How We Built Schedule Intelligence for EPC

The Core Problem

EPC projects are notoriously difficult to forecast. A typical greenfield project contains 10,000 to 50,000 activities spanning 2–5 years, with complex dependency chains that make traditional Critical Path Method (CPM) analysis insufficient for predicting real-world outcomes.

Planners spend countless hours manually updating schedules, but the data remains largely static and disconnected from the reality on the ground. When thousands of activities shift due to supply chain issues or engineering changes, understanding the cascading impact becomes a guessing game rather than an exact science.

New-Generation Data Parsing for Schedules

We started with a simple, ambitious question: If we had access to hundreds of completed EPC schedules, could we learn the hidden patterns that predict delays before they happen?

To answer this, we developed a proprietary parsing engine capable of digesting over 500 Primavera P6 schedules from completed EPC projects. Over 18 months, we normalized data across the oil & gas, petrochemical, and infrastructure sectors. Each historical schedule contributed:

• Activity-level metadata (original durations, dependencies, lag, resource assignments)
• Version-over-version progression profiles (how did the planned start diverge from the actual start?)
• DCMA 14-point compliance scores captured at every weekly snapshot
• Ground-truth project outcomes (final delay duration, cost overrun percentage, rework ratios)

Establishing the Gold Standard for Feature Engineering

Raw schedule data isn't directly useful for Machine Learning. A schedule with 50,000 lines requires deep feature engineering to extract the recognizable "shape" of the project. We transformed raw P6 exports into a gold standard dataset containing engineered features such as:

• Logic Density: The ratio of relationships to activities. Healthy schedules typically maintain a 1.5–2.0x ratio. Dropping below this indicates missing logic.
• Float Distribution: The mathematical distribution of total float values across all activities, identifying artificially suppressed or inflated float.
• Critical Path Stability: A measure of how often the critical path shifts between versions. High volatility usually precedes major delays.
• Resource Loading Profile: The peak-to-average resource utilization ratio, flagging impossible resource bottlenecks.
• WBS Depth Balance: Analyzing whether work packages are evenly distributed or heavily skewed, highlighting planning blind spots.

Interactive Progress and Risk Scoring

By training a gradient-boosted ensemble model on these features—with the target variable being the delta between the planned and actual finish dates—we created an AI that understands project physics.

We brought this to life through an interactive dashboard that doesn't just show static bars, but continuously scores project health. Our model achieves:

• 85% accuracy at predicting whether a specific project milestone will finish late.
• ±12% error margin on the magnitude of the delay (predicting exactly how many months late).
• Top 3 risk factors correctly identified for 91% of historical delays.

Real-Time Status and The Future of Planning

We are moving away from autopsy-style reporting and stepping into predictive status monitoring.

Instead of waiting for the end of the month to realize a project is slipping, Konnect xD integrates these ML predictions directly into the Planning workspace. Every time a new schedule version is uploaded, the AI orchestrator instantly evaluates it, assigning a real-time risk score.

Planning teams no longer have to manually dig through 50,000 lines to find the critical issues. The system automatically surfaces the hidden risks, empowering EPCs to course-correct weeks—or even months—before the delay materializes.