Advisory Insights from
Mega EPC Projects
ADVISORY ARTICLE
By Tauseef Naz Arshad
Reading, UK
Executive Advisory Summary
Large Engineering, Procurement, and Construction (EPC) projects continue to experience schedule overruns despite the widespread use of sophisticated planning tools, detailed baseline schedules, and established project control frameworks. In practice, the challenge is rarely the absence of planning effort; rather, it is the limited ability of traditional forecasting methods to anticipate emerging schedule risk early enough to enable meaningful intervention.
Based on professional experience across large-scale EPC programs and comparative evaluation of traditional and data-driven forecasting approaches, this advisory article examines how artificial intelligence (AI)–based predictive scheduling performs when applied alongside conventional Critical Path Method (CPM) schedules and Earned Value Management (EVM). The focus is not on algorithms for their own sake, but on what the comparison reveals for planners, project directors, and executive decision-makers responsible for delivering complex projects under uncertainty.
The central advisory insight is that AI-based predictive forecasting should not be viewed as a replacement for established scheduling practices. Instead, when applied pragmatically, it can strengthen early-warning capability, improve schedule confidence, and support more proactive decision-making—particularly in volatile execution environments where deterministic forecasts and lagging indicators often fall short.
- Why Schedule Forecasting Remains a Persistent EPC Challenge
Schedule performance remains one of the most visible and consequential challenges in mega EPC projects. Delays frequently cascade into cost overruns, commercial disputes, strained stakeholder relationships, and loss of confidence at executive and client levels. Despite decades of advancement in planning tools and project controls methodologies, many large projects still struggle to produce reliable forward-looking forecasts once execution is underway.
In my experience, this is largely due to the inherent characteristics of mega EPC programs. These projects involve dense interdependencies between engineering deliverables, long-lead procurement packages, and construction sequencing that is highly sensitive to early-stage disruption. Minor slippages in engineering maturity or vendor delivery often propagate downstream in ways that are difficult to capture through deterministic logic alone. As execution progresses, productivity variability, interface risk, and external disruptions further complicate forecasting reliability.
Most EPC organizations rely on CPM schedules as the backbone of planning and control, supplemented by EVM metrics to monitor performance and project completion trends. These tools remain essential and contractually embedded, yet they are fundamentally retrospective in nature. Forecasts derived from static logic and cumulative performance often reflect what has already happened rather than what is about to occur. As a result, warning signals frequently emerge only after critical path erosion is already visible, leaving limited scope for corrective action.
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How to cite this article: Arshad, T. N. (2026). From Deterministic Schedules to AI Forecasting: Advisory Insights from Mega EPC Projects, PM World Journal, Vol. XV, Issue II, February. Available online at https://pmworldjournal.com/wp-content/uploads/2026/02/pmwj161-Feb2026-Arshad-from-deterministic-schedule-to-AI-forcasting.pdf
About the Author
Tauseef Naz Arshad
Reading- United Kingdom
Tauseef Naz Arshad (PMP, RMP, PMI-SP, ACP, PgMP, PfMP) is a senior project planning and project controls professional with over twenty years of experience delivering large-scale Engineering, Procurement, and Construction (EPC) programs across complex industrial and infrastructure environments. His work focuses on the practical integration of advanced analytics, artificial intelligence, and digital technologies into project scheduling, forecasting, and executive decision support. He has led planning and project controls functions on major EPC projects and continues to explore data-driven approaches that bridge established industry practices with emerging digital capabilities. Mr. Arshad can be contacted at tauseefnaz@gmail.com
