Resilient Production Control Using Digital Twins in the Industrial Internet of Things

Through the development of digital twins, the Industrial Internet of Things (IIoT) has fundamentally changed how the physical and digital worlds are integrated. The purpose of this study is to examine how digital twins can improve production control in smart consumer electronics factories. In particular, the study examines how well digital twins work as a production control tool and suggests a novel strategy that combines real-time data, sophisticated analytics, and predictive capabilities. The proposed method uses a digital twin-based methodology to model and simulate the behavior of the production system in real-time, enabling the early identification of potential problems and proactive decision-making. The proposed method optimizes resource utilization, lowers energy consumption, and adapts to changing conditions by using cutting-edge algorithms and machine-learning techniques. However, existing approaches face limitations in energy efficiency, scalability, reliability, robustness, cost-effectiveness, and interoperability. Addressing these challenges is essential to advance production control in smart consumer electronics factories.