Framework for Optimizing the Construction Process: The Integration of Lean Construction, Building Information Modeling (BIM), and Emerging Technologies

Abstract

This paper presents a novel framework for integrating Building Information Modeling (BIM), Lean Construction tools, and Emerging Technologies to optimize construction project management. The framework categorizes these elements across four phases: Plan, Design, Construction, and Operation in construction projects. Each phase identifies specific intersections and synergies among the three domains to enhance functionality, collaboration, and value delivery. Integration is built upon core principles such as functionality alignment, process efficiency, and lifecycle optimization. This study also highlights Key Performance Indicators (KPIs) to evaluate the framework’s effectiveness in real-world applications and addresses limitations to guide future research. The framework was developed and validated using the Design Science Research Methodology (DSRM). The proposed approach offers a transformative pathway for addressing inefficiencies, reducing waste, and maximizing project outcomes in modern construction.

1. Introduction

The construction industry is a key driver of economic growth worldwide, contributing significantly to infrastructure development, job creation, and technological advancements. However, despite its crucial role, the sector continues to face persistent challenges, including cost overruns, project delays, inefficiencies, and poor coordination among stakeholders [1,2]. Numerous studies and industry reports highlight that a significant proportion of construction projects face budget overruns and schedule delays, primarily due to fragmented workflows, inefficient resource allocation, and poor decision-making processes [3]. These issues are compounded by the complexity of modern construction projects, which require better coordination, integration of digital tools, and real-time performance monitoring, and management of resources to achieve better project outcomes [4]. Addressing these challenges requires the strategic integration of Lean Construction, Building Information Modeling (BIM), and Emerging Technologies, which have become pivotal methodologies for optimizing construction workflows. Lean Construction emphasizes eliminating waste, streamlining workflows, and improving efficiency through systematic planning and continuous improvement strategies [5], while BIM facilitates better collaboration by providing a centralized platform for data sharing, visualization, and lifecycle management across project stakeholders [6]. Emerging Technologies such as Artificial Intelligence (AI), Internet of Things (IoT), construction robotics, and digital technologies further enhance these processes by enabling automation, real-time monitoring, and secure data management [7]. Building upon an extensive literature review conducted by the authors [8], this study identifies critical synergies between these methodologies.

A significant gap remains in their holistic integration within construction project management frameworks. Existing frameworks focus on Lean Construction with BIM alone but lack Emerging Technologies capabilities [9]. Additionally, studies integrating Emerging Technologies often do so in isolation without a structured approach that aligns them with BIM and Lean Construction in a coherent lifecycle framework. So, the review [8] revealed gaps in existing frameworks, particularly in integrating Lean Construction and Emerging Technologies with BIM to address the industry’s pressing challenges. This research addresses this gap by proposing a comprehensive framework that systematically integrates Lean Construction, BIM, and Emerging Technologies across the four key lifecycle phases of construction projects—the Plan Phase, which focuses on early-stage decision making, feasibility assessment, and cost estimation; Design Phase; Construction Phase; and Operation Phase, which facilitates predictive maintenance, space utilization, and asset management. The framework is evaluated using Key Performance Indicators (KPIs) such as cost efficiency, time savings, productivity, and stakeholder satisfaction, ensuring a holistic understanding of its impact.

To develop and validate the proposed framework, the study follows the Design Science Research Methodology (DSRM), a widely accepted approach for designing and evaluating innovative solutions (artifacts) in complex environments [10]. The DSRM process comprises three steps as follows: (i) designing and developing the framework; (ii) proposing the framework; and (iii) evaluation.

This study offers a twofold contribution as follows: theoretically, it advances the construction management literature by establishing a unified framework that bridges the siloed application of Lean Construction, BIM, and Emerging Technologies, a gap noted in previous studies. Practically, it provides industry professionals with a scalable and validated tool to enhance project management efficiency, reduce waste, and improve coordination throughout the construction lifecycle. The framework’s empirical validation using expert input ensures its relevance and applicability to current industry challenges.

The remainder of this paper is structured as follows: Section 2 presents the research methodology, detailing the DSRM approach used in designing and evaluating the framework. Section 3 provides a comprehensive literature review on Lean Construction, BIM, and Emerging Technologies, identifying their individual and collective contributions and Key Performance Indicators. Section 4 introduces the proposed framework, explaining its core components and their integration across construction lifecycle phases. Section 5 describes the evaluation process, including expert validation and their insights. Finally, Section 6 concludes with the findings, limitations, and future research directions and the study’s contributions to industry and academia.

Key Contributions of This Study

• Proposes a novel, integrative framework combining Lean Construction, BIM, and Emerging Technologies.
• Structures integration across the full construction lifecycle (Plan, Design, Construction, and Operation).
• Validated through expert review with high agreement scores across SEQUAL dimensions.
• Provides a theoretically grounded basis for improving KPIs such as cost efficiency, stakeholder collaboration, and process automation through the structured integration of Lean, BIM, and Emerging Technologies.

2. Materials and Methods

The framework was developed using the Design Science Research Methodology (DSRM) [10], a systematic approach for designing and evaluating solutions in Information Systems (IS) research [11]. DSRM involves the following three main steps: (i) design and development; (ii) proposal; and (iii) evaluation, ensuring consistency with the existing literature and facilitating artifact acceptance.

Previous studies have demonstrated DSRM’s effectiveness in construction contexts. One study [12] highlighted its structured approach to problem solving in civil engineering, enhancing project efficiency and productivity. Another integrated Agile Project Management, Design Thinking, and Lean Start-up with DSRM for Lean Construction research, emphasizing iterative processes and improving experiment validity [13]. Additionally, another study [14] utilized DSRM to explore 3D printing technology in formwork, outlining key steps such as problem identification, artifact creation, and evaluation. In this study, the framework development began with a literature review [8] identifying relevant Lean Construction tools, BIM uses, and Emerging Technologies aligned to lifecycle phases. The framework was then proposed by integrating these elements systematically, aiming to improve efficiency, reduce waste, and enhance collaboration, aligned with industry KPIs such as cost efficiency, time savings, and stakeholder satisfaction. Finally, expert evaluations assessed the framework’s practicality and effectiveness, leveraging their extensive experience in construction management, BIM, Lean methodologies, and Emerging Technologies [15]. Figure 1 illustrates the conceptual framework (DSRM) developed in this study.

3. Literature Review

3.1. Lean Construction and BIM Interaction

This section synthesizes prior research on the interaction between Lean Construction and Building Information Modeling (BIM), highlighting their complementary roles in improving construction efficiency and process integration. The insights presented here serve to complete and extend the authors’ previous study on the systematic literature, which identified key tools and functions enabling this integration and informed the conceptual development of the proposed framework.

Existing studies emphasize the independent benefits of Lean Construction, BIM, and Emerging Technologies [7,16,17]. BIM integration with Lean Construction is well-established, particularly BIM’s role as a digital enabler. The study in [18] demonstrates that BIM enhances waste reduction, logistics, scheduling, cost management, and real-time tracking when combined with Lean tools like Last Planner System (LPS), Target Value Design (TVD), Kanban, and Just-in-Time (JIT) [19,20]. This research introduced BeaM!, a BIM-integrated Lean production system that improved workflow reliability and reduced variability. A BeaM! case study confirmed significant improvements in task coordination and efficiency. However, the study mainly covers the Construction phase, neglecting full lifecycle integration (Plan–Design–Construction–Operation) and excluding Emerging Technologies, highlighting the need for a more comprehensive framework.

Table 1. Key summary of the interactions and findings of [18].

