The construction industry has witnessed many technological breakthroughs in the past 30 years, but the essence of project controls has actually changed very little. Fundamentally, it’s all about setting expectations and tracking outcomes across scope, cost and schedule. Organizations that do that well—in other words, deliver projects where expectations equal outcomes—are valued more than ever by capital project owners. More than anything, owners value certainty in the delivery of their projects, and that is especially true in uncertain economic times, when budgets tighten and capital investments are viewed with utmost scrutiny.

Owners will always be conscious of the capital that they have at stake, and greater risk and unpredictability almost always means increased costs, not to mention opportunity loss. It is common for owners to accept additional cost in order to remove risk and uncertainty, so the challenge facing construction tech is whether the vast amounts of data being collected could play that role instead. Can connected data, including data from past projects, lead to reduced risk on future projects? The short answer is yes.

Intelligent Insights, Realistic Execution

The complexity of large capital projects is not a new phenomenon, and to a large degree they are only as complex as the ability to manage them. Advances in project controls methodologies—and project controls technologies—have actually simplified the delivery of large projects. Planning and constructing the Panama Canal or Hoover Dam with today’s technologies would significantly reduce the cost and duration of those projects, with far more predictable outcomes. Innovation in the construction industry constantly pushes the industry forward, and as artificial intelligence and machine learning are becoming more prevalent, contractors become better equipped to capture the many details that contribute to construction project complexity and mine it for intelligent insights.

Construction projects are particularly well suited to take advantage of digitalization. Despite the traditional refrain that “every project is different,” when projects are broken down to their component parts, there is tremendous commonality across projects that may not be readily apparent. That creates the opportunity to take advantage of historical data from previous projects to be predictive on future projects. In so doing, contractors can reduce risk and potentially reduce cost and duration.

Big strides in machine learning will undoubtedly allow owner organizations, and the engineers and contractors they hire, to benefit from benchmarking and continual assessment of the “expectations equal outcomes” equation. With good historical data that connects scope, cost and schedule, and validates the planning assumptions that went into the left side of that equation vs. the right side (i.e., reality), more realistic plans and forecasts can be developed from the funding stage all the way through construction and commissioning. Most importantly, that historical data can be a valuable store of potential risks that a machine learning-based approach can surface as the project evolves.

Part of what has made this possible is the industry’s move away from point solutions to fully integrated project control platforms. Owners and contractors no longer need to face the daily struggle of importing data between different solutions that each have their own view of data granularity, data quality and data frequency. Trying to make a bunch of point solutions act like a connected platform is about as much fun as putting square pegs in round holes. Today’s integrated project controls platforms solve that problem, enabling project stakeholders to connect the dots and easily measure the cost and schedule impact of risks as they get surfaced.

When the key dimensions of project controls are integrated, the inevitable changes that occur along the way to scope or design can be assessed in a fully risk informed way. Likewise, revised budget or schedule targets can be played out through what-if scenarios, allowing stakeholders to rapidly understand the interplay between scope, cost and schedule—not only how they affect each other, but what risks should we be thinking about and how do they affect project certainty.

With connected data becoming more common across projects, there is a notable move away from siloes and specialists towards greater collaboration and transparency. An integrated project controls platform, coupled with good historical data and machine learning, can democratize project knowledge, essentially making everyone on the project team smarter.

Certainty as a winning factor

With capital project owners focused more than ever on certainty and predictability, tremendous opportunities for differentiation are being enabled by current technology trends. Those trends as they relate to construction organizations fall into three categories:

1. The move to connected platforms (and away from disconnected silos). The days of using a dozen or more disconnected solutions to plan and track a project’s scope, cost and schedule are moving to the rear-view mirror. Today’s integrated project management platforms can get that solution ecosystem down to just a few major systems, creating a naturally connected set of data that continually evolves as the project goes through its lifecycle.
2. The adoption of cloud-based solutions that encourage collaboration. The management of large capital projects often involves hundreds of roles across numerous stakeholders. Today’s cloud-based project controls platforms simplify the ability for those roles to share information in real time, creating fluid and efficient collaboration without relying on e-mails and constant importing and exporting of data.
3. The application of machine learning and the redefinition of risk assessment. Risk assessment used to mean a formal process at a defined point in time, often involving specialized risk consultants. While there is no doubt still value in that approach, machine learning is increasingly democratizing the risk assessment process. Today, integrated project controls platforms can already monitor a project’s cost and schedule to surface risks based on previous experience. In the future, as machine learning gets more advanced, and as organizations capture more historical project data to feed the algorithms, proactive identification of cost and schedule risk will only get better.

Industry-wide benefits

What does it mean to the construction industry to reduce risk and improve certainty in capital project delivery? For one, more realistic planning could significantly reduce rework and improve productivity, which is a well-documented $1.6 trillion opportunity for the industry. But it could also unlock a further $2 trillion in contingency funds that are sitting on the sidelines. Capital project owners typically set aside 20% or more of a project’s budget as contingency to cover risks.

Connected data—made possible by integrated project controls platforms—can drive more realistic, risk-informed plans and dramatically reduce the contingency associated with uncertainty. Reductions in contingency create new opportunities to fund additional capital projects, to the benefit of all stakeholders in the industry.