The Probability of Success P(s) can be assessed with 4 simple metrics. Requirements, Resources, Planning, and Execution

1. ASSESSING ENTERPRISE
PROJECT RISK
Probability of Program Success P(s)

2. Assessing Schedule Risk is One Step
2
Probability of Success P(s)
Assesses a project’s
performance using 4 simple
metrics.
It is the Planning metric that
can be assessed using two
methods deployed by Lewis &
Fowler
 Monte Carlo Simulation
 Schedule Health

3. 3
The Data Elements of a Project are Random Variables
Cost, Schedule, Technical Performance and Risk
 Each is drawn from a
probability
distribution.
 The shape of this
distribution has direct
impact on the
confidence of the
outcome.
 Modeling this
probability
distributions and
their outcomes is
what a Monte Carlo
tool does.

4. The Schedule Assessment Process
 Topology
 Task with no successors
 Tasks with no
predecessors
 Constraints
 Must Finish On
 Finish No Later Than
 Must Start On
 Start No Later Than
 Margin analysis
 Task duration
probability
distributions
 Confidence of
completing on or
before a planned
date
Schedule Health Schedule Risk

5. Every Manager Should Know…
5
 When will this project finish?
 Is the project’s schedule realistic and achievable?
 What will it cost when we are done?
 How is the project performing against its plan?
 What deliverables are slipping?
 How are we going to get the deliverables back on schedule?
 What does past performance say about the future performance?
 What is the impact on the schedule of any change requests?
 Is there enough schedule slack and reserve to cover the risk?
 Is there enough management reserve to cover any overrun?
 Where is the risk in the schedule?
 How can we get the work done sooner to reduce risk?
 How can we recover from any foreseen delays?
 How can we work around a problem?

6. EXAMPLE SCHEDULE RISK
ANALYSIS

7. 7
Monte Carlo Risk analysis starts with a credible
schedule and defines the probabilistic behavior of
each activities and how it drives the deliverables
The Risk+ tool sets the upper and lower bounds of the possible durations

8. 8
Date: 2/25/2009 3:34:12 PM
Samples: 300
Unique ID: 30
Name: Final Testing
Completion Std Deviation: 5.51d
95% Confidence Interval: 0.62d
Each bar represents 2d
Completion Date
Frequency
CumulativeProbability
Tue 2/11/03Mon 1/20/03 Mon 3/3/03
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 Date
0.05 Wed 1/29/03
0.10 Fri 1/31/03
0.15 Tue 2/4/03
0.20 Wed 2/5/03
0.25 Wed 2/5/03
0.30 Thu 2/6/03
0.35 Fri 2/7/03
0.40 Mon 2/10/03
0.45 Mon 2/10/03
0.50 Tue 2/11/03
0.55 Wed 2/12/03
0.60 Thu 2/13/03
0.65 Fri 2/14/03
0.70 Fri 2/14/03
0.75 Mon 2/17/03
0.80 Tue 2/18/03
0.85 Wed 2/19/03
0.90 Thu 2/20/03
0.95 Tue 2/25/03
1.00 Mon 3/3/03
The output of Risk+ is a Probability Distribution Function and a
Cumulative Distribution of all the possible dates that “watched”
activity could take.
This shows the Confidence in the target date – 2/10/3
It shows 40% chance of completely on or before 2/10/03

9. Beneficial Outcomes
 A confidence metric that the project will complete
on or before the planned date
 Measures of the integrity of the activity network
 Overly constrained plan
 Activities with no dependencies
 Visibility into the Probability of Success from a
schedule point of view