Key Interaction	Findings	Case Study
BIM as Lean Enabler	BIM supports Lean principles such as waste reduction, workflow stability, and continuous improvement through real-time visualization which confirms BIM’s role in Lean process optimization.	BeaM! Production Management System, BIM-driven Lean scheduling improved workflow reliability and reduced project variability.
Lean Tools Integrated with BIM	LPS, which is aligned with 4D BIM-driven task planning and Digital Twins for execution monitoring; TVD, where BIM supports cost modeling and scenario testing to align designs with project budget constraints; Kanban, which enables real-time task prioritization and resource allocation in the construction phase; and JIT are enhanced through BIM for scheduling and workflow tracking, where BIM optimizes material procurement schedules and prefabrication workflows.	BIM-based TVD and Kanban boards improved cost tracking and work in progress (WIP) management [21].
Production Management and Scheduling	Introduces BeaM!, a Lean–BIM integration system for project planning and execution which can Supports the Construction Phase, where BIM is integrated with Takt Planning (the process of dividing construction work into zones (Takt areas) and assigning a consistent time (Takt time) for completing the work in each zone), IoT, and Robotics.	The case study confirms efficiency gains through BIM.

summarizes the key findings of the study [18]:

Another study [22], reinforces BIM and Lean Construction integration, highlighting BIM’s role in enhancing Lean principles through improved workflow coordination, waste reduction, and optimized scheduling. It identifies key Lean methodologies, Value Stream Mapping (VSM), Target Value Design (TVD), Last Planner System (LPS), and Kanban, which benefit from BIM’s real-time visualization and automated tracking. Notable cases include the Lower Manhattan Construction Command Center (LMCCC) in the USA, where BIM-driven 4D scheduling improved coordination, reduced rework, and mitigated risks; the Yas Island Hotel project, located in the United Arab Emirates, demonstrating BIM’s effectiveness in prefabrication planning and scheduling; and the KAUST Solar Chimney project located in Saudi Arabia, showcasing construction sequencing and work detailing improvements. Additionally, the CCJ Precast Project located in Brazil, utilized BIM automation to reduce processing time by 80%. Despite these strengths, the study mainly emphasizes BIM’s visualization capabilities, lacking a structured integration with Emerging Technologies.

Table 2. Key summary of the interactions and findings of [22].

Key Interaction	Findings	Case Study
BIM as Lean Enabler	BIM supports Lean Construction by improving project efficiency, reducing waste, and enhancing collaboration, focusing mainly on BIM’s role in coordination and scheduling and lacking a full exploration of Lean–BIM.	The Lower Manhattan Construction Command Center (LMCCC) case study shows how BIM-driven scheduling optimization improved project coordination and risk mitigation.
Lean Tools Integrated with BIM	BIM enhances Value Stream Mapping (VSM), Target Value Design (TVD), Last Planner System (LPS), and Kanban through automated scheduling and real-time tracking.	The Yas Island Hotel project utilized BIM for prefabrication planning, grid-shell resource leveling, and scheduling optimization.
Production Management and Scheduling	BIM supports automated work package creation, resource leveling, and 4D scheduling to improve Lean project execution, which can validate Takt Planning and the Digital Twin-driven Lean execution approach.	The case study confirms efficiency gains through BIM.
Prefabrication and Lean Supply Chain	BIM facilitates prefabrication, modularization, and logistics coordination, reducing variability and improving efficiency, which can support Just-in-Time (JIT) integration and Blockchain-enabled Lean supply chain tracking.	The CCJ Precast Project used BIM to streamline precast detailing and production sequencing, reducing processing time by 80%.

summarizes the findings of the study in [22].

Reference [23] emphasizes the synergy between Lean Construction and BIM, demonstrating enhanced waste reduction, efficiency, and decision making. BIM supports clash detection to minimize rework and waste, aligning with Lean’s Just-In-Time (JIT) for optimized material procurement. Collaboration improves through BIM’s Common Data Environment (CDE), integrated with Lean’s Last Planner System (LPS) for reliable scheduling. Workflow optimization is achieved via 4D BIM visualizations complementing Lean pull planning techniques. For quality control, BIM integrates real-time analysis with Lean’s PDCA cycle, while Lean’s Target Value Design (TVD) closely aligns with BIM cost estimation tools. However, the study mainly offers theoretical models without empirical project validation and lacks integration with emerging technologies like AI, IoT, and Blockchain.

Table 3. Key summary of the interactions and findings of [23].

Key Interaction	Findings
Clash Detection and Waste Reduction	BIM enhances error detection and reduces rework in construction. Lean ensures continuous improvement in workflow efficiency, where it can support the Kaizen, PDCA, and BIM-based quality checks in the design and construction phases.
Lean Tools Integrated with BIM	LPS and 4D BIM where BIM visualizes task sequences. While LPS ensures smooth project execution by reducing uncertainties, TVD ensures cost alignment. Meanwhile, BIM-based cost estimation tools (5D BIM) provide real-time financial tracking, and JIT approach aligns with BIM-driven prefabrication, ensuring materials arrive only when needed.
Automation and Robotics	BIM facilitates robotic integration, while Lean removes inefficiencies in construction sequences which can be aligned with Takt Planning, RFID tracking, and robotics-driven logistics.

summarizes the findings.

The study in [24] examines BIM and Lean Construction synergies, highlighting enhanced efficiency, waste reduction, and optimized decision making. It emphasizes benefits to Lean tools, Value Stream Mapping (VSM), Last Planner System (LPS), and Target Value Design (TVD), from BIM’s real-time visualization and digital tracking. BIM supports waste reduction via clash detection and prefabrication aligned with Lean’s Just-In-Time (JIT). Integration with a Common Data Environment (CDE) strengthens collaboration, providing stakeholders real-time updates. Automated quality control through BIM simulations and IoT monitoring aligns with Lean’s Kaizen and PDCA cycles. However, the study mainly addresses BIM as a Lean enabler during design and construction phases, offering limited insights into post-construction operations. Additionally, detailed strategies for implementing AI, IoT, and Digital Twins are lacking, emphasizing the need for a comprehensive BIM–Lean–Emerging Technologies lifecycle framework.

Table 4. Key summary of the interactions and findings of [24].

Key Interaction	Findings
Process Optimization and Efficiency	BIM and Lean integrate to improve construction efficiency, reduce waste, and enhance scheduling through VSM and LPS.
Reducing Waste and Variability	BIM’s real-time data tracking eliminates non-value-adding activities, reducing material waste and rework which can be aligned with JIT-driven material tracking.
Collaboration and Stakeholder Involvement	BIM facilitates a Common Data Environment (CDE), while Lean focuses on workflow synchronization and continuous communication which can be aligned with Blockchain-based CDE for secure, real-time data sharing and collaboration.
Design Optimization and Constructability	BIM provides 3D, 4D, and 5D models that improve early constructability assessments. Lean’s TVD ensures alignment between cost, value, and efficiency goals.
Automated Quality Control and Safety	Lean emphasizes quality improvement and defect prevention, which BIM enhances via clash detection, simulations, and IoT-based monitoring which can be aligned with which integrates Lean’s Kaizen and PDCA for continuous improvement.
Continuous Improvement (Kaizen and PDCA Cycle)	BIM enables iterative design improvements, aligning with Lean’s Kaizen and PDCA methodologies.

summarizes these findings.

Study [21] investigates Lean Construction and BIM integration, aiming to enhance workflow efficiency, waste reduction, and collaboration in the design phase. It highlights Lean tools, Last Planner System (LPS), Target Value Design (TVD), Set-Based Design (SBD), and Choosing by Advantages (CBA), which benefit from BIM integration for improved decision making. The paper emphasizes Integrated Project Delivery (IPD) and Big Room collaboration for effective communication among stakeholders, and it discusses IoT and Digital Twins for performance monitoring, though it lacks comprehensive coverage of automation and AI-driven processes. Despite validating Lean–BIM synergies in planning and coordination, the study’s scope is limited to the design phase, without the exploration of lifecycle-wide integration or the inclusion of Emerging Technologies. It also notes challenges with software interoperability and a shortage of real-world case validations.

