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As presented at the Project Control Summit, May 18–21, 2022

Tim Mather, COO at PMA Technologies

As COO of PMA Technologies, Tim leads the design and development team and the intellectual property efforts surrounding NetPoint. He is a certified Project Management Professional with extensive project management, software development, and marketing experience. Tim earned his bachelor’s degree from Michigan State University and pursued postgraduate study of project management at the University of Chicago.

Why Perform Schedule Simulation?

We are trying to predict the probability of project completion. This simulation produces a predicted project end date for each iteration. Over thousands of iterations, a pattern will emerge. When the pattern stabilizes, the simulation has come to convergence and the probability end dates can be charted (P dates).  P dates are expressed in the percentage probability of meeting that date based on the simulation. Many large capital projects require a certain P-value (P80 for instance would predict an 80 percent chance of project completion by a certain date) before the project will be authorized. A board of directors uses the Monte Carlo results to inform their decision about committing capital to a project. The more accurate the simulation results, the better informed the decision makers will be.  That is why our innovation is so important. CPM is overly optimistic and gives the decision makers bad information.

The Problem: CPM Optimism Bias in Simulation

Every CPM-based Monte Carlo simulation is based on a deterministic schedule model in which every activity starts on the early date. As we apply the Monte Carlo simulation to this model, each iteration will also start with every activity on the early date. The CPM forward and backward pass is a bulk operation that is insensible to intermediate changes to the network as the iteration progresses through the network.

The Solution: Floating and Pacing

In a GPM Monte Carlo simulation, activities can react to what has occurred earlier in their float chain. This means that the schedule model can more closely approximate what happens in a real-world project. GPM can model decision making based on progress on a chain during the simulation.

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The PMA team is fortunate to have project and program managers with a plethora of backgrounds that are leveraged for our clients to drive home successful projects. With his background in project risk management, PMA Senior Director, Francisco Cruz, helps break down how cost and schedule risk analysis can help to improve project certainty.

A primary goal of any cost or schedule risk analysis is to identify which risks impact the project the most and how to mitigate them cost-effectively. As a risk analyst, you need to present this information efficiently to the project team in a way that makes sense. As an owner or project executive, you need to understand the tradeoff between time saved and money spent on mitigation to make the best decisions.

Ranking by Probability and Impact

One of the traditional methods during a qualitative analysis involves estimating each risks’ approximate probability and impact. The two are multiplied and can be mapped from 0 to 100 to generate a score, or rating, allowing all risks to be compared to one another.

Fig 1: A typical probability/impact matrix with rating scores from 0 to 100

While this method is synonymous with a qualitative analysis, there are a handful of drawbacks. Imagine you’re working on the design and construction of a new facility. The team has identified a potential risk of a labor shortage during inspections, which could cause a delay to the elevator installation, with a high (80%) likelihood and a high (10-15 day) impact. Despite a high rating (65), it turns out the elevator installation activities aren’t even on the critical path! Without mapping the risk to any activities in the schedule, money could be spent unnecessarily mitigating it.

Consider another risk—a machine breakdown. The team agrees this has a high (80% likelihood) and a high (10-15 day) impact. This risk ends up with the same ranking as the labor shortage risk (65), even though the machine breakdown could repeat itself multiple times during the project! As a result, I prefer quantitative analysis for planning mitigation strategies.

Traditional Sensitivity Analysis

One of the most common outputs of quantitative analysis is the tornado chart, which shows the correlation between the existence of a risk and the project completion (or cost) using sensitivity analysis. The more highly correlated a risk is, the more it drives the project, and the higher up it shows on the chart.

Fig 2: A typical risk sensitivity tornado chart (calculated using Spearman Rank Order)

While the risk analyst may be comfortable with correlation coefficients, most project executives and stakeholders find them too abstract. What does it mean if a risk has a correlation coefficient of 0.8 or 80%? Or when compared to risk with a correlation coefficient of 0.7 or 70%?

Furthermore, correlation is not always clear-cut. There are at least two common methods for calculating correlation provided by many risk analysis software. The Pearson Method is typically suggested when the data are normally distributed, while the Spearman Rank Order method is typically suggested if the data are scattered or outliers exist. Depending on the method chosen, the correlation and even order of risks may change. This adds another level of complexity to interpreting and communicating the results.

Fig 3: Risk sensitivity tornado chart calculated using Pearson method. Only 4 risks demonstrate this method (the rest being statistically insignificant) and the order has changed.

Manual Risk Removal

Throughout my work, my clients have often asked: if risk X were to be mitigated, how many days might be saved? How many dollars are we talking about? So I resorted to a method first pioneered by Dr. David Hulett:

1. Run a stochastic simulation, store the overall completion date
2. Remove the top risk (according to the sensitivity) from the model
3. Re-run the simulation and store the delta between the two dates
4. Repeat steps 2-3 until no risks are left

This approach’s main advantage allows me to build a table in Excel to show the exact number of days or dollars that could be saved if a risk were completely mitigated.

Fig 4: Table in Excel showing output from manually removing risks and re-running the simulations

While my clients loved this chart, it was extremely time-consuming. For a model with 15 or 20 risks, the review could take an entire day.

Automation to the Rescue

Thanks to NetRisk, I can now generate the same results in a matter of minutes rather than days. In NetRisk, an initial simulation is performed to generate a traditional sensitivity tornado chart. The risk with the highest sensitivity is removed, and the simulation is re-run. Next, the risk with the now-highest sensitivity is removed, and the simulation is re-run and so-on-and-so-forth until all the risks have been removed. For example:

The result is a new tornado chart called Risk Priority.

Fig 5: Risk priority chart showing number of days that could potentially be saved for mitigating risks

While the chart looks similar, the key is on the x-axis: it shows the number of days that could be saved rather than an abstract correlation coefficient. Not only does this save time, but it also eliminates any ambiguity about which correlation method to use. I can customize which risks are considered, whether the project is schedule or cost-driven, and the desired confidence level (e.g., P80). Overall, I’m now able to provide results more efficiently and make sense, and decision-makers can understand the real tradeoffs and make the best decisions for their projects right after the risk assessment.

Regardless of whether you’re using risk priority, sensitivity analysis, or even a probability/impact matrix, one thing is always true: your results are only as good as the inputs that go into them. Many times, the project team might be over or underconfident in their responses. Performing a calibration assessment at the onset of the risk workshop proves to be a great success on many of my projects.

Francisco Cruz has substantial experience in risk management, cost control, estimating, scheduling, and value engineering. His experience includes passenger rail projects nationally, providing support from FEL through design, construction, C&Q, validation and regulatory stages. Francisco has performed cost and schedule risk analyses on more than 90 public and private projects totaling more than $15B. He serves on PMA’s FTA Project Management Oversight Contract (PMOC) where he facilitates FTA risk workshops, reviews Sponsor’s Risk & Contingency Management Plans, enhances Sponsor risk registers, and assisted the FTA with the re-write of its Oversight Procedure OP-40 covering Risk Management.

For more information about PMA Construction Management, Project Risk Management, and Construction Claims services, please contact info@pmaconsultants.com.

Coming soon: How to Calibrate Your Audience Before a Risk Workshop

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