An Integrated Framework for Balancing Workload and Capacity in Project-Based Organizations Using System Dynamics

Project-based organizations (PBOs) face persistent challenges in managing workload fluctuations that influence performance, competitiveness, and resource sustainability. Although previous research has explored bidding strategies and project inflows and outflows, few studies have systematically modeled workload-capacity dynamics or assessed policy responses to manage them effectively. To address this gap, this study develops a system dynamics (SD) model that integrates both pre-award and post-award project phases with internal and external organizational processes. Data for model development were drawn from the literature, industry reports, and expert interviews, resulting in the identification of 28 variables organized into subsystems covering demand, capacity planning, work execution, competitiveness, and financial performance. The model was validated through dimensional and structural tests, expert review, and further examined using social network analysis (SNA) and sensitivity analysis. The SNA results identified workload, production rate, and organizational capacity as the most influential variables. Sensitivity analysis conducted through Monte Carlo experiments, employing screening, regression, and ANOVA (analysis of variance) methods, revealed that capacity adjustment flexibility, minimum capacity, and demand level are critical factors influencing organizational stability. The validated model was then applied to evaluate policy alternatives under two distinct market conditions. Findings indicate that in lowest-price environments, a competitive, market-share-oriented policy enhances utilization and responsiveness, whereas in average-price markets, a stable capacity policy yields more sustainable outcomes. These results demonstrate how project-based organizations can strategically adjust bidding and capacity policies to stabilize workload dynamics and improve long-term operational resilience under different market conditions. The study contributes theoretically by extending the application of SD modeling to workload-capacity management in PBOs and contributes practically by offering a decision-support tool that helps managers assess capacity strategies, reduce risks, and align organizational policies with long-term sustainability objectives.