Table 5. Key summary of the interactions and findings of [21].

Key Interaction	Findings
Lean–BIM Synergy	The paper highlights the integration of Lean and BIM in the design phase to improve workflow efficiency, reduce waste, and enhance collaboration, but it is limited to design phase not the project lifecycle.
Lean Tools Integration	The study identifies Last Planner System (LPS), Target Value Design (TVD), Set-Based Design (SBD), and Choosing by Advantages (CBA) as Lean tools supporting BIM in decision making and design management.
Technology Usage	The paper discusses the use of digital tools such as BIM, IoT, and Digital Twins for performance tracking.
Process Optimization and Stakeholder Collaboration	Lean–BIM integration enhances planning, control, and decision making in design, leading to reduced rework and improved stakeholder collaboration, which can be aligned with real-time data sharing in CDE and AR/VR visualization.

summarizes the study’s key findings.

A study of the state-of-the-art review [25] on BIM and Lean Construction interactions highlights BIM as a key enabler of Lean principles, enhancing workflow efficiency, waste reduction, and process transparency. The study emphasizes the use of Last Planner System (LPS), Value Stream Mapping (VSM), and Pull Planning to integrate Lean methodologies within BIM-based design and construction workflows. While BIM significantly improves collaboration through Integrated Project Delivery (IPD), Common Data Environment (CDE), and clash detection, the research lacks automation strategies, AI-driven decision making, Blockchain applications, and emerging technologies, all crucial for next-generation construction management.

Table 6. Key summary of the interactions and findings of [25].

Key Interaction	Findings
Lean–BIM Synergy	The paper highlights BIM as a facilitator of Lean Construction, enhancing efficiency in workflow, reducing waste, and promoting continuous improvement limited to design and construction.
Lean Tools Integration	The paper discusses the use of Last Planner System (LPS), Value Stream Mapping (VSM), and Pull Planning in BIM-driven projects to improve scheduling, waste reduction, and process transparency.
Process Optimization and Stakeholder Collaboration	The paper emphasizes BIM’s role in reducing variability and uncertainty in Lean workflows, particularly in clash detection, design coordination, and cost control. Also, the study acknowledges that BIM enhances collaboration through Integrated Project Delivery (IPD), Common Data Environment (CDE), and digital coordination tools.

summarizes the study’s key findings.

Reviewed studies on Lean Construction and BIM integration identify critical gaps justifying a comprehensive, technology-enhanced Lean–BIM framework. Most existing research emphasizes the design and early construction phases, overlooking execution, logistics, and post-construction operations. While BIM effectively enables Lean methods for workflow optimization, waste reduction, and collaboration, standardized full lifecycle integration remains lacking. Additionally, studies frequently omit Emerging Technologies, and interoperability issues between BIM and Lean tools present practical challenges. This paper addresses these gaps by proposing a unified framework integrating Lean Construction, BIM, and Emerging Technologies across the entire project lifecycle.

3.2. Selected Lean Construction Tools

Lean Construction principles focus on minimizing waste and maximizing value throughout the project lifecycle [26]. The tools selected for this framework were identified through an extensive literature review and tailored to address the specific challenges in each phase of construction projects [8], and based on their functionalities in the construction process, they aligned with core management principles [27]. In the Plan and Design phases, tools such as Value Stream Mapping (VSM) and Target Value Design (TVD) are utilized to enhance early-stage efficiency. VSM offers a visual representation of processes, helping to identify non-value-adding activities [28], while TVD promotes cost alignment with client priorities through early stakeholder engagement [29]. The PDCA (Plan–Do–Check–Act) facilitates iterative improvements and quality control in both planning and operational phases [30], and Kaizen drives continuous improvement in the Design and Operation phases, fostering iterative refinements in workflows and processes. This emphasizes the importance of standardizing processes to enable the effective implementation of the PDCA cycle [31].

While PDCA, Kaizen, and LPS all support continuous improvement and control, their application within the framework is differentiated by scope and lifecycle phase. PDCA functions as a strategic cycle applied during the planning and operational stages. Kaizen acts as a continuous improvement philosophy embedded across stages, especially in design and operation. In contrast, LPS is a tactical tool focused on short-term planning. This phase-based distinction ensures that each tool aligns with its intended management function.

Total Quality Management (TQM) reinforces quality assurance across disciplines, embedding responsibility for continuous enhancement at all organizational levels [32]. Visual Management (VM) is applied during the Design phase to strengthen transparency and collaboration. By using graphical communication tools and real-time data visualization, VM facilitates shared understanding and early issue detection [33]. In the Construction phase, tools such as Takt Planning, Just-In-Time (JIT), and Kanban help optimize workflow pacing, material delivery, and resource allocation. Takt Planning ensures consistent production rhythms [34], JIT aligns deliveries with actual site needs to minimize waste [35], and Kanban supports task tracking and prioritization [36]. The 5S Methodology enhances workplace organization and safety by instilling structured site management practices [37]. Finally, the Last Planner System (LPS) is incorporated as a central planning tool across phases, particularly to reinforce short-term scheduling, team accountability, and workflow reliability [38]. These Lean Construction tools were strategically incorporated into the framework to align with the phases of Plan, Design, Construction, and Operation, ensuring holistic integration with BIM uses and Emerging Technologies.

3.3. BIM Uses

Building Information Modeling (BIM) plays a pivotal role in streamlining construction processes by enhancing collaboration, visualization, and decision making [6]. This study builds upon the BIM analysis presented in the previous literature review [8], particularly the adoption of the Penn State BIM Uses framework due to its comprehensive and structured classification of BIM applications across the construction lifecycle. The framework offers a standardized foundation for aligning BIM functionalities with project phases—Planning, Design, Construction, and Operation—ensuring interoperability and clarity in implementation strategies [39,40]. For the purposes of this research, the relevant BIM uses were selectively mapped to the framework’s life cycle phases based on their capacity to support Lean integration and technology-enabled workflows. These include functions such as capturing existing conditions (3D/7D), authoring 4D models, cost estimation (5D), performance and sustainability analysis (6D), and facility management (7D). Each BIM use is associated with one or more BIM dimensions that enhance data visualization, decision making, and coordination throughout the project lifecycle. A detailed justification and categorization of these uses and dimensions is provided in an earlier review [8].

