios are mutually exclusive.
- A way of discerning if any of the alternate scenarios has taken place.
The comparative ease with which the EV specialist can collect the objective data needed to perform her analysis compared to the difficulty involved in gathering the risk manager’s almost completely subjective data should, all by itself, be a strong indicator as to which PM analysis technique is going to win this little wager. I mean, seriously, no SME in the world is going to be able to reliably provide a definitive list of the tasks’ scopes’ alternatives, along with anything resembling an accurate estimate of those alternatives’ duration and costs, let alone odds of occurring. And feeding all that subjective data into either a decision tree analysis structure, or a Monte Carlo simulation, will not overcome those problems.
However, since I know beforehand who is going to win this bet, I’ll be magnanimous. We’ll choose to not compare the data collection minutes needed by the EV specialists with the hours needed by the risk manager, and put the entire basis on its performance results. I’m extremely confident of the outcome; however, if a member of GTIM Nation is in a position to actually perform the experiment, I’d love to hear the real-world results.
So, I’ll return to the question in the title. If your PMO includes a significant risk management component, and that particular information stream is easily out-performed by a far simpler, cheaper method, it certainly raises the question: Does any PMO that includes a significant but irrelevant management component deserve to be sustained?