Digital twin in manufacturing: use cases,
real-world examples & adoption guidelines

In addition to identifying product flaws during the design stage, digital twins can also be useful for detecting defects at the assembly stage. IoT sensors across the production line, including high-resolution cameras and CMMs, collect information on critical metrics (component dimensions, weld integrity, etc.) and stream it to the platform hosting the digital twin. The digital twin simulates changes based on the previously received data, with any deviation from expected parameters promptly flagged to the operator. The use of digital twins in product quality management also simplifies root cause analysis, as engineers can identify potential causes for specific defects, such as a worn mold, based on the source of deviating parameters.

Another popular use case involves using digital twin technology as virtual representations of specific manufacturing equipment, such as a pump or a mechanical arm, to spot anomalous equipment performance and conditions. IoT sensors collect real-time data on equipment temperature, vibration, acoustic emissions, and other metrics so that the digital twin can mirror the machine’s behavior, with any abnormalities in operation immediately visible. Additionally, manufacturing predictive analytics software powered by machine learning models can process this information to identify outliers that can be a sign of degradation, forecast the likelihood and timing of component failure, and trigger a maintenance request if needed.

Digital twins are often deployed at scale, serving as virtual replicas of an entire production line, manufacturing cell, or even a factory. This way, the technology helps companies get a high-level overview of their large, integrated production systems and track manufacturing processes end-to-end to identify bottlenecks and areas for improvement. Such digital twins also enable manufacturers to simulate and validate potential process adjustments, such as changes in production scheduling or facility layout, predicting their impact on production processes before implementing them. Supply chain changes and new logistics scenarios can be simulated the same way to optimize operations and validate their readiness for various disruptions.

Digital twins are excellent workforce development tools, allowing companies to train their employees in a highly realistic but risk-free virtual environment. Combined with AR and VR technology, digital twins simulate workplace scenarios where new operators, maintenance staff, and other trainees can safely interact with virtual machinery and thus practice their tasks.