Where Risk Forms on Capital Projects: A Project Controls Perspective

By Adis Sehic, PhD — Project Controls, Group PMX

Risk in Capital Projects Is Visible in Data—But Not Created There

From a project controls standpoint, risk is usually identified through data. It shows up in schedule movement, cost variance, or changes in forecast.

But by the time those indicators are visible, the underlying drivers have often been in place for some time.

Project controls provide visibility into performance. They do not create the conditions that drive it.

When projects begin to drift, the data tends to reflect patterns such as:

• Incremental schedule slippage across reporting periods
• Forecast adjustments without a corresponding change in scope
• Repeated variance explanations tied to decision delays
• Inconsistent movement between cost and schedule

These are signals. They are not root causes.

Looking across projects, risk tends to form in the following three areas before it becomes visible in reporting.

Decision latency, diffused ownership, and misaligned data are the three structural risk drivers that form silently on capital projects—well before they appear in any report.

Three Structural Risk Drivers: Where Risk Forms Before It’s Visible

1. Decision Latency (Alignment Showing Up in the Schedule)

One of the most consistent indicators is a delay tied to decision-making. From a schedule perspective, this appears as:

• Activities waiting on input or approval
• Logic that extends due to unresolved direction
• Milestones shifting without a clear execution issue

In many cases, this is not a scheduling problem. It’s an alignment problem showing up in the schedule.

On one multi-site infrastructure program, a 15-day milestone slip repeated across three consecutive reporting periods. The variance explanation cited subcontractor coordination each time. The actual driver was an unresolved design decision that three different stakeholders believed belonged to someone else. The schedule reflected the delay accurately. But it took a deeper look at the decision chain to understand why.

2. Diffused Ownership (Structure Showing Up in Performance)

When accountability and decision rights are not clearly defined, it becomes evident in how issues are handled. Typical patterns include:

• Multiple stakeholders involved in a single decision
• Similar issues escalated through different paths
• Delays between identification and resolution

Even small issues begin to have a greater impact on schedule and cost than expected.

3. Misaligned or Lagging Data (Visibility Breaking Down)

Another common issue is fragmentation in how performance is tracked. This can include:

• Schedule and cost not fully aligned
• Different baselines referenced across stakeholders
• Reporting focused on past performance rather than forward indicators

At that point, the team is reacting to outcomes rather than proactively managing risk.

What Separates Early Detection from After-the-Fact Reporting

Early in a project, these patterns are easy to rationalize. Variances are within tolerance. Progress appears steady. Issues are resolved as they arise.

But project controls is less about individual data points and more about consistency across them. When the same patterns repeat, they usually point to a structural issue. The project controls professionals who catch these early are not looking at different data. They are looking at the same data differently. They are tracking whether decision timelines assumed in the schedule match actual decision timelines. They are watching whether the same variance explanation appears in consecutive periods. They are checking whether cost and schedule are moving together or diverging.

The difference is not access to better tools; it’s the discipline of reading patterns across reporting cycles rather than within them.

A Practical Diagnostic for Project Risk

From a project controls perspective, these four diagnostic questions can help identify whether the three structural risk drivers are present:

• Are decisions resolving within the timeframe assumed in the schedule? If not, decision latency is likely present. Compare the decision durations assumed in the baseline schedule to the actual elapsed time since each open item was identified.
• Is ownership of those decisions clearly defined? If not, diffused ownership is a risk. Multiple stakeholders co-owning a single decision is a leading indicator of escalation delays and inconsistent resolution.
• Do cost and schedule data align consistently? If they diverge, misaligned data is masking the true project state. Diverging cost and schedule trends are a sign that the team is managing two distinct performance measures.
• Are issues being addressed at their point of origin? If not, risk is compounding rather than resolving. Issues addressed downstream accumulate delay and cost at each handoff; the further from the source, the more difficult and expensive the fix.

If the answers vary, the focus is typically not on the data itself but on the conditions behind it.

Bottom Line

In capital projects, risk rarely originates in the data—it stems from the structure that underlies it. Project controls provide the earliest visibility into risk, but that visibility is only as effective as the alignment, decision-making, and accountability structures they measure.

When those structural conditions are clear—defined decision ownership, aligned data, and accountability at the right level—the data becomes more reliable and actionable. When they are not, the data reflects the problem but cannot resolve it.

That gap between what the data shows and what the project does about it is where the project controls function earns its value: not by producing reports, but by linking the signals in those reports to the decisions and structures that produced them.

Author

Adis Sehic, PhD, is an Associate Director at Group PMX and a recognized expert in project controls for large-scale capital programs. Over a 20-year career, he has led scheduling, risk analysis, and earned value management on some of the most complex programs in the country—including the LaGuardia Airport Redevelopment, the NYC Borough-Based Jails Program, and the Jacob K. Javits Convention Center Expansion. He holds a PhD in Construction Management from NYU Tandon School of Engineering, where he continues to teach as an Adjunct Professor.