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Schedule Risk Analysis

"Schedule Risk Analysis is a technique which recognizes this uncertainty by replacing the deterministic duration for each task by a distribution representing the range of likely durations. Analytic methods exist to process probability distributions in simple cases, but project networks are typically too complicated for these to be applicable."

Schedule Risk Analysis: What Is It and Why Do It?
tensix.com/2012/02/schedule-risk-analysis-what-is-it-and-why-do-it/

The “Nominal” Task Durations represent the Single-Point Estimates used to create IMS Critical Path Schedules.

Most schedule planners will use these Single Point Estimates to construct the IMS for a Project, but many realize that for several of the Riskier Tasks there is actually a Range of Durations, or in other words, a Distribution of potential Durations – a Task Duration Probability Density Function. If you could Model these Distributions and somehow embed them into the IMS you could run a Simulation to observe their effects on the Schedule.

Well, this is precisely what an SRA Tool enables – and Chrono™ has the most Innovative SRA Tool available for Developing and Managing Achievable Project Plans – a more detailed rationale is provided below.

Here is a one of the Key Graphical Outputs:

Key Graphical Output

Developing Achievable Project Plans

 

We (Project Managers, Program Managers, Portfolio Managers, Team Leads, Subject Matter Experts, and Functional Managers of Project Team Members) tend to develop product development plans by estimating specific (or deterministic) timelines for completing tasks, then linking them together to generate the project schedule. We commit to these timelines, and always expect that a good team of people will deliver as planned.

Now when the stakes increase, and plan execution becomes imperative (as indeed happens more and more, for our competitive world is always trying to “Do More with Less” to gain Business Advantages over others), do we always come through? Not as much as we would typically like. So, lets investigate some of the usual culprits.

We call the Deterministic Single-Point Task Duration Values the “Nominal” Task Durations, and when they are networked together using an IMS (Integrated Master Scheduling) Tool, they comprise the “Critical Path” of the schedule and give us an expected Project Completion Timeline and Date. This typically turns into the Team’s Commitment, and as we will soon discuss, normally these schedules can have a very low likelihood of being met (especially if the Project is complex and risky) – not due to the people, but due to the inherent unreliability of most Single-Point Estimates, especially those will little or no analogous back-up data.

Task durations can vary due to many influences. The Best-Case Durations are influenced by Opportunities and the Worst-Case (or High Confidence) Durations are influenced by Risks. In today’s world where we are constantly trying to “Do More with Less” most of our plans are risky and our “Nominal” Estimates are no exception. If we estimate the Nominal, Best-Case and High-Confidence Durations to complete a Task, we can create a Triangular Distribution for all possible Task Durations.  To do this we plot the Best-Case Duration on the X-axis (Time) with “0” Y-axis value, the Nominal Duration has a positive Y-axis value and the High-Confidence Duration has a “0” Y-axis value.  Connecting the three values creates a triangle – the Task Duration Probability Density Function.  An example is shown below.

Example overlay of the Cumulative Probability Function of % Confidence 'S' Curve
Example overlay of the Cumulative Probability Function of % Confidence 'S' Curve

Unless we are accomplished “sand-baggers” and/or our Organization is not striving to “Do More with Less”, our estimates usually have less Opportunity (Nominal Duration minus Best-Case Duration) than Risk (High-Confidence Duration minus Nominal Duration). This means our Probability Density Functions are inherently skewed in the wrong direction – i.e., the Opportunity Area of the triangle is less than the Risk Area. Dividing the Opportunity Area by the Risk Area is the % Confidence for completing that Task on or before the Nominal Duration estimate, and it is usually less than 50%. When we cascade this effect through the schedule network the resulting Nominal end-date (per the Critical Path) is typically far less than 50% Confidencefor real complex Projects it is likely to be less than 1% Confidence.

There is another phenomenon that skews (in a negative way) Project Schedules as well – it is commonly referred to as “Merge Bias”. This relates to the merging of two or more Schedule Paths into a common subsequent Task Start Milestone. The closer the paths are to being to the Critical Path the more pronounced the effect on the % Confidence of the Completion Milestone.

The good news is that from these Triangular Task Duration Distributions we can actually derive the Completion Milestone Probability Distribution, or ‘S’ Curve (% Confidence as a function of Completion Date) using an SRA Modelling and Simulation Tool. Suffice it to say, using an SRA Tool effectively can lead to determination of an Achievable (>50% Confidence) Project Schedule Plan, by enabling the Team to perform “What-ifs” to figure out how to most effectively bring down the Likelihood and/or Impact of the significant Project Schedule Risk Contributors. This clearly improves the Likelihood of Project Success and provides the Organization’s Management Team with Overall Project Schedule Risk Assessments in terms that they are familiar with -- % Confidence.

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