Schedule Risk Management

Schedule Risk Management By Ursula Kuehn, PMP, EVP UQN and Associates www.uqnandassociates.com

How We Tend To Develop a Schedule For Our Projects • Identify tasks • Get estimates of durations • Network tasks • Crash the schedule, if needed • Baseline the schedule • Execute the schedule • Do what we can to keep the schedule on track

I need your estimates by tomorrow. Getting Estimates

How We Tend To Estimate Let’s see...I have to do this, and then do this. That should take me 2 days, but I better say a week because I always underestimate.

I’ll bet he’s padded it some, but I’ll pad it a little more to be sure. How does a week sound? How about I give you two weeks? What Tends To Happen Next

I have so many tasks to do. I’ll start this task next Thursday. That gives me 2 days to finish it. I think I can finish it in that time. Parkinson’s Law

Let’s Try An Example • Changing an oil filter

Polaris Submarine Missile Experiment for Estimating Optimistic Pessimistic Most Likely

The Mean and Standard Deviation * Program Evaluation and Review Technique

What We Got From That Geeky Guy Named Gauss Mean Using the normal curve to determine probability of success -1σ +1σ -1σ 68+% Range +2σ -2σ 95+% Range -3σ +3σ 99+% Range 0.2% 2.3% 16% 50% 84% 97.7% 99.8% Probability of Success

Range Estimating • Ask for four (4) pieces of information when estimating • The “most likely” estimate, i.e., how long will it most likely take to do the work • The “optimistic” estimate, i.e., if everything goes perfectly how long will it take to do the work • Two or three things that could go wrong, i.e., risk identification • The “pessimistic” estimate, i.e., if these things happen, how long will it take to do the work

PERT Example 11.3 2.0 8.0 1.7 26.7 3.3 3.7 1.0 11.7 3.3

Determining the Probability of Meeting a Due Date using PERT • Uses the summation of events rule of statistics • Due to the “mutually exclusive” portion of this summation rule, PERT can only be performed on a single path of the schedule

PERT Example 11.3 2.0 4.0 8.0 1.7 2.9 26.7 3.3 11.0 3.7 1.0 1.0 11.7 3.3 11.0 Project 55.0 Mean= 61.4 ∑((p-o)/6)2= 29.0 5.4 SQRT(∑((p-o)/6)2)=

Mean Using the normal curve to determine probability of success -1σ +1σ -1σ 68+% Range +2σ -2σ 95+% Range -3σ +3σ 99+% Range 0.2% 2.3% 16% 50% 84% 97.7% 99.8% Probability of Success Determining the Probability of Meeting a Due Date 45.2 50.6 56.0 61.4 66.8 72.2 77.6 Our Most Likely date of 55 has less than a 15% chance.

…And That Is Just One Path • How many of you have only 5 tasks on your critical path? • How many of you have only one path through your schedule?

Merge Bias Task B 8 Days Task E 7 Days Task H 3 Days Task A 6 Days Task I 2 Days Task D 9 Days Task G 3 Days

Statistical Sum

Merge Bias Demonstration 50% Chance Task B Task E Task H 25% Chance at the merge point Task A Task I Task D Task G 50% Chance

Monte Carlo Simulation • Randomly generates durations based on optimistic, most likely, and pessimistic estimates of each individual work package • Runs the simulation of the entire project schedule a number of times (e.g., 1,000 times) • Computes the frequency data of the end dates • Determines probability based on frequency data curve

Example of Monte Carlo Results

Try Working With Two Project Plans • Most project management software tools allow for a number of different baselines in the same project file • To avoid Parkinson’s Law have one baseline with the “most likely” estimates, which will be the one used to direct the team member’s tasks • The second baseline will use the calculated “mean” estimates, which will be used to status the progress of the project

Conclusions • If we base our schedule on single point duration estimates, we’re not giving ourselves a chance to be successful • We should challenge our team members to their most likely estimates • Using risk identification, mitigate the risk of being unsuccessful by having a second baseline that has a higher probability of success

Schedule Risk Management