Critical Path Method (CPM) has long been treated as the primary control mechanism for schedule management in engineering projects. In relatively simple and stable systems, this assumption can hold. However, in complex engineering programs spanning FEED and EPC stages, reliance on a single critical path often provides false confidence rather than meaningful early warning.
In large-scale engineering systems, delay rarely emerges from one dominant path. Instead, it develops through the interaction of multiple near-critical paths, productivity variability, design churn, interface misalignment, and compounding uncertainty. As execution conditions evolve, the identity of the critical path itself becomes unstable. By the time a delay is clearly visible on the nominal critical path, recovery options are often already constrained.
From a systems engineering perspective, this behavior is not surprising. Complex projects behave as dynamic networks rather than linear chains. Sensitivity is distributed across the schedule logic, and small perturbations in multiple areas can collectively generate significant delay without any single activity appearing dominant in isolation. Treating critical path as a static control metric therefore encourages retrospective explanation rather than proactive intervention. Reporting becomes focused on defending baseline logic instead of identifying emerging delivery risk.
The practical alternative is not to abandon planning discipline, but to shift the focus of control from a single deterministic path to the behavior of the schedule system as a whole. Effective control in complex engineering programs comes from monitoring path volatility, near-critical convergence, and the propagation of risk across interfaces. This requires understanding how uncertainty accumulates across multiple paths and how small changes in productivity, design maturity, or handovers interact over time.
AI-enabled analytics can support this shift by learning from historical and live schedule data to identify emerging risk patterns that are not visible through deterministic logic alone. Rather than predicting a single completion date, AI can highlight clusters of activities where volatility is increasing, signal which near-critical paths are becoming dominant, and provide early warning when multiple small deviations are likely to combine into a material delay. In this way, forecasting moves from date prediction to risk anticipation.
Used responsibly, AI does not replace engineering judgement or governance. It augments them by improving the timing and quality of information available to decision-makers. This allows engineering leaders to intervene earlier, test recovery options while they are still viable, and govern uncertainty proactively rather than explaining variance after it has already occurred.
Takeaway
Critical path remains a useful reference, but it is a weak primary control metric in complex engineering systems. Effective delivery assurance requires moving beyond static path logic toward an understanding of schedule behavior, volatility, and risk interaction. Engineering leaders who adopt this mindset, supported by AI-enabled predictive insight, are better positioned to act early, preserve optionality, and deliver complex engineering programs with greater reliability and confidence.
Author bio
Tauseef Naz Arshad is a senior engineering and project controls leader with over twenty years of experience delivering complex engineering programs across FEED and EPC stages. Based in the United Kingdom, he specializes in applied AI-enabled predictive scheduling, risk forecasting, and delivery assurance for large-scale engineering and infrastructure projects. He is a member of the Institution of Engineering and Technology, the Association for Project Management, and the Project Management Institute.