System Dynamics Modeling of Critical Chain Project Management in Multi-Project Engineering Environments

Abstract

This study examined the problem of persistent schedule instability, resource bottlenecks, rework cycles, buffer overconsumption, and weak dynamic control in multi-project engineering environments where several projects compete for shared engineers, technical teams, equipment, budgets, and decision-making resources. The purpose of the study was to assess how system dynamics modeling strengthens Critical Chain Project Management and improves project performance by supporting better understanding of feedback loops, delay propagation, resource constraints, buffer behavior, and project interdependence. A quantitative, cross-sectional, case-based research design was adopted, focusing on engineering enterprise cases where multiple projects were managed simultaneously under shared-resource conditions. The sample consisted of 220 respondents, including project managers, project engineers, planning engineers, operations and resource managers, technical supervisors, consultants, and project control professionals with direct experience in scheduling, resource allocation, CCPM practices, and engineering project execution. The key variables included system dynamics modeling, resource constraint management, buffer management, feedback-loop analysis, CCPM implementation, critical chain buffer consumption control, and project performance. The analysis plan used descriptive statistics, reliability testing, Pearson correlation, multiple regression, and hypothesis testing through SPSS, with Likert-scale data supported by conceptual system dynamics visualization. The headline findings showed high agreement across all major variables: CCPM implementation recorded the highest mean score of 4.20, followed by system dynamics modeling at 4.18, feedback-loop analysis at 4.15, project performance at 4.13, resource constraint management at 4.11, and buffer management at 4.07. Reliability was strong, with Cronbach’s alpha values ranging from 0.83 to 0.88 and an overall alpha of 0.91. Correlation results showed that CCPM implementation had the strongest association with project performance, r = 0.71, p < 0.001. Regression analysis explained 64% of the variance in project performance, R² = 0.64, adjusted R² = 0.63, with CCPM implementation as the strongest predictor, β = 0.31, p < 0.001, followed by system dynamics modeling, β = 0.27, p < 0.001. The findings imply that engineering organizations should integrate dynamic feedback analysis, constraint-based planning, resource prioritization, and buffer monitoring to improve schedule reliability, project control, resource utilization, and delivery performance in complex multi-project environments.