As more and more companies are turning to IoT and big data, digital twin technology is gaining traction and improving manufacturing operations.
Fremont, CA: A digital twin is a virtual duplicate of an object, product, piece of equipment, process, supply chain, or even an entire business environment. It is built utilizing information derived from the Internet of Things (IoT) sensor technology attached to or incorporated into the original object. This cloud-connected data enables structural and operational views of the object's behavior in real-time, enabling engineers to monitor systems and analyze system dynamics. Adjustments can be made to the digital twin to determine how the system would alter in the actual world before modifying the original system. Quality, supply chain management, predictive maintenance, and customer experience are all being improved by digital twins in manufacturing.
The role of digital twins in manufacturing:
Helps improve quality management: Monitoring and responding to data from the Internet of Things (IoT) sensors throughout manufacturing is crucial for preserving quality and minimizing rework. The digital twin can mimic every aspect of the production process to determine where deviations arise and whether or not better materials or methods can be implemented.
Improved product design: During the design phase, digital twins can serve as virtual prototypes and be modified to test different simulations or designs before investing in a physical prototype. This reduces the number of iterations required before putting the product into production, saving time and money.
Enhanced supply chain management: Digital twins are utilized by supply chains and logistics/distribution companies to measure and analyze key performance indicators, including packaging performance, fleet management, and route efficiency. Essentially, they are useful for optimizing just-in-time or just-in-sequence production and assessing distribution routes.
Optimization of processes: A production line's sensors can be utilized to generate a digital twin of the process and assess key performance indicators. Adjustments to the digital twin can reveal new methods for optimizing production, minimizing variations, and facilitating root-cause investigation.