Despite tremendous progress, the Industrial Internet of Things is still in its adolescence. Through advanced analytics, big data, edge computing and cloud computing, we know exactly what the factory will look like in the future, and how far manufacturers need to go to complete Industry 4.0. The journey of change.
However, all these advances come together to reduce costs and increase efficiency by predictive maintenance. Factory floor machines can monitor and evaluate their performance, even order replacement parts for themselves if necessary.
By implementing predictive maintenance, manufacturers can increase safety, reduce downtime, and extend equipment life.
Evolving maintenance case
Manufacturers have enough power to improve equipment efficiency and effectiveness. Poor maintenance can reduce plant productivity by 5-20%, while unplanned downtime is estimated to cost US manufacturers $50 billion annually.
Reducing production disruptions also means more reliable product delivery, helping to maintain customer loyalty. This higher customer retention rate can lead to more revenue.
Previously, manufacturers used preventive maintenance or repair equipment as expected to prevent failures. Predictive maintenance is more effective than preventive maintenance because corrective actions are closely related to the actual condition of the machine.
Our goal is not to replace a component too early – when it is still in good condition, but only when it is really needed. This is like car based on level or belt thickness, not mileage. Provide maintenance services.
By minimizing unnecessary maintenance and downtime, the cost savings potential is huge. Manufacturers' predictive maintenance costs average $9 per hour, while preventive maintenance costs are $13 (44% higher).
Data driven predictive maintenance
Advanced technologies, including infrared thermal imaging, vibration analysis and oil analysis, can be used to predict faults. Based on experience, 70% of equipment can be predicted by using sensors to monitor and collect machine data and then using automated analysis to determine when equipment failures can occur. malfunction.
The backend also saves money when automatically triggering management procedures related to ordering and installing new parts.
For example, the machine can sense bit wear and automatically order new drill bits, alert the technical department to dispatch field personnel, and forward purchase requests for new parts to the ERP system. By automating manual, error-prone, labor-intensive in this way Management functions, manufacturers can ensure greater efficiency.
However, connecting the shop floor to the back office is not that easy. Machines used in existing business processes may generate data, but the challenge is how to access and evaluate the data. The resulting data stream needs to be integrated into the company application.
Machines, devices, sensors, and people need to seamlessly connect and communicate with each other. A virtual copy of the physical operation (digital generation) is often needed to understand all the data and conceive information.
It may also be necessary to deploy technologies such as artificial intelligence to support decision making and problem solving, and to make the network system as autonomous as possible.
There are also some specific obstacles to overcome. Manufacturers' proprietary information needs to be kept confidential, use data filtering, and take additional security measures to protect financial and customer data from hackers.
Most importantly, any data management platform needs scalability to collect, filter, process, and share large amounts of data with high performance and reliability.
Future factory
When machine data can be used to perform high-precision IoT predictive maintenance, manufacturers can focus on using digital capabilities to differentiate products, such as technical health self-awareness.
The value of the manufacturer can be measured not only by the quality of its shop floor processes, but also by how it protects assets. This can be achieved by using IoT predictive maintenance to extend equipment life and increase the efficiency of maintenance procedures.
Predictive maintenance is an important part of the future plant. It not only automates the manufacturing process, but also automates equipment maintenance. This way, manufacturers can benefit from new levels of productivity.