Digitalization has disrupted the manufacturing industry, making it imperative for companies to find ways to drive increased value through innovation. Already, most manufacturers routinely use the digital thread to track, test, simulate and optimize their products from ideation through production. This is done through the product digital twin and production digital twin – virtual representations of the product and production line, respectively.
Predicting, based on historical data, how a product will perform and how a production line will operate not only saves on building physical prototypes, but the data can be leveraged to refine aspects of the manufacturing process based on inputs, such as material costs and line utilization to determine efficient and cost-effective processes.
While this boosts productivity and efficiency, is this enough to stay competitive? Not anymore. While these predictive digital twins are based on known factors and previous data, there are still unknowns when trying to accurately project how the product or process will act in production. The next step in the digitalization journey is to solve for these unknowns.
To stay competitive, manufacturers need to turn to the Industrial Internet of Things (IIoT) and the third digital twin it enables.
Closing the loop to optimize decision making
Cloud-based, open IIoT platforms supply the power for unparalleled data collection and analysis. With it, every sensor on every machine in every one of your production lines can quickly transmit their data to a centralized location. This data is then available for advanced analytics to drive quick, informed decision making.
In conjunction with the IIoT, manufacturers are able to implement a third digital twin, the digital twin of performance, to eliminate the unknowns and make near real-time production optimization decisions. The performance twin involves capturing and sending back live performance data of the production line and of product itself, at a customer location. This near real-time data allows engineers to determine if the production line and product behave as they were intended. If not, this information will quickly drive actionable insights and informed decision making back into the product and production line design.
Additionally, long-term data from the live production cycle can increase efficiency by feeding into other systems to help with things such as managing supply and bill of materials, reducing bottlenecks, and calculating the long-term productivity of the manufacturing equipment.
To learn more about the closed-loop digital twin, its impact on factory optimization, and the feasibility of getting started with it, watch this short webinar.