Nostradamus for Projects (Part II): Using Predictability Metrics in Project Benchmarking

In Part I of Nostradamus for Projects, we covered how improving predictability in the construction industry can mitigate cost and schedule overruns, ultimately having a huge impact on returns and margins.

Project predictability is simply knowing the outcome variance of a project as early as possible. It answers the question, “how early in the project lifecycle is the outcome first known?” Improving predictability requires both accurate AND timely forecasts.

We also looked at some of the causes of low predictability in projects that lead to cost and schedule overruns. The Construction Industry Institute’s Research Team (RT) 291 studied the effects of certain variables on project outcomes and found that human behavior is the single most important factor influencing project overruns.

Further confirming CII’s findings is professor Bent Flyvbjerg, an expert in the field of project predictability. In his recent article “Five things you should know about cost overrun,” Professor Flyvbjerg concludes:

“The root cause of cost overrun, according to behavioral science, is the well-documented fact that planners and managers keep underestimating scope changes and complexity in project after project.”

Then, we looked at how utilizing technology — empowering and powered by digital transformation — can help organizations achieve high predictability. Enterprise Project Performance (EPP) software drives the timeliness and accuracy of forecasts, promotes good project management practices, and helps shape human behavior. This results in an organizational culture of transparency, efficiency, and predictability.

Now, let’s take a closer look at utilizing predictability metrics within an Enterprise Project Performance solution. You will see how you can go beyond traditional project benchmarking to improve project analysis, and ultimately performance.

Traditional Benchmarking

According to the Project Management Institute (PMI), benchmarking is setting goals using objective, external standards, and learning from others. Benchmarking is important because it helps you identify best practices and provides a way to measure and improve internally relative to other project teams, regions, departments, business units etc. You can also use it externally to measure your company relative to others. Benchmarking has its challenges when it comes to projects in the engineering and construction industry. As PMI explains, these are most notably:

• Projects have different goals, objectives, and priorities, so it is hard to universally apply a single metric.
• Maintaining benchmarking processes can be difficult as most organizations executing projects have limited resources do not have expertise in benchmarking.
• Obtaining reliable data from organizations in the industry can be difficult, and most organizations are reluctant to share performance data.

Traditional benchmarking has its benefits and provides good direction and solid principles to drive improvement. But it is mostly an external practice. By collecting project data and applying benchmarking principles to predictability measures through the use of the right EPP software, you can turn benchmarking into both an internal and external practice. This will take your project performance to the next level while overcoming some of the challenges noted above.

Internal Project Benchmarking Using Predictability

Enterprise Project Performance software platforms serve as a valuable repository of historical project data. You can easily apply benchmarking practices internally to measure current projects against past performance.

Predictability metrics are normalized in a way that you can compare any two projects regardless of size or type. That means that once you measure predictability using an EPP software platform, you can aggregate predictability data and compare across virtually any variable. For example, business units, regions, departments, project teams, project size, and team leadership.

You can look at project predictability across your entire organization over time, and drill down to identify root causes of low predictability that need to be addressed. You can also implement best practices promoting high predictability across the enterprise.

An EPP platform can help automate benchmarking practices, meaning you can utilize predictability-based project benchmarking despite limited resources and expertise. As Mark White, SVP for Project Performance at Hexagon PPM recently explained in his blog post on project cost overruns:

“Many organizations lack comprehensive cost databases because they allow projects to be controlled in Excel spreadsheets or limited, inflexible database applications. Controlling every project in an adaptable Enterprise Project Performance (EPP) platform that incorporates key attributes and measurements supports semi-automated collation of reference data, reusable on future project estimates within the same platform. Timeliness and outcome metrics can also be captured automatically, supporting correlation with systemic issues that must be addressed prior to commencing the next project.”

Additionally, the benchmarking based on predictability performance provides the benefit of self-reinforcement. As project teams are measured and incentivized based on predictability, organizational culture and behavior shifts to emphasize trust, transparency, alignment, and timely disclosure of project performance information (Back and Grau).

External Project Benchmarking Using Predictability

As PMI points out, external benchmarking is challenging due to organizations’ reluctance to share their project data publicly. But what if you were able to aggregate vast amounts of project data anonymously across industries, so you could benchmark against industry averages?

That’s exactly what Enterprise Project Performance platforms such as EcoSys have the potential to do. EcoSys can facilitate external benchmarking based on predictability metrics. This provides a unique opportunity benchmark against an incredible volume of project data, as Mark White suggests:

“Consider this: the industry leading Enterprise Project Performance technology provider has customers with in excess of 50,000 projects in one database. Across all of its customers there exists more than one million projects. Once collated via automated cloud orchestration, it will be the largest dataset of reference projects in the history of mankind. The resulting findings will be so statistically significant as to border on absolute!”

Think about being able to benchmark the predictability of your projects against 1 million projects in the industry. And then aid in forecasting by learning from the typical course of countless similar projects. How valuable would that knowledge be? Such a dataset would be truly groundbreaking in terms of identifying best practices, developing models, and measuring and validating performance.

Next Steps: Enable Predictability and Improve Organizational Culture

To explore predictability and how it can change organizational culture and behaviors by restructuring incentives around predictability, read this article.

For more information, about how you can enable predictability within your organization, view this webinar “Pillars of Project Predictability.”