## Appendix A
### Full Set of Application Recommendations of the Proposed Framework
#### a.1.1 Plan Phase  
This subsection describes the framework's applicability during the Plan phase, continuing the discussion initiated in Section 4.2.1. Each stage is elaborated sequentially according to the corresponding rows in Table 7.  
TVD in Author Cost Estimation  
Figure 14 presents the implementation of Target Value Design (TVD), outlining a structured process to align project costs with client goals through phases such as goal definition, target costing, collaborative design, decision optimization, cost monitoring, and validation [84]. The process integrates BIM and Emerging Technologies to ensure cost efficiency, value delivery, and informed decision making. Defining Project Goals establishes client priorities (cost, sustainability, quality) through stakeholder workshops. Setting Target Costs breaks the budget into components using historical data and AI-driven predictions to align spending with value drivers. During Collaborative Design, stakeholders use BIM for real-time modeling and iterative refinement. Optimizing Design Decisions involves evaluating alternatives through BIM simulations and AI cost analysis to meet budget and value objectives, including prefabrication and material selection. Monitoring and Adjusting Costs uses BIM dashboards to track changes, supported by regular budget reviews and corrective actions like value engineering. Finally, Delivering and Validating reviews outcomes against goals, documenting lessons learned to improve future TVD applications. Key KPIs include cost efficiency through value-driven budgeting, improved time management via real-time tracking, enhanced stakeholder collaboration through transparency, and process optimization supported by BIM and AI. Waste reduction and quality improvements are secondary but positively affected. LPS in Author 4D Model  
Figure 11 illustrates the integration of the Author 4D Model (BIM), Last Planner System (LPS), and emerging technologies like Digital Twins and IoT to optimize construction planning and execution. Key steps include master scheduling, phase scheduling, lookahead planning, weekly work planning, commitment tracking, and continuous improvement. The 4D BIM model visualizes construction sequences, while LPS supports collaborative task management [85]. Digital Twins and IoT provide real-time insights and predictive updates to enhance workflow reliability. Commitment Tracking monitors progress through IoT sensors, with Digital Twin updates helping address schedule deviations. Learning and Improvement uses analytics (e.g., Percent Plan Complete) to identify inefficiencies for future optimization. KPI impacts include improved time efficiency through real-time adjustments, enhanced productivity via optimized task sequencing, and increased process automation through IoT and AI insights. Stakeholder collaboration is strengthened through 4D BIM visualizations, while cost efficiency benefits indirectly from improved planning and resource use. PDCA in Analyze Program Requirements  
Figure 14 demonstrates the integration of BIM, Lean's Plan-Do-Check-Act (PDCA) cycle, and Big Data Analytics for analyzing program requirements [86]. This iterative process involves defining goals, implementing designs, evaluating outcomes, and refining processes. BIM serves as a visual and analytical platform, while Big Data supports data-driven decision making, promoting continuous improvement, resource efficiency, and stakeholder collaboration. In the Plan phase, BIM and Big Data define spatial and operational needs, producing a data-driven program with measurable KPIs. In Do, initial design prototypes are developed in BIM, integrating energy and material data. Check evaluates design performance against KPIs, using analytics and stakeholder feedback to identify gaps. In Act, the BIM model is refined based on these insights, ensuring improved accuracy and alignment with project goals.