| Περίληψη: |
Engineering design phase delays persistently challenge construction projects, cascading into costly construction setbacks. Traditional static methods fail to capture dynamic feedback among schedule pressure, productivity, and design errors. A gap exists in applying system dynamics (SD) specifically to design-phase schedule control. This study develops an SD model to simulate schedule strategies, testing its efficacy as a virtual decision-making platform for proactive management. A comprehensive SD model integrating five subsystems (design process, target duration, human resources, performance, cost) was built using Vensim, validated through mental model verification and sensitivity analysis. It was applied to a petrochemical design case to simulate strategies such as overtime, recruitment, and schedule adjustments under various scenarios. Simulations revealed critical trade-offs: moderate overtime (1.25 factor) reduced the prolonged duration from 72 days to near the original plan but increased costs; midphase recruitment of L2 employees optimally balanced progress and budget; combined L3/L2 recruitment best mitigated senior staff turnover impacts. This research contributes to the body of knowledge by providing (1) a novel validated SD model tailored to design-phase dynamics, addressing a literature gap, and (2) a practical "virtual laboratory" enabling managers to proactively test strategies, bridging theoretical planning and real-world execution in construction management. Practical Applications: This study offers a practical tool for project managers to optimize engineering design schedules using system dynamics (SD) simulations. By modeling key factors such as human resources, design processes, and costs, the research demonstrates how managers can test strategies—such as overtime, schedule adjustments, or recruitment—before implementation. For example, simulations reveal that moderate overtime reduces project duration, but excessive overtime raises costs and lowers design quality. Similarly, recruiting midlevel (L2) employees early in the project balances progress and budget, whereas delaying adjustments to deadlines diminishes returns. The findings are particularly relevant for industries such as construction or petrochemicals, in which design delays cascade into costly construction setbacks. By leveraging the SD model as a "virtual decision-making platform," teams can proactively address risks such as employee turnover or design changes, ensuring projects stay on track. This approach bridges the gap between theoretical planning and real-world execution, empowering practitioners to make data-driven decisions that improve efficiency, reduce costs, and mitigate delays in complex engineering projects. [ABSTRACT FROM AUTHOR] |