Intel Corporation’s Vice President Network and Edge Group and General Manager Federal & Industrial Solutions, Christine Boles, discusses the evolution of digital twins in manufacturing operations. Digital twins, virtual replicas of physical assets, have been optimizing manufacturing operations for over a decade by allowing manufacturers to simulate changes and predict behaviors in a virtual model. Advancements in open platforms, edge computing, and AI-powered analytics have led to the development of a new generation of digital twins that break down data silos and drive enterprise-wide data streams.
The digital twin has evolved from beneficial to essential with the integration of AI. AI technology enables dynamic 4D representations of physical assets, powered by multimodal sensors and powerful AI for comprehensive data analysis. These dynamic digital twins can power multi-sensor robots and make real-time decisions that impact the system immediately. By merging diverse data types with fast analytics, users can track and monitor operations as they happen, allowing for quicker and more accurate decision-making.
Dynamic digital twins have a wide range of applications in manufacturing, including quality control, asset and inventory tracking, worker safety, and asset health monitoring. These digital twins can learn, make decisions, and act on behalf of users, improving worker safety, detecting defects on production lines, and monitoring high-value assets in real-time. By continuously analyzing data from the factory floor, dynamic digital twins can detect issues and minimize unplanned downtime, leading to cost savings and improved efficiency.
The value of dynamic digital twins in manufacturing lies in real-time situational monitoring, “what if” modeling, and the identification of anomalies and system disruptions. Intel Corporation has seen its customers in all manufacturing settings use digital twins specific to their needs for factory design optimization, product development, robotics simulation, and equipment lifecycle modeling. Despite challenges such as data integration complexities and resistance to change, organizations are innovating and deploying dynamic digital twins to enhance workplace safety and equipment efficiency.
Christine Boles emphasizes the importance of identifying, understanding, and prioritizing use cases for digital twins in manufacturing to achieve the greatest ROI. As deploying dynamic digital twins becomes essential in manufacturing operations, focusing on real business value is crucial to unlocking their full potential. The integration of AI-powered digital twins empowers engineers and workers to enhance workplace safety and production equipment utilization and efficiency, showcasing the transformative impact of this technology in the manufacturing industry.
