Agile Risk Management Model Based on the Internet of Things Using a System Dynamics Approach

Abstract

The present study aimed to design and present an agile risk management model based on the Internet of Things (IoT) using a system dynamics approach in order to improve organizational agility, resilience, and decision-making capability in complex and dynamic environments. Methods and Materials: This applied and descriptive-correlational study employed a mixed-methods approach combining qualitative and quantitative techniques. Data were collected through library research, structured interviews, and standard questionnaires. In the qualitative phase, 15 experts from Iran Khodro Company in Tehran participated until theoretical saturation was achieved. The statistical population in the quantitative phase consisted of industrial experts and specialists from large companies in Kerman Province selected through simple random sampling. Data validity and reliability were confirmed using content validity, Cronbach’s alpha, and the qualitative trustworthiness criteria of Guba and Lincoln. Data analysis was conducted using SPSS, SmartPLS, and Vensim software. The Delphi technique, Friedman test, Kendall’s coefficient, and system dynamics modeling including Causal Loop Diagram (CLD) and Stock and Flow Diagram (SFD) were applied to analyze relationships among variables and simulate system behavior over time. Findings: Inferential findings demonstrated that all identified indicators of agile IoT-based risk management were statistically confirmed by experts in both Delphi rounds (p < 0.05). Kendall’s coefficient increased from 0.703 in the first round to 0.912 in the second round, indicating strong consensus among experts. The most significant components included rapid identification of IoT-related risks, continuous monitoring of IoT service quality, integration of risks into agile development cycles, and rapid response to unexpected events. System dynamics simulation revealed that increased risk knowledge enhanced organizational resilience and agility through reinforcing feedback loops. Sensitivity and scenario analyses showed that increasing the rate of real-time risk data collection significantly improved resilience capacity and long-term system stability, whereas weak infrastructure and delayed responses reduced system adaptability. The findings further indicated that leverage points such as accumulated risk knowledge and resilience capacity had the greatest impact on improving overall system performance. Conclusion: The results confirmed that agile risk management in IoT ecosystems requires a systemic, dynamic, and integrated perspective that simultaneously addresses security, resilience, organizational agility, infrastructure quality, and rapid decision-making. The proposed model demonstrated that balancing reinforcing and balancing feedback loops is essential for sustaining organizational performance under uncertain conditions. The framework can serve as a practical and strategic reference for managers and researchers in digital supply chains and IoT-based project management environments.