3.4. Emerging Technologies

The technologies identified and incorporated into this framework are tailored to address specific challenges and improve efficiencies across all lifecycle phases and were selected based on the literature review conducted [8]. These include Laser Scanning, Photogrammetry, and Reality Capture, which facilitate precise documentation of existing site conditions for accurate planning, design, and verification [41,42]. Drones/UAVs (Unmanned Aerial Vehicles) enable aerial site inspections, progress tracking, and enhanced data collection for emergency management and large-scale project monitoring [43]. Artificial Intelligence (AI) and Machine Learning optimize cost estimation, predictive analytics, and data-driven decision making while automating scheduling, enhancing real-time quality control, and improving proactive project management [44,45,46,47]. AI Predictive Analytics specifically provides insights into structural performance, energy efficiency, and other critical project parameters during design and operational phases [47,48,49]. Digital Twins offer virtual replicas of physical assets for real-time monitoring, simulation, and performance optimization, enabling lifecycle management [44,45,50,51,52]. The Internet of Things (IoT) and sensors collect and transmit real-time data for maintenance monitoring, system performance evaluations, and energy management [45,46]. Big Data and Analytics analyze vast quantities of project related data to identify trends, optimize workflows, and improve decision making [51,53,54]. Geographic Information Systems (GISs) integrate spatial data with BIM to support site selection, environmental analysis, and resource planning [55]. Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) enhance spatial visualization, immersive design reviews, and real-time collaboration during design and construction activities [56,57,58]. The Common Data Environment (CDE) provides a centralized platform for managing and sharing project information, ensuring all stakeholders access the latest data. Blockchain ensures secure, immutable data sharing, transparency, and collaboration across all phases while tracking sustainability metrics [47,54]. Robotics and Automation improve efficiency, precision, and safety in construction tasks, including material handling, repetitive processes, and onsite construction, with tools such as UAVs (Unmanned Aerial Vehicles) and AGVs (Automated Guided Vehicles) for transport and assembly [44,54,59,60,61]. Radio Frequency Identification (RFID) enables efficient tracking and management of materials and equipment, improving logistics and resource allocation [16,62,63]. Finally, 3D Printing/Additive Manufacturing supports on demand fabrication, reducing material waste and lead time while enabling customized construction solutions [47,54,60,64]. These Emerging Technologies complement Lean Construction tools and BIM applications, creating a cohesive and efficient framework for managing complex construction projects while addressing key industry challenges.

3.5. Key Performance Indicators (KPIs)

Key Performance Indicators (KPIs) are essential metrics for evaluating the efficiency, effectiveness, and overall success of the framework for construction projects. In this framework, the selected KPIs align with the best global practices and industry standards to ensure the integration of Lean Construction, BIM, and Emerging Technologies addresses critical project challenges. These KPIs include cost efficiency and savings, which evaluates the framework’s ability to minimize overall project costs, reduce waste, and support budget adherence [65]; time efficiency and delivery, measuring improvements in schedule reliability, reduction in delays, and on-time completion [65]; productivity and resource utilization, assessing the optimized use of labor, equipment, and materials to maximize performance [66,67]; waste reduction, which focuses on minimizing non value-adding activities and material waste [66]; quality and safety, monitoring improvements in construction quality, error prevention, and compliance with safety standards [65]; stakeholder satisfaction and collaboration, evaluating communication flow, coordination, and stakeholder engagement [67]; and process optimization and automation, which highlights advancements in automation, real-time data integration, and overall process efficiency [66,68]. To validate the effectiveness of the framework in real-world construction projects, a structured approach for measuring and assessing these KPIs is essential.

4. Integrated Framework for Construction Lifecycle Optimization

4.1. Framework Core

The framework is built on key interaction areas and core principles that ensure seamless synergy between Lean Construction, BIM, and Emerging Technologies. The integration is founded on waste reduction, process efficiency, stakeholder collaboration, continuous improvement, and value-driven decision making. The framework structures Lean–BIM and Emerging Technologies interactions across the entire project lifecycle (Plan–Design–Construction–Operation), ensuring workflow optimization, cost control, and real-time monitoring are embedded at every stage. It goes beyond traditional Lean–BIM models by addressing data interoperability, supply chain transparency, and lifecycle optimization, ensuring a scalable, technology-driven Lean–BIM methodology for enhanced project performance and sustainability [69]. The following

Table 7. Framework integrating Lean Construction, BIM, and Emerging Technologies.

Phase	BIM Use	Lean Tool	Emerging Technology	Integration
Plan	Capture Existing Conditions	Value Stream Mapping (VSM)	Laser Scanning, Photogrammetry, Reality Capture, Drones/UAVs	VSM analyzes and streamlines workflows for efficient data collection, ensuring optimal processes for capturing existing site conditions beside Laser scanning, Photogrammetry, and drones. See Figure 2.
	Author Cost Estimation	Target Value Design (TVD)	AI and Machine Learning	AI optimizes cost estimation by analyzing historical data, while TVD aligns cost outputs generated by 5D BIM with project goals. See Figure A1.
	Author 4D Model	Last Planner System (LPS)	Digital Twins, IoT	LPS enhances BIM’s 4D modeling by aligning construction schedules with short term task planning, improving workflow reliability beside Digital Twins and IoT to enhance real-time schedule monitoring in 4D models. See Figure A2.
	Analyze Program Requirements	PDCA	Big Data and Analytics	PDCA ensures continuous refinement of program requirements defined in BIM, improving alignment with project objectives through iterative feedback loops. Big Data analyzes programmatic requirements. See Figure A3.
	Analyze Site Selection Criteria	Value Stream Mapping (VSM)	GIS	GIS integrates with BIM for spatial analysis, while VSM can streamline the workflows for evaluating site selection criteria. See Figure A4.
	Author Design	Target Value Design (TVD)	Common Data Environment (CDE), AR, VR	TVD ensures designs align with cost and value targets, supported by CDE for collaboration and AR/VR for enhanced design visualization in BIM. See Figure A5.
	Review Design Model(s)	Target Value Design (TVD)	Common Data Environment (CDE), AR, VR	TVD ensures design reviews align with value-driven goals, supported by CDE for collaborative coordination and AR/VR for enhanced visualization. For the same process, see Figure A5.
Design	Analyze Structural Performance	Kaizen	AI Predictive Analytics	Kaizen can promote continuous improvement in structural design by leveraging BIM simulations for iterative testing and optimization. Predictive analytics provides insights into structural performance. See Figure A6.
	Analyze Energy Performance	Total Quality Management (TQM)	IoT	TQM ensures that BIM’s detailed energy analyses meet performance and quality standards, aligning with sustainability and efficiency goals. IoT can monitor energy consumption metrics and ensure collaboration, and it can be used in all phases. See Figure A7.
	Analyze Lighting Performance	Total Quality Management (TQM)	Smart Sensors, IoT	BIM integrates lighting simulations with IoT-enabled smart sensors to evaluate energy-efficient lighting designs under TQM standards. For the same process, see Figure A7.
	Coordinate Design Models	Visual Management Techniques	Common Data Environment (CDE)AR, VR	Visual Management and BIM together improve collaboration and transparency, facilitating efficient multi-disciplinary model coordination. CDE ensures all stakeholders access the latest BIM data and it can be used in all phases. AR and VR can enhance the spatial understanding of design models. See Figure A8.
	Analyze Engineering Performance	Kaizen	AI and Machine Learning	Kaizen promotes the refinement of engineering solutions by leveraging AI in BIM for predictive analysis and performance optimization. See Figure A9.
	Analyze Sustainability Performance	Target Value Design (TVD)	Blockchain	Blockchain tracks material sustainability data, ensuring alignment with 6D BIM-based sustainability evaluations and cost constraints under TVD which can ensure that sustainability evaluations in BIM align with project goals and cost constraints. See Figure A10.
Construction	Author Construction Site Logistics Model	Takt Planning and Control	Robotics and Automation, RFID	Takt Planning aligns with BIM site logistics models by balancing workflows, improving resource utilization, and preventing bottlenecks. Robotics, automation, and RFID improve material handling and logistics. See Figure A11.
	Author Temporary Construction Systems Model	Kanban	IoT, Digital Twins	Kanban ensures the efficient planning and execution of temporary systems, while Digital Twins and IoT enable real-time updates to BIM models during construction. See Figure A12.
	Fabricate Products	Just In Time (JIT), 5S Methodology	3D Printing/Additive Manufacturing, IoT	IoT-enabled 3D printing ensures precise, on-demand fabrication, aligning with JIT’s inventory reduction and 5S’s organized workspace. IoT sensors optimize material use, automate replenishment, and enhance workflow efficiency while minimizing waste. See Figure A13.
	Layout Construction Work	Kanban	AR, VR, and MR	AR, VR, and MR enhance spatial visualization for layout tasks, while Kanban ensures effective prioritization and management of construction activities. See Figure A14.
Operation	Compile Record Model	Kaizen	Blockchain	Blockchain ensures secure, immutable record models, while Kaizen iteratively improves their structure and usability.
	Monitor Maintenance	Kaizen	IoT Sensors, Digital Twins, Big Data Analytics	IoT provides real-time performance data for assets, Digital Twins simulate asset behavior for predictive insights; Big Data Analytics processes data trends to refine maintenance strategies supporting Kaizen’s iterative improvements in maintenance strategies. See Figure A15.
	Monitor System Performance	Kaizen	IoT Sensors, Digital Twins, Big Data Analytics	Digital Twins simulate system performance for optimization, aligning with Kaizen’s goal of continuous refinement based on the data captured by IoT and analytics provided by Big Data Analytics with BIM models. For the same process, see Figure A15.
	Monitor Assets	Plan-Do-Check-Act (PDCA)	IoT, Blockchain	PDCA ensures continual monitoring of asset conditions, while IoT provides real-time data and Blockchain secures asset records. See Figure A16.
	Monitor Space Utilization	Kaizen	IoT Sensors, Digital Twins, Big Data Analytics	Kaizen optimizes space usage by leveraging IoT for real-time monitoring and Big Data Analytics for identifying patterns and trends with Digital Twins to simulate the space. For the same process, see Figure A15.
	Analyze Emergency Management	PDCA	Drones/UAVs	Drones provide real-time site monitoring during emergencies, while PDCA ensures the continuous testing and refinement of response strategies using BIM simulations. For the same process, see Figure A16.

