An Integrated Delivery Roadmap Framework for Civil Engineering Projects: Bridging Pmbok, Lean, Bim, Agile, and Evm
Year: 2026
Type: article
Abstract: The construction industry continues to experience persistent challenges related to cost overruns, schedule delays, low productivity, and fragmented project delivery processes. Despite advances in project management methodologies and digital technologies, the lack of integration across project lifecycle phases remains a critical barrier to performance improvement. This study proposes an integrated Delivery Roadmap framework for civil engineering projects, designed to systematically align strategic planning and project execution across six lifecycle phases: initiation, planning and design, procurement, construction execution, monitoring and control, and post-construction. The framework synthesizes five major paradigms—PMBOK, Lean Construction (including the Last Planner System), Building Information Modeling (BIM), Agile methodologies, and Earned Value Management (EVM)—into a unified and operational structure. A qualitative research approach was adopted, based on a systematic literature review following PRISMA principles and a structured framework analysis. The proposed model is grounded in Koskela’s Transformation–Flow–Value (TFV) theory, ensuring a multidimensional perspective that simultaneously top management task efficiency, workflow reliability, and stakeholder value generation. The results demonstrate that integrating management frameworks within a phased roadmap enhances project predictability, improves coordination among stakeholders, reduces waste, and supports data-driven decision-making. The study contributes to the literature by providing a comprehensive and adaptable framework that bridges the gap between fragmented methodologies and holistic project delivery. Although conceptual in nature, the framework establishes a foundation for future empirical validation and practical implementation in diverse construction contexts.
Author: BECKER, VANDIR FILHO
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7146956699
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