for Mega FEED and EPC Programs
FEATURED PAPER
By Tauseef Naz Arshad
Reading, UK
Abstract
Mega Engineering, Procurement, and Construction (EPC) programs operate in execution environments characterized by high interface density, evolving engineering maturity, long-lead procurement exposure, and significant schedule uncertainty during the transition from Front-End Engineering Design (FEED) to detailed delivery phases. Although scheduling dashboards are widely used to support project control and management oversight, most remain descriptive in nature, relying on periodic updates from deterministic planning systems and lagging performance indicators that provide limited visibility into emerging execution risks.
This article presents a Digital Twin–enabled framework for project scheduling dashboards designed specifically for FEED and EPC mega programs. The proposed approach establishes a continuously synchronized virtual execution environment that integrates scheduling logic with engineering deliverable maturity, procurement readiness signals, construction constraints, and risk intelligence to support predictive schedule governance. By embedding engineering readiness forecasting, critical path volatility monitoring, and risk-adjusted milestone confidence modelling within dashboard architectures, the framework enables earlier identification of delivery threats and improves decision responsiveness across program leadership levels.
The paper further introduces a structured dashboard maturity model illustrating the evolution from descriptive reporting tools to scenario-driven decision twins capable of supporting proactive mitigation planning and execution strategy optimization. Practical implementation considerations are discussed to support adoption within large EPC organizations operating complex, multi-contractor delivery environments.
The findings demonstrate that Digital Twin–enabled scheduling dashboards represent a significant advancement over traditional project control approaches by improving milestone predictability, strengthening interface alignment between engineering and construction phases, and enabling data-driven decision-making throughout the FEED-to-EPC transition lifecycle. Collectively, these capabilities position Digital Twin scheduling environments as a foundational component of next-generation project controls practice in mega engineering programs.
- Introduction
Mega Engineering, Procurement, and Construction (EPC) programs operate within highly complex execution environments characterized by dense technical interdependencies, geographically distributed engineering centers, long-lead procurement exposure, and evolving construction constraints. In such settings, project scheduling dashboards play a central role in supporting coordination, monitoring delivery performance, and informing management decision-making across multiple organizational layers.
Despite their widespread use, traditional scheduling dashboards remain largely descriptive in nature. They typically rely on periodic updates extracted from deterministic schedule models and present milestone tracking, S-curve performance indicators, and critical path status as static reporting artefacts rather than dynamic decision-support tools. As a result, emerging execution risks are often detected only after float erosion has already occurred and mitigation options have become limited.
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How to cite this article: Arshad, T. N. (2026). Digital Twin–Enabled Project Scheduling and Decision Dashboards for Mega FEED and EPC Programs, PM World Journal, Vol. XV, Issue V, May. Available online at https://pmworldjournal.com/wp-content/uploads/2026/05/pmwj164-May2026-Arshad-Digital-Twin-Applications-for-Mega-FEED-EPC-Projects.pdf
About the Author
Tauseef Naz Arshad
Reading, UK
Tauseef Naz Arshad, PMI-SP, PMP, PMI-RMP, PMI-ACP, PfMP is a senior project controls and engineering innovation leader with over twenty years of experience delivering complex mega-scale EPC programs across the energy and infrastructure sectors. He currently works in the UK, as Planning Manager for Wood Engineering Ltd, specialising in AI-augmented predictive scheduling, earned value analytics, and digital-twin-based project control systems. His recent work focuses on integrating artificial intelligence with traditional EVM and critical path methods to improve forecast reliability in complex delivery environments. Tauseef regularly publishes advisory and applied research articles on advanced project controls and contributes to the professional discourse on data-driven project management innovation. Tauseef can be contacted at tauseefnaz@gmail.com