illustrates the framework in detail.

4.2. Application Recommendations of the Framework

To demonstrate the practical implementation of the proposed framework, a comprehensive set of application recommendations has been developed, covering the following four key phases of the construction lifecycle: Plan, Design, Construction, and Operation. While the full range of applications is provided in Appendix A.1, this section presents one representative example to illustrate how the integration of BIM, Lean Construction tools, and Emerging Technologies can be applied in practice.

4.2.1. Plan Phase

This subsection presents the recommended applications of the proposed framework for the plan phase. Below is one example of the first row of the framework that is shown in Table 7, and the rest are provided in Appendix A.1.

VSM in Capturing Existing Condition

Figure 2 provides a detailed visualization of the implementation process of the first step in the plan phase, which is using VSM in capturing existing conditions.

Figure 2. Example for the application of VSM in capturing existing conditions and using different technologies (Laser Scanning, Photogrammetry, Reality Capture, and Drones/UAVs).

Figure 2. Example for the application of VSM in capturing existing conditions and using different technologies (Laser Scanning, Photogrammetry, Reality Capture, and Drones/UAVs).

The diagram illustrates integrating Value Stream Mapping (VSM) with advanced technologies, laser scanning, photogrammetry, and drones during construction planning. VSM streamlines workflow by identifying inefficiencies, reducing redundant processes, and enhancing collaboration between data collection and BIM, resulting in reduced cycle time, improved accuracy, productivity, and cost savings [70].

The first step, Mapping the Current State, involves deploying drones, laser scanners, and photogrammetry to gather essential site data (aerial images, point clouds, and topographical details) converted into BIM-compatible 3D models. Common challenges include manual data validation, delays in processing data, and coordination inefficiencies [71]. The second step, Identifying Waste, categorizes workflow inefficiencies such as overproduction (excessive data), waiting times (processing delays), defects and rework (measurement errors), and underutilized talent (poor team allocation) [72].

In the third step, Designing the Future State, workflow optimizations include automating data processing using AI tools, establishing standardized protocols (SOPs) for consistent data collection, promoting cross-functional collaboration through Common Data Environments (CDEs) and implementing IoT-enabled drones for real-time quality control. Focus is placed on collecting project-critical data and automating error checks.

The final step, Implementation and Monitoring, involves training programs on VSM and technologies, real-time dashboards to monitor progress, and iterative feedback loops to continuously refine processes.

Direct impacts on KPIs include cost savings via reduced redundancies; improved time efficiency through automation; enhanced productivity by streamlined workflows; waste reduction by eliminating unnecessary data collection; improved quality and safety from standardized protocols; higher stakeholder satisfaction due to transparency; and process optimization leveraging AI and IoT, minimizing manual interventions.

5. Evaluation

5.1. Framework Assessment Approaches

A structured expert evaluation approach utilizing the Delphi Method [73] was adopted to assess the validity, feasibility, and applicability of the proposed framework. This systematic, iterative method collects structured feedback from expert panels, ensuring the integration of Lean Construction, BIM, and Emerging Technologies aligns with the industry’s best practices. The evaluation process includes the following:

• Expert selection: Diverse experts in Lean Construction, BIM, and Emerging Technologies, with a minimum of 10 years’ experience.
• Questionnaire development: A structured questionnaire based on the SEQUAL framework [74,75].
• First round assessment: Experts review the framework and provide quantitative (Likert-scale) and qualitative feedback.
• Second round refinement: Feedback is analyzed, and the framework is adjusted accordingly.
• Final validation round: Experts reassess the revised framework for validity and practicality.

Using this Delphi approach ensures the framework’s robustness, iterative refinement, and practical relevance to construction management.

5.2. Structured Expert Questionnaire

To ensure the comprehensive assessment of the proposed framework, this study adopts the Semiotic Quality Framework (SEQUAL). Originally developed to evaluate the quality of conceptual models, SEQUAL provides a structured set of dimensions for assessing how well a model or framework communicates its intended meaning to different stakeholders. It evaluates factors such as syntactic quality, semantic clarity, pragmatic usefulness, and other related dimensions, making it a suitable choice for assessing the clarity, applicability, and relevance of the proposed construction framework [74,75].

The expert evaluation is conducted through a structured questionnaire, focusing on assessing the framework based on predefined dimensions derived from the Semiotic Quality Framework (SEQUAL) used to assess the quality of the proposed framework; it provides a structured approach to evaluating the framework based on multiple quality dimensions [74,75]. The SEQUAL quality statements with seven dimensions outlined below is assessed, as presented in

Table 8. SEQUAL quality statements.

Nº	Quality Dimension	Statement
1	Physical	The framework and its relevant components are systematically structured, visually representing the relationships between BIM Uses, Lean Tools, and Emerging Technologies, making them available for stakeholders’ interpretation.
2	Empirical	The framework is based on valid and practical knowledge that makes it applicable to real-world construction projects.
3	Syntactic	Follow a standardized methodology, ensuring a structured and systematic representation of processes.
4	Semantic	The relevance and meaningful representation of the framework’s components in addressing construction challenges.
5	Pragmatic	Usefulness and applicability.
6	Social	Stakeholder agreement and collaboration.
7	Deontic	The framework’s ability to meet industry regulations, promote sustainability, and support innovation.

.

The questionnaire consists of 21 evaluation statements categorized under the seven key dimensions, which are presented to experts in the form of closed-ended questions. To further enrich the framework, an open-ended question (Q22) is included at the end of the survey, inviting experts to share additional feedback, suggestions, or critiques that may not have been captured in the structured items. This qualitative input aims to enhance the robustness and applicability of the framework. The participant information needed before responding to the evaluation statements, experts are asked to provide the following background information:

• Name (Optional).
• Occupation.
• Profession (Grade).
• Field of Work.
• Country of Residence.
• Years of Experience.
• Relevant Experience in Lean Construction, BIM, and Emerging Technologies.

The evaluation statements are assessed using a Likert scale as follows: 1 = Strongly Disagree; 5 = Strongly Agree.

Table 9. Framework assessment statements.

