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Assessing Enterprise Project Risk

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

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

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Assessing Enterprise Project Risk

1. 1. ASSESSING ENTERPRISE PROJECT RISK Probability of Program Success P(s)
2. 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. 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. 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. 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. 6. EXAMPLE SCHEDULE RISK ANALYSIS
7. 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. 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. 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