1. Q N U Schedule Risk Management By Ursula Kuehn, PMP, EVP UQN and Associates

2. Q N U 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

3. Q N U Getting Estimates I need your estimates by tomorrow.

4. Q N U 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. How We Tend To Estimate

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

6. Q N U 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

7. Q N U Let’s Try An Example Changing an oil filter

8. Q N U Polaris Submarine Missile Experiment for Estimating Optimistic Most Likely Pessimistic

9. Q N U The Mean and Standard Deviation * Program Evaluation and Review Technique

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

11. Q N U Range Estimating Using PERT 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

12. Q N U PERT Example TasksOptimisticMost Likely RisksPessimistic(O+4ML+P) 6 P-O 6 A8.010.020.0 B5.07.015.0 C20.025.040.0 D2.03.08.0 E5.010.025.0 11.7 3.7 26.7 8.0 11.3 3.3 1.0 3.3 1.7 2.0

13. Q N U 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

14. Q N U PERT Example TasksOptimisticMost Likely RisksPessimistic(O+4ML+P) 6 P-O 6 ((P-O)/6) 2 A8.010.020.0 B5.07.015.0 C20.025.040.0 D2.03.08.0 E5.010.025.0 ∑((p-o)/6) 2 = SQRT(∑((p-o)/6) 2 )= 5.4 11.7 3.7 26.7 8.0 11.3 3.3 1.0 3.3 1.7 2.0 11.0 1.0 11.0 2.9 4.0 29.061.455.0Mean=Project

15. Q N U Determining the Probability of Meeting a Due Date -1σ 50%84%97.7%99.8%16%2.3%0.2% 68+% Range +1σ+1σ Probability of Success +2σ+2σ +3σ+3σ -1σ-1σ -2σ -3σ-3σ 95+% Range 99+% Range Mean Using the normal curve to determine probability of success 61.445.250.656.066.872.277.6 Our Most Likely date of 55 has less than a 15% chance.

16. Q N U …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?

17. Q N U Merge Bias Task I 2 Days Task H 3 Days Task E 7 Days Task G 3 Days Task B 8 Days Task D 9 Days Task A 6 Days

18. Q N U Statistical Sum

19. Q N U Merge Bias Demonstration Task I Task H Task E Task G Task B Task D Task A 50% Chance 25% Chance at the merge point

20. Q N U 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

21. Q N U Example of Monte Carlo Results

22. Q N U 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

23. Q N U 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