For manufacturers to compete in industry 4.0, they need to get new products as well as existing product iterations out the door before competitors capture the market. Access to data empowers the digital twin to evolve and continuously update to reflect any changes to the physical counterpart throughout the product lifecycle. Creating a closed-loop of feedback in a virtual environment enables companies to continuously optimize their products, production and performance.
Within a closed feedback loop, manufacturers can augment data with process-based information from the entire array of systems that play a crucial role within today’s enterprise such as manufacturing execution systems, enterprise resource planning (ERP) systems, computer-assisted design (CAD) models, customer feedback, integrated product sensors and complex supply chain systems.
The best way to close the feedback loop is to establish a digital thread. A digital thread provides an understanding of why various aspects exist, including a structure for looking at a virtual product or simulation. It allows teams to answer questions such as:
- How do changes impact product performance?
- Why is the production team inspecting a specific component?
- What is that inspection telling them?
Being able to quickly answer these questions empowers the type of fast iteration discussions that enable manufacturers to meet evolving market demands.
A digital thread also automates verification management. For instance, during the design process, if some changes are not communicated down, teams may be working with information that is no longer accurate. This is all too common in complete manual environments, but it should never be a scenario when the manufacturer is leveraging a digital twin.
A key part of the feedback loop is to ensure that those data streams are then securely delivered for aggregation and ingestion into a modern data repository, followed by processing and preparation for analytics. Today’s entire array of data processing tools and capabilities, including edge computing and artificial intelligence, can play a meaningful role in the analysis process. Manufacturers can then apply these insights back to the production environment through additive manufacturing, robotics, or other tools utilizing decoders and actuators.