A System Dynamics–Optimization Framework for Human-Error-Aware Preventive Maintenance Planning

Maintenance activities are fundamental to reducing equipment failures and improving operational efficiency. Despite their critical role, the impact of human error on the effectiveness of both preventive and corrective maintenance remains insufficiently examined in the existing literature. This study addresses this gap by introducing a comprehensive and dynamic framework that integrates a System Dynamics (SD) model for quantifying Human Error Probability (HEP) with an optimization model designed to minimize total maintenance costs. By explicitly incorporating human error into maintenance planning and execution, the proposed approach identifies optimal maintenance levels and human-error-related variables, supporting more accurate and practical decision-making. The framework was validated through real-world industrial data and further examined using sensitivity analysis, confirming its robustness and practical relevance. The results indicate that an optimal HEP of 0.0204 provides an effective balance between operational performance and cost reduction. These findings offer actionable insights for maintenance managers to design strategies resilient to human error, ultimately enhancing equipment reliability, reducing unplanned downtime, and improving organizational performance. This integrated SD–optimization framework provides a novel human-centered perspective on maintenance planning and addresses a significant gap in maintenance management research.