Schedule, Critical Path (CP), Milestone, Deterministic Value, 3-Point estimates, Monte Carlo Simulation, Correlation, Iteration, Cumulative Frequency Chart, Probabilistic Branching, Distribution, Triangular Distribution, Categories, Duration, Finish Date, Uncertainty, Skewness, Positively Skewed, Negatively Skewed.
A schedule risk analysis (SRA) uses a risk-based project schedule (derived from the master schedule) to apply statistical techniques to envisage a level of confidence in meeting a project’s completion date. This analysis focuses on uncertainty they affect the schedule overall and activities durations and completion date. SRA starts with reviewing the project schedule and performing schedule quality checks as specified by DCMA 14-points. A sequence of activities and activities relationship plays an important role to avoid “Garbage in and Garbage out”.
There can be several other parameters that can contribute to quality SRA. Like:
Perform quality checks on Schedule before Monte Carlo simulation as per DCMA 14-points may be a good choice for quality checks. Few points are given below
Schedule risk analysis (SRA) helps to analyze the uncertainty in the project schedule using Monte Carlo Simulation. Deterministic schedule duration and finish date don’t provide enough information to answer flowing questions:
SCR scope is:
List of activities are given below with duration and predecessors
Parameter Entry Table is created for 3-Point estimates collected after a brainstorming session with the Subject Matter Experts (SME). Collect data is also benchmarked with archives available for similar-sized projects within the organization. Chain-of-causes is a good approach to understand the various factors contributing to uncertainty in the activity. Another development is using the Casual Loop Diagram (CLD). It helps not only to identify the key variables but to draw a relationship between cause and effect. Another methodology is Mind mapping. Mind Mapping helping to analyze the problem by lining the different variables.
Parameter Entry Table is shown above with min. most likely and max. values. Most likely is the schedule actual duration. For this article, optimistic value is adjusted to 10% of the most likely value. While Pessimistic value is 50% of the most likely value. For 3 points estimates either use PERT or Triangular distribution. The triangular distribution is the best choice to have positively skewed data with wider distribution of data.
Run the Monte Carlo Simulation (MCS) for SRA. There is a 90% chance that the project will finish before June 18th, 2020. Surprisingly, there is only less than a 5% chance to complete the project on the target date. Why there are shoulders in the output graph? It is because the project calendar is set to five (5) working days. It is representing off days.
What if to simulate project risk analysis for a project schedule with seven (7) days working calendars? It depends on the project nature. Usually, construction projects, commissioning and outages are following seven (7) working days calendar.
In the case of seven (7) working days calendar the project target finish date is Feb 23, 2020. results are:
Again, there is less than a 5% chance to meet the target date. There is a 90% chance that the target date will be close to Mar 30, 2020.
Correlation is required for Schedule Risk Analysis to avoid merged biased because of parallel paths along with critical Path (CP) i.e. near Critical paths. During a simulation, these paths merged together resulting shorter project completion duration. A risk driver is an approach to deal with it. However, another approach is to correlate related activities. correlating related activities results in wider distribution. Keep in mind activities in parallel or in series also play an important role in simulation and results.
There is a 90% chance that the target date will be Mar 30, 2020. Almost seven (7) days of difference between SRA with activities correlated and without correlation.
Probabilistic branching is useful to analyze the scenarios within schedule during Scheduling Risk Analysis (SRA). For this project, there are two branches and executed simultaneously. The interesting part is that one Probabilistic branch lies in the Critical path (CP) while other path lies in a near-critical path.
Probabilistic branches are:
Results are negatively skewed. It is because of various reasons and one is having two branches in a schedule. Further analysis can be carried out like Sensitivity Analysis and Probabilistic Gantt chart to analyze the behavior of the activities.
SRA is good for analyzing the project schedule statistically. Prior quality checks help to prepare schedule for a SRA. Running MCS, again and again, helps to analyze the various scenarios and compare results.