Predictive Maintenance 4.0
Transforms Manufacturing

With IIoT, Industry 4.0, digital transformation, advanced analytics, Big Data, and the Cloud, it is still unclear what the factory of the future will look like

  • December 13, 2018
  • Stephan Romeder

But one place where all of these technological advances are coming together to reduce costs and increase efficiency today is predictive maintenance. Machines on the factory shop floor can now monitor and evaluate their own performance and even order their own replacement parts when necessary. By implementing predictive maintenance, manufacturers can improve safety, reduce downtime and extend equipment life.

Maintenance Evolves with IIoT

Manufacturers have a huge incentive to improve the efficiency and effectiveness of equipment maintenance. Poor maintenance can reduce a plant’s productivity by 5 – 20 percent and unplanned downtime costs U.S. manufacturers an estimated $50 billion each year.

Fewer interruptions in production also means more reliable product delivery, which is an important factor for keeping customers loyal. Higher customer retention can result in more revenues, which is another important side benefit of predictive maintenance.

Previously, manufacturers used preventive maintenance or servicing equipment based on expected wear and tear to prevent breakdowns. Predictive maintenance is significantly more efficient than preventive maintenance because corrective action is based on the actual condition of the equipment rather than how much time has passed. The goal is not to replace a part too early when it’s still in good condition, but instead only to service equipment when it’s really needed. It’s like bringing your car in for service based on fluid levels or belt thickness rather than the mileage.

The cost savings are huge. Predictive maintenance costs $9 hourly pay per year while preventive maintenance costs $13 hourly pay per year. In addition to fewer breakdowns, there can be less downtime by planning maintenance procedures in advance to coincide with factory shutdowns.

Predictive Maintenance Fueled by Data

Various advanced techniques including infrared thermal imaging, vibration analysis, and oil analysis can be used to predict failures. As a rule of thumb, seventy percent of machine-specific malfunctions can be predicted by using sensors to monitor and collect machine data and then using analytics to determine when equipment failures might occur.

When administrative procedures related to ordering and installing new parts are triggered automatically, cost savings can also be experienced in the back office. For example, a machine could sense that a drill bit is wearing out and automatically order a new one, alert the technical service department to send a field service representative, and forward the purchase request for a new part to the ERP system. By automating manual, error prone, labor intensive administrative functions manufacturers can experience an additional level of efficiency.

However, connecting the shop floