Introduction

Many ‘Request for Proposal’ (RFP) documents require that a Schedule Risk Analysis (SRA) must be performed on the submission. Performing an SRA is also promoted as a ‘Best Practice’ by most professionals in our industry.

But, what is the benefit of doing an SRA, and how much effort will it consume?

There are some obvious penalties for neglecting to do an SRA, such as bid disqualification, and future penalties if you don’t delivery on time and on budget.  So, it really comes down to how much effort we have to expend to realize tangible benefits from an SRA.

Risk and Uncertainty

All too often, SRA is lumped into Risk Management. I prefer the term Risk and Uncertainty Management, because they are really two different disciplines

Risk Management, using a Risk Register, is all about identifying and planning a response to random events that may, or may not, occur.

Uncertainty Management involves quantifying the impact that a lack of precise knowledge will have on the time estimates of future tasks.

Not all items in the risk register will impact the schedule. Even if they do, I prefer to use that knowledge to include risk mitigation steps in the schedule, which reduces the impact if the risk event should occur. Simply including a delay in the schedule to represent a risk that may or may not occur isn’t always useful, and the result is generally not a surprise. If the risk event occurs, then future tasks will be delayed by “x” amount of time.

However, the effect of uncertainty on time estimates is more insidious, and we regard this as the biggest reason why projects are delivered unexpectedly late.

A Story About Risk Management

A relative suffered a punctured tire while driving near my home. That’s a risk that we mitigate by carrying a spare wheel. I gallantly offered to drive over and change the wheel. However, after 5 minutes of searching, I was unable to locate the spare wheel. Fortunately, they had mitigated the risk of my lack of knowledge by carrying the owner’s manual in the glove box. Unfortunately, the manual confirmed that, when they purchased the car, they had selected an option for a built-in vacuum cleaner, and this deleted the spare wheel! So, what might have been a 30-minute impact quickly escalated!  Their trip (a.k.a. project) for the day was cancelled. There would have been no benefit modelling this risk in the schedule. There was a “lessons learned,” and they now carry a spare in the trunk at the expense of significant storage capacity, so this risk event has affected the scope of all future projects!

Happy Wife, Happy Life… (Estimate Uncertainty)

Everyone has been asked the question, “when will you be home?” My average drive from office to home (when we had an office!) was 30 minutes. It’s all too easy to answer, “30 minutes.” However, that’s just an average, so it’s unlikely that my drive home today will be exactly that length. So, what’s a better estimate that is less likely to annoy my wife?

My best-case estimate is 20 minutes (school holidays, good weather etc.) but my worst-case estimate is 60 minutes (rain, malfunctioning traffic lights, accident etc.).  It’s unlikely to be either extreme, and far more likely to be closer to the average.

So, let’s model that in a simulation. Best Case 20 minutes, Most Likely 30 minutes, Worst Case 60 minutes. We will use a Beta distribution to model the fact that the extreme best case/worst case times are unlikely to happen often.

Assuming I leave at 5pm, The simulation suggests I have a 41% chance of arriving home by 5:30pm. Depending on my appetite for risk (incurring the wife’ wrath) I can be 80% confident of arriving home by 5:37pm. Of course, I could play it safe and say 6pm, but then there’s a good chance I’ll arrive early and we could have eaten earlier, giving us more time for other tasks later (opportunity cost).

Two People Coming to Dinner…

If we have two people coming before dinner can be served, using the same journey profile, it gets more interesting.

So now we have just a 17% chance (previously 41%) that we will both be home for dinner at 5:30, and our 80% confidence time is now 5:41pm (previously 5:37pm). When a task has more than one predecessor (with uncertainty), it becomes less likely it will start on time. It gets worse with three predecessors etc.

This effect is called Merge Bias and is why forecasts from traditional critical path method (CPM) algorithms are inherently optimistic.

Capturing Uncertainty Doesn’t Have to Be Onerous

You can use generalizations for uncertainty based on experience.  If no prior experience is available, then applying a small amount of symmetrical uncertainty (i.e., just as likely to finish early as late) can give valuable insight into the effect of Merge Bias. For known high risk work, estimators can usually provide a range of estimate with little additional effort.

Summary

Modelling estimate uncertainty can dramatically improve the realism of future forecasts with little effort. Risk Management is still important, but most benefit can be achieved by using that information to plan for mitigating schedule impacts.

Learn more about Protecting Contract Deliverables with Schedule Risk Analysis and Schedule Margin with John Owen at our upcoming webinar! Register for this event here.