An integrated lean-digital framework for optimizing civil- engineering supply chains: mixed-methods evidence from multi-project case analyses

Authors

  • Mustafa Sabbar Graduate Student, Civil Engineering Department, Faculty of Engineering, Altinbas University, Istanbul, Türkiye.
  • Sepanta Naimi

DOI:

https://doi.org/10.22399/ijcesen.2784

Keywords:

Civil-engineering supply chains, Lean construction, Building Information Modelling, Optimization framework

Abstract

All around, civil engineering megaprojects are still struggling with budget overruns, schedule slippage,
and disjointed supply-chain coordination. This paper conducts an embedded, multiple-case study of
publicly recorded projects spanning Asia, Africa and the Middle East. Using a pragmatist mixed-methods
approach, we thematically coded secondary reports, audits and peer-reviewed case narratives;
descriptive statistics were used to investigate related cost- and time-performance data. Convergent
triangulation found four common levers distinguishing better performers, early strategic alignment
among stakeholders, proactive scenario-based risk management and digitally enabled transparency
through (BIM) and (IoT) twins. Collaborative contracting cultures such as Integrated Project Delivery.
Projects using these levers reduced lead-time variation by as much as 17% and cost overruns by 4–6
percentage points. Building on these ideas, we suggest an Integrated Lean-Digital Framework
cascading through Strategic Alignment, Collaborative & Digital Integration, Operational
Execution and Continuous Improvement & Sustainability. Though reliance on secondary data
reduces statistical generalizability, deliberate case selection and cross-source triangulation improve
transferability, therefore offering a consistent path to fight fragmentation. This synthesis
offers practical advice for managers and politicians.

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Published

2025-06-26

How to Cite

Mustafa Sabbar, & Sepanta Naimi. (2025). An integrated lean-digital framework for optimizing civil- engineering supply chains: mixed-methods evidence from multi-project case analyses. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.2784

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Research Article