Modeling Quality and Waste Management Using System Dynamics in the Internet of Things

Introduction: The impact of the Internet of Things (IoT) on product quality, due to the complexities of production processes and the multitude of influencing factors, requires thorough and comprehensive examination. IoT technology enables organizations to closely monitor their various processes, providing real-time and accurate information on the status of equipment and products. This study aims to investigate the effects of IoT on waste reduction and the improvement of equipment performance in various industries. Accordingly, it focuses on identifying key variables influencing production processes and modeling their behavior.

Methods: To analyze the behavior of key variables, the system dynamics method was utilized. This approach allows us to simulate changes in variables over time and examine the complex relationships between them. Given the large number of variables influencing product quality, mathematical modeling methods appear to be unsuitable. Therefore, leveraging simulation and complex systems analysis methods, which are capable of examining nonlinear relationships and interactions between variables, is a more effective solution. After developing a causal loop diagram based on key variables influencing product quality, waste, and cost, a stock-and-flow diagram was created. The model was validated through sensitivity testing and boundary condition analysis. Following the analysis of results, improvement scenarios were proposed and evaluated.

Results and discution: The validity of the simulated model in this research was confirmed following the implementation of sensitivity testing and boundary condition analysis. The findings of this study indicate that product waste, after reaching a peak at approximately 20 months, gradually decreases over time. This gradual decline may result from improvements in production processes, learning and experience in waste management, or the implementation of preventive measures. The reduction in the risk of equipment failure and the costs associated with quality control also reflects improved system performance. Initially, the risk of equipment failure is high, but it decreases rapidly. Similarly, quality control costs trend toward zero by the end of the period. An exponential increase in demand and labor costs is also observed, highlighting the need for effective planning and management to meet this demand. This trend further indicates a direct relationship between demand, workforce requirements, and the associated costs. The increase in costs may be attributed to expenses related to hiring specialized labor and training personnel to acquire the necessary skills. The results demonstrated that installation and implementation costs show an exponential rise, reaching their maximum level by the end of the period. This increase could be due to the growing need for equipment and infrastructure related to the use of the technology. Additionally, the adoption rate increases over time.

Conclusion: The significance of this research lies in emphasizing the role of modern technologies, particularly the Internet of Things (IoT), in process optimization and cost reduction. The findings can assist managers and decision-makers in industries to better understand IoT's impacts, enabling more informed investments in technology and enhancing system quality and efficiency. This research also serves as a foundation for further studies on IoT applications in various industries, contributing to the development of effective solutions for performance improvement and waste reduction.