The technologies behind Industry 4.0 can open new doors for industry and manufacturing to increase efficiency, reduce costs, and improve quality. Combining new technologies will lead to even greater advancement. Let’s examine three possibilities: digital threads, predictive maintenance, and dark factories.
A digital thread is a set of connected records capturing the data and activities that define a product or, in some cases, a process. The thread closes the loop between the digital and physical worlds and can transform how products are designed, manufactured, and serviced. This is the next level up from a digital twin—instead of just the current state, the digital thread captures the entire lifecycle.
The thread starts in the digital world during product development with data such as CAD models or a bill of materials (BOM). The digital design is then manufactured, bringing it into the physical world. Throughout the product’s lifecycle, the digital thread record is continuously updated through technologies like IoT. What the product experiences and when and how it is serviced continue to update the record, which can also be passed on as the product goes from the manufacturer to the first owner and any subsequent owners. The thread can also provide valuable real-world usage and performance data back to the original designers to aid in future decisions around updates or redesigns.
A digital thread can help manufacturers see a bigger picture than digital twins alone can enable. Ideally, the digital thread data is broadly available for data analysis in cloud infrastructure to provide the greatest value and insights. An effective digital thread can improve product design, manufacturing processes, service, and maintenance.
Machines need to be maintained, or they can fail at inconvenient times, causing delays or outages. Predictive maintenance is meant to be an early warning system of a problem, using IoT or OT sensor data and AI to detect failure patterns in machinery and components. By understanding when a machine or part is likely to fail, manufacturers can take preventative action and maintain their equipment more effectively than regular maintenance intervals.
And this isn’t just limited to new equipment. Siemens has used such sensors on older motors and transmissions – and by analyzing the data from these sensors, Siemens says it can interpret a machine’s condition, detect irregularities and fix machines before they fail. (1) This shows how predictive maintenance processes can be applied even to legacy machinery.
Predictive maintenance can also be used in conjunction with digital thread to understand better when and how parts may fail. Sharing digital thread data with AI can improve predictions for just-in-time maintenance that is efficient and cost-effective.
With technologies like AI, digital twins, and cloud computing, machines are now capable of carrying out more and more tasks that were previously reserved for humans. This has enabled fully automated production lines where work happens without direct human intervention on-site, called dark factories or lights-out manufacturing. These automated factories can run 24 hours a day for increased productivity and accuracy at lower costs.
However, humans won’t be out of a job just yet. They’re needed to monitor the machines, perform maintenance, and inspect output quality. A dark factory is best suited to simple mass production of a standard product on a fixed schedule. Complexity and customization make full automation more difficult, leading some manufacturers to implement it for specific operations or shifts. (2)
If you’re looking to embrace Industry 4.0 and transform your business, Taos can help. They offer Advisory Services, Professional Services, Managed IT, and Security Services to help industrial enterprises grow their business by capitalizing on data-enabled decision-making, scalability, enhanced security, and cloud economics.
Learn more at https://www.taos.com/industries/industrial/
1 – The 10 Biggest Future Trends In Manufacturing, Forbes, January 2022
2 – What is a Lights-out Factory?, Siemens, retrieved May 2022