QNU

Schedule Risk Management

By Ursula Kuehn, PMP, EVPUQN and Associates

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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

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Getting Estimates

I need yourestimates bytomorrow.

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Let’s see...I haveto do this, and then do this.That should take me 2 days,

but I better say a weekbecause I always underestimate.

How We Tend To Estimate

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How does aweek sound?

How about Igive you twoweeks?

I’ll bet he’spadded it some, but

I’ll pad it a little more to be sure.

What Tends To Happen Next

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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

QNU Let’s Try An Example

• Changing an oil filter

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Polaris Submarine Missile Experiment for Estimating

Task Duration Simulation

0

5

10

15

20

25

30

35

Optimistic Most Likely Pessimistic

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The Mean and Standard Deviation

6

4(mean) *PERT

PMLO

6(std.dev.)* PERT

OP

* Program Evaluation and Review Technique

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What We Got From That Geeky Guy Named Gauss

-1σ

50% 84% 97.7% 99.8%16%2.3%0.2%

68+% Range

+1σ

Probability of Success

+2σ

+3σ

-1σ

-2σ

-3σ

95+% Range

99+% Range

Me

an

Using the normal curve to determine probability of success

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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

QNU PERT Example

Tasks Optimistic

Most Likel

y

Risks

Pessimistic

(O+4ML+P)6

P-O6

A 8.0 10.0 20.0

B 5.0 7.0 15.0

C 20.0 25.0 40.0

D 2.0 3.0 8.0

E 5.0 10.0 25.011.7

3.7

26.7

8.0

11.3

3.3

1.0

3.3

1.7

2.0

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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

)(Mean Mean Packages Work Critical(project)

)( Packages) Work (Critical(project) Dev. Std.2

Std.Dev.

QNU PERT Example

Tasks Optimistic

Most Likel

y

Risks

Pessimistic

(O+4ML+P)6

P-O6

((P-O)/6)2

A 8.0 10.0 20.0

B 5.0 7.0 15.0

C 20.0 25.0 40.0

D 2.0 3.0 8.0

E 5.0 10.0 25.0∑((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

29.061.455.0 Mean=Project

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Determining the Probability of Meeting a Due Date

-1σ

50% 84% 97.7% 99.8%16%2.3%0.2%

68+% Range

+1σ

Probability of Success

+2σ

+3σ

-1σ

-2σ

-3σ

95+% Range

99+% Range

Me

an

Using the normal curve to determine probability of success

61.445.2 50.6 56.0 66.8 72.2 77.6

Our Most Likely date of 55 has less than a 15% chance.

QNU …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?

QNU Merge Bias

Task I2 Days

Task H3 Days

Task E7 Days

Task G3 Days

Task B8 Days

Task D9 Days

Task A6 Days

QNU Statistical Sum

BP x APBAP

QNU Merge Bias Demonstration

Task I

Task H

Task E

Task G

Task B

Task D

Task A

50% Chance

50% Chance

25% Chance at the merge point

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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

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Example of Monte Carlo Results

Date: 3/31/2004 2:38:36 PMSamples: 350Unique ID: 1706Name: Systems Analysis and Design Contracts Awarded

Completion Std Deviation: 9.22 days95% Confidence Interval: 0.96 daysEach bar represents 3 days

Completion Date

Fre

quency

Cum

ula

tive P

robability

Tue 7/6/04Fri 6/11/04 Tue 8/17/04

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16 Completion Probability Table

Prob ProbDate Date0.05 Thu 6/17/040.10 Fri 6/18/040.15 Tue 6/22/040.20 Wed 6/23/040.25 Thu 6/24/040.30 Fri 6/25/040.35 Mon 6/28/040.40 Tue 6/29/040.45 Thu 7/1/040.50 Thu 7/1/04

0.55 Tue 7/6/040.60 Wed 7/7/040.65 Wed 7/7/040.70 Fri 7/9/040.75 Tue 7/13/040.80 Thu 7/15/040.85 Mon 7/19/040.90 Mon 7/26/040.95 Mon 8/2/041.00 Tue 8/17/04

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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

QNU 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