ID	SEQUAL Quality Dimension	Question
Q1	Physical	The framework’s structure is clear and well-documented, making it easy to understand and apply.
Q2	Physical	The framework effectively visualizes the interactions between BIM uses, Lean tools, and Emerging Technologies, making it easy to interpret.
Q3	Empirical	The framework is written and presented in a way that ensures ease of understanding for both construction professionals and researchers (clarity of the explanation and language used), regardless of prior familiarity with similar methodologies.
Q4	Empirical	The framework contains sufficient details (e.g., diagrams, models, examples) for direct practical application.
Q5	Syntactic	The framework uses standardized industry terminology, ensuring consistency in definitions and concepts.
Q6	Syntactic	The framework facilitates a structured workflow by aligning BIM, Lean tools, and Emerging Technologies in a way that enhances efficiency and decision making.
Q7	Semantic	The selection of BIM uses, Lean tools, and Emerging Technologies is relevant and applicable to modern construction challenges.
Q8	Semantic	The framework captures key dependencies and interactions between Lean Construction, BIM, and Emerging Technologies.
Q9	Semantic	The framework effectively addresses common industry challenges such as cost overruns, project delays, and inefficiencies.
Q10	Pragmatic	The framework is too complex to be practically implemented in real-world projects.
Q11	Pragmatic	The proposed BIM, Lean tools, and Emerging Technologies are appropriately selected for each construction phase.
Q12	Pragmatic	The framework offers a structured approach to optimizing construction performance in terms of the selected KPIs: cost efficiency and savings, time efficiency and delivery, productivity and resource utilization, waste reduction, quality and safety, stakeholder satisfaction and collaboration and process optimization and automation.
Q13	Social	The framework promotes collaboration and coordination between stakeholders (e.g., project managers, contractors, clients, designers...).
Q14	Social	The framework facilitates better decision-making processes by ensuring transparent information flow.
Q15	Social	The framework is not flexible enough to be applied across different construction project types (residential, infrastructure, industrial, etc.).
Q16	Deontic	The framework aligns with the best international practices, industry regulations, and compliance standards for construction management, ensuring regulatory adherence.
Q17	Deontic	The framework effectively identifies and mitigates project risks, uncertainties, and performance constraints, supporting proactive decision making.
Q18	Deontic	The framework supports sustainable construction practices, ensuring lifecycle efficiency and reducing resource waste.
Q19	Deontic	The integration of technologies within the framework enhances automation, digitalization, and real-time decision making in construction projects.
Q20	Deontic	The framework is flexible and scalable, allowing for modifications and adjustments based on project size, complexity, and evolving technological trends.
Q21	Deontic	The framework serves as a solid foundation for future research, enabling continued development in construction management.

states the questions’ statements.

Once responses are collected, a quantitative and qualitative analysis is conducted as follows:

• Non-parametric statistical analysis: (Wilcoxon signed-rank test in SPSS) will be conducted to determine statistical significance [76].
• Consensus analysis: The level of agreement across experts will be assessed to determine the framework’s acceptability [73].
• Qualitative insights: Open-ended feedback from experts will be analyzed to refine and improve the framework [73].

This structured approach ensures that the framework is rigorously evaluated, refined based on expert insights, and validated for industry-wide applicability.

5.3. Expert Assessment

An evaluation of the proposed framework was conducted through an expert review aimed at addressing questions Q1 to Q22. The panel comprised both academic and industry professionals who met the following two main eligibility criteria: over 10 years of experience in the field, and proven expertise in Lean Construction, Building Information Modeling (BIM), and Emerging Technologies. A total of 20 experts were invited to contribute to the study, of whom 12 agreed to participate. Participants were presented with the framework’s structure, components, and intended application for review and assessment with a 5-point Likert-scale questionnaire (1 = Strongly Disagree, 5 = Strongly Agree).

Table 10. Summary of expert participants’ profiles.

N°	Occupation	Profession (Grade)	Field of Work	Country of Residence	Experience
1	Lean Construction Consultant, Project Manager	Architect, MSc, MBA	Lean Construction, BIM, Design, and Construction Management	Spain	+15 years
2	Lean Construction Consultant	Civil Engineer, PhD	Lean Construction, BIM	United States	+15 years
3	Director of Study and Design, Consultant	Architect, PhD	Lean Construction, BIM, Design, Construction Management	Saudi Arabia	+20 years
4	Engineering Director, Consultant	Architect, PhD	Lean Construction, BIM, Construction Management and Automation	Saudi Arabia	+20 Years
5	Academic, Consultant	Architect, PhD	BIM, Construction Management	Saudi Arabia	+20 years
6	Academic, Chairman of Architectural Engineering, Consultant	Construction Management, PhD	Strategic Project Management	Saudi Arabia	+20 years
7	Academic	Civil Engineer, PhD	Lean Construction 4.0	Chile	+15 years
8	Academic, Consultant, Owner Representative	Construction Management, MSc, PhD candidate	Lean Construction, BIM, Technological Expert	United State	+25 years
9	Head of the Department of BIM Infrastructure, GIS, Mapping, Consultant	Civil Engineer, PhD, MBA	Lean Construction, BIM, GIS, Construction Management	Germany	+10 years
10	Director of PhD engineering program, Consultant	Civil Engineer, PhD, MBA	Construction 4.0	Chile	+25 years
11	Academic, Consultant	Building Engineer, PhD	Knowledge and construction management	Italy	+10 years
12	Academic, Consultant	Civil Engineer, MSc	Lean Methods Implementation, BIM Methods, Digitalization	Spain	+40 years

outlines the professional profiles of the participating experts.

To enhance the representativeness of the results, the expert panel was purposefully selected to reflect a diverse distribution across professional roles, sectors, and geographies. Of the twelve experts, six are industry-based professionals, including directors, consultants, and Lean/BIM managers, while six are affiliated with academia, providing a balanced academic–industry perspective. The panel also reflects geographical diversity, with members from Europe (Spain, Germany, and Italy), the Middle East (Saudi Arabia), North America (USA), and South America (Chile). Professionally, the participants represent the architecture, civil engineering, construction management, and digital innovation domains, ensuring comprehensive expertise in Lean Construction, BIM, and Emerging Technologies. This multidisciplinary and multi-regional representation strengthens the external validity of the framework evaluation.

The expert panel comprised 12 professionals, a sample size consistent with Delphi studies in construction and management research, where a range between 10 and 18 participants is often sufficient to reach consensus and derive meaningful insights [77,78]. Moreover, this sample size is considered adequate for applying non-parametric tests such as the Wilcoxon signed-rank test and parametric procedures like the one-sample t-test in expert-based framework evaluations [79].

6. Results and Discussion

6.1. Reliability Analysis

To ensure the reliability of the questionnaire used to evaluate the proposed framework, a reliability analysis was conducted using Cronbach’s Alpha. As shown in

Table 11. Reliability statistics.

Reliability Statistics
Cronbach’s Alpha	N of Items
0.757	21

, the scale, which consists of 21 questions, demonstrated acceptable internal consistency, yielding a Cronbach’s Alpha value of 0.757 [80]. This suggests that the scale is sufficiently reliable for measuring perceptions of the framework’s effectiveness.

6.2. Descriptive Statistics (One-Sample Statistics)

A descriptive analysis was conducted to examine participants’ perceptions of the framework. The analysis was based on responses from 12 participants.

Table 12. Descriptive statistics.

Question	N	Mean	Std. Deviation
1	12	4.17	0.577
2	12	4.17	0.577
3	12	4.17	0.835
4	12	4.33	0.651
5	12	4.75	0.452
6	12	4.00	0.853
7	12	4.33	0.651
8	12	4.25	0.754
9	12	4.42	0.669
10	12	1.83	0.718
11	12	3.92	0.793
12	12	4.17	0.577
13	12	4.33	0.778
14	12	4.50	0.674
15	12	1.75	0.866
16	12	4.08	0.669
17	12	4.17	0.718
18	12	4.33	0.492
19	12	4.75	0.452
20	12	4.67	0.492
21	12	5.00	0.000

summarizes the one-sample statistics including the mean and standard deviation for each question.

Most items achieved a mean score above 4.00 on a 5-point Likert scale, indicating a generally favorable evaluation of the framework [81]. The highest-rated statement, “The framework serves as a solid foundation for future research”, received a perfect score of 5.00 with a standard deviation of 0.000, demonstrating unanimous agreement. Other highly rated items include the use of standardized industry terminology (M = 4.75, SD = 0.452), the integration of technologies to enhance automation (M = 4.75, SD = 0.452), and the flexibility and scalability of the framework (M = 4.67, SD = 0.492). These scores indicate strong confidence in the clarity, technological relevance, and adaptability of the proposed framework.

Conversely, two reverse-coded items addressing potential concerns about complexity and flexibility received low mean scores, suggesting that the majority of respondents did not view these aspects as problematic. Specifically, the statement “The framework is too complex to be practically implemented in real projects” scored a mean of 1.83 (SD = 0.718), while “The framework is not flexible enough to be applied across different scenarios” scored 1.75 (SD = 0.866). These items were intentionally reverse-phrased to detect negative perceptions; hence, low scores here indicate strong disagreement with the negative statements. This confirms that experts largely perceived the framework as both practically implementable and adaptable across diverse project contexts. Therefore, while the numerical values are low, their interpretation supports the framework’s strengths, not its weaknesses.

6.3. One-Sample t-Test

A one sample t-test was conducted to statistically verify whether the mean scores of each item differed significantly from a neutral test value of 3. The choice of this parametric test was justified as the data met the normality assumption [82] (Shapiro–Wilk test, p > 0.05), and the sample size (N = 12) was adequate for the central limit theorem to apply [81]. As detailed in

Table 13. One-sample t-test.

Question	Test Value = 3
	t	df	Sig. (2tailed)	Mean Difference	95% Confidence Interval of the Difference
					Lower	Upper
1	7.000	11	0.000	1.167	0.80	1.53
2	7.000	11	0.000	1.167	0.80	1.53
3	4.841	11	0.001	1.167	0.64	1.70
4	7.091	11	0.000	1.333	0.92	1.75
5	13.404	11	0.000	1.750	1.46	2.04
6	4.062	11	0.002	1.000	0.46	1.54
7	7.091	11	0.000	1.333	0.92	1.75
8	5.745	11	0.000	1.250	0.77	1.73
9	7.340	11	0.000	1.417	0.99	1.84
10	5.631	11	0.000	1.167	1.62	0.71
11	4.005	11	0.002	0.917	0.41	1.42
12	7.000	11	0.000	1.167	0.80	1.53
13	5.933	11	0.000	1.333	0.84	1.83
14	7.707	11	0.000	1.500	1.07	1.93
15	5.000	11	0.000	1.250	1.80	0.70
16	5.613	11	0.000	1.083	0.66	1.51
17	5.631	11	0.000	1.167	0.71	1.62
18	9.381	11	0.000	1.333	1.02	1.65
19	13.404	11	0.000	1.750	1.46	2.04
20	11.726	11	0.000	1.667	1.35	1.98

, all items showed statistically significant deviations from neutrality (p < 0.05). Positive t-values indicate strong agreement with the statements, with the largest t-values observed in items related to standardized terminology, technology integration, and decision-making support. Notably, the negative phrasing of items regarding complexity and flexibility also yielded significant results, reaffirming that respondents did not perceive these as limitations.

Although slightly lower in t-values compared to the highest-rated components, the following aspects still reflect strong agreement among respondents as follows: the framework’s effectiveness in addressing common industry challenges (t = 7.340, p = 0.000, Mean Difference = 1.417), capturing key dependencies and interactions (t = 5.745, p = 0.000, Mean Difference = 1.250), and presenting sufficient detail through diagrams and models (t = 7.091, p = 0.000, Mean Difference = 1.333). These results suggest a consistently positive perception, albeit with slightly more variation in participant responses. The 95% Confidence Intervals (CIs) for all statements do not include 0, confirming that the differences from the test value of 3 are statistically significant. Specifically, positive mean differences, reflected in the confidence intervals, with both bound above zero, reinforce the framework’s perceived strengths across various dimensions. Conversely, negative mean differences, where both CI bounds are below zero, indicate a clear rejection of concerns related to the framework’s complexity and lack of flexibility. This one-sample t-test provides strong evidence that the framework is well perceived across multiple dimensions.

6.4. Nonparametric Validation (Wilcoxon Signed-Rank Test)

The Wilcoxon signed-rank test was employed as a nonparametric alternative to validate the t-test results, addressing potential outliers or minor violations of normality. All items showed significant differences from neutrality (p < 0.05), confirming the robustness of the findings. Nonparametric tests, being particularly useful for small samples like this study, offer a conservative check on the results [79].

Table 14. Hypothesis Test Summary.

Null Hypothesis	Test	Sig.	Decision
The median of Q1 equals 3.000.	One-Sample Wilcoxon Signed-Rank Test	0.002	Reject the null hypothesis
The median of Q2 equals 3.000.		0.002
The median of Q3 equals 3.000.		0.006
The median of Q4 equals 3.000.		0.003
The median of Q5 equals 3.000.		0.001
The median of Q6 equals 3.000.		0.010
The median of Q7 equals 3.000.		0.003
The median of Q8 equals 3.000.		0.004
The median of Q9 equals 3.000.		0.003
The median of Q10 equals 3.000.		0.004
The median of Q11 equals 3.000.		0.009
The median of Q12 equals 3.000.		0.002
The median of Q13 equals 3.000.		0.004
The median of Q14 equals 3.000.		0.002
The median of Q15 equals 3.000.		0.005
The median of Q16 equals 3.000.		0.004
The median of Q17 equals 3.000.		0.004
The median of Q18 equals 3.000.		0.001
The median of Q19 equals 3.000.		0.001
The median of Q20 equals 3.000.		0.001
The median of Q21 equals 3.000.		0.001

summarizes the hypothesis test.

Each hypothesis test in the table assesses whether a specific aspect of the framework has a median score of 3.000 (indicating a neutral rating). The decision to reject or retain the null hypothesis is based on the significance (Sig.) value. All p-values are below 0.05, demonstrating that the differences between the observed medians and the neutral value of 3.000 are statistically significant. Consequently, the null hypothesis was rejected for all items, indicating that participants rated each aspect of the framework significantly different from neutral. Given that the earlier one-sample statistics table reported mean scores predominantly above 4.000, these results collectively suggest that the framework was generally perceived positively.

6.5. Correlation Analysis (Spearman’s Rho)

Spearman’s rho correlation matrix, analyzing the relationships between various framework attributes, demonstrates internal consistency [83]. Strong positive correlations emerged between key constructs as follows: Q1 (framework clarity/documentation) and Q11 (Selected BIM, Lean Tools, and Emerging Technologies) (ρ = 0.826) highlighted that a well-structured framework directly facilitates appropriate BIM, Lean, and Emerging Technologies adoption, while Q20 (flexibility/scalability) and Q19 (automation enhancement) (ρ = 0.816) suggest that perceived scalability is closely tied to technological integration and operational efficiency. Similarly, the link between Q4 (ease of understanding) and Q15 (cross-project applicability) (ρ = 0.738) reinforced that clarity drives practical utility. A cohesive cluster of moderate correlations connected Q2 (visualization), Q5 (terminology), Q7 (workflow alignment), Q11, and Q14 (decision making), suggesting these elements collectively support framework efficacy. Notably, Q13 (collaboration) and Q12 (process optimization) correlated moderately with multiple items, emphasizing their integrative role. Overall, Spearman’s Rho analysis supports the construct validity of the framework, with clear clusters of interrelated items and a general pattern of positive associations, especially among core functional and strategic dimensions.

Table 15. Spearman correlation matrix.

	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19	Q20
Q1	1.000
Q2	0.490	1.000
Q3	0.557	0.336	1.000
Q4	0.624	0.341	0.126	1.000
Q5	0.536	0.536	0.624	0.310	1.000
Q6	0.355	0.195	0.110	0.000	0.236	1.000
Q7	0.624	0.624	0.359	0.632	0.310	0.247	1.000
Q8	0.336	0.336	0.356	0.311	0.030	0.431	0.082	1.000
Q9	0.131	0.131	0.269	0.385	0.310	0.642	0.219	0.135	1.000
Q10	0.142	0.064	0.354	0.263	0.426	0.452	0.382	0.252	0.336	1.000
Q11	0.826	0.448	0.726	0.236	0.564	0.268	0.572	0.467	0.226	0.175	1.000
Q12	0.490	0.184	0.163	0.393	0.168	0.160	0.624	0.304	0.101	0.395	0.407	1.000
Q13	0.284	0.408	0.256	0.153	0.061	0.420	0.378	0.470	0.157	0.242	0.167	0.661	1.000
Q14	0.287	0.525	0.372	0.044	0.477	0.270	0.264	0.558	0.007	0.026	0.389	0.343	0.396	1.000
Q15	0.341	0.341	0.238	0.738	0.310	0.181	0.370	0.542	0.123	0.076	0.054	0.110	0.153	0.224	1.000
Q16	0.204	0.031	0.023	0.081	0.220	0.150	0.452	0.292	0.456	0.374	0.174	0.031	0.155	0.110	0.160	1.000
Q17	0.188	0.307	0.451	0.076	0.426	0.129	0.263	0.173	0.025	0.058	0.337	0.307	0.208	0.731	0.157	0.052	1.000
Q18	0.154	0.431	0.082	0.171	0.408	0.000	0.028	0.221	0.114	0.168	0.055	0.092	0.112	0.584	0.171	0.462	0.335	1.000
Q19	0.201	0.503	0.119	0.589	0.333	0.000	0.372	0.030	0.465	0.122	0.089	0.201	0.243	0.572	0.248	0.220	0.426	0.408	1.000
Q20	0.246	0.523	0.082	0.655	0.000	0.217	0.456	0.055	0.570	0.112	0.109	0.246	0.335	0.380	0.342	0.173	0.168	0.500	0.816	1.000

demonstrates the correlation analysis.

Overall, these results support the validity and perceived effectiveness of the proposed framework in facilitating a more integrated and efficient approach to construction process optimization through the convergence of BIM, Lean Construction, and Emerging Technologies.

6.6. Expert Feedback and Framework Refinements

In addition to the quantitative evaluation, experts provided open-ended feedback (Q22) to enhance the framework’s real-world applicability. Their insights highlighted several actionable areas for refinement. First, multiple experts stressed the need for real-world validation, recommending that future research includes case studies across various project types to assess the framework’s scalability and universality. Second, the importance of training and workforce readiness was emphasized, including the involvement of Lean-trained personnel and employee assessments to support effective framework adoption. Economic feasibility was also raised as a concern, with one expert questioning the return on investment. This led to the recommendation of integrating a cost-modeling component to evaluate the economic viability of implementing emerging technologies compared to traditional methods. Additionally, issues of standardization and regulatory alignment were noted, particularly concerning software interoperability among stakeholders and variations in regional regulations. One expert also criticized the limited vertical integration within the framework and recommended strengthening phase-specific workflows beyond horizontal BIM–Lean–Technology links. Experts unanimously stressed the importance of practicality, recommending modular implementation, cost–benefit toolkits, and real-world case studies, while affirming the framework’s theoretical robustness. These insights will guide future refinements, prioritizing pilot deployments, workforce training, and regulatory alignment to bridge the gap between theory and practice.

6.7. Influencing Factors on Framework Validity

While the framework demonstrated high expert agreement and robust statistical support, its validity is influenced by several interrelated factors. First, the expertise and background diversity of evaluators play a critical role in shaping feedback quality; professionals with industry experience may prioritize practicality, while academics may emphasize theoretical coherence. Second, project typology and scale, residential status, infrastructure, or mega-projects, can affect the relevance and adaptability of specific Lean, BIM, or Technology tools integrated within the framework. Third, regional context, including technological infrastructure, digital maturity, and regulatory requirements, impacts implementation feasibility. For instance, regions with limited digital adoption may encounter integration challenges despite the framework’s structured approach. Lastly, the stage of the project lifecycle at which the framework is applied may influence its perceived utility, with higher impact expected when adopted early (Plan or Design phases). These factors must be considered when interpreting the framework’s applicability and generalizability.

7. Conclusions

The integration of Lean Construction, Building Information Modeling (BIM), and Emerging Technologies proposed in this research provides a comprehensive approach to optimizing construction management across all lifecycle phases. This research builds directly upon a previous systematic literature review conducted by the authors, which identified the foundational tools and integration challenges related to Lean Construction, BIM, and Emerging Technologies. While the earlier study focused on mapping and analyzing the academic landscape, this paper addresses the observed gap by developing and validating a comprehensive integration framework structured across the full construction lifecycle. In doing so, it translates theoretical findings into a practical and actionable model for optimizing project delivery. By categorizing interactions clearly and illustrating synergies through a structured framework, this study clarifies how these combined methodologies enhance efficiency, reduce waste, and improve collaboration among stakeholders. Previous frameworks predominantly focused on either integrating BIM with Lean or separately exploring Emerging Technologies. In contrast, this research presents a uniquely holistic and systematically structured integration approach aligned with distinct lifecycle phases, validated through rigorous methodologies such as the Design Science Research Methodology (DSRM). This structured approach provides clear guidelines and actionable insights, enabling industry professionals to implement integrated strategies effectively and systematically. Furthermore, the framework’s division into distinct phases, each comprising specific steps, offers stakeholders valuable flexibility, allowing partial or incremental adoption based on specific project requirements or constraints. Despite its theoretical robustness and positive validation through expert consensus, certain limitations warrant consideration. Real-world applications and comprehensive empirical validation across diverse construction contexts are needed to fully assess practical effectiveness and scalability. Additionally, dependency on advanced technologies raises potential barriers related to workforce preparedness and necessary skillsets, highlighting the importance of targeted training programs and capacity-building initiatives. To address these limitations, future research should emphasize empirical validation through detailed case studies spanning various construction project types to confirm the framework’s adaptability and efficacy. Furthermore, economic feasibility studies and cost–benefit analyses are recommended to provide stakeholders with clearer insights into financial implications and facilitate informed decision making. Exploring software interoperability and standardization strategies is also crucial, ensuring seamless integration among Lean tools, BIM platforms, and Emerging Technologies, thereby overcoming the interoperability challenges highlighted by industry experts. The results of this study suggest that the developed framework has the potential to address persistent inefficiencies in construction management by promoting the early alignment of stakeholders, integration of emerging technologies, and lifecycle coordination. Expert evaluations confirmed its conceptual robustness. Although the framework has not yet been tested on site, it is structured in a way that aligns with current project delivery demands in fast-developing regions. The phase-based modular design allows flexibility and scalability across project types, particularly large-scale infrastructure and smart city initiatives. These attributes enhance the framework’s readiness for real-world piloting and contribute to ongoing efforts to improve cost control, waste reduction, and digital collaboration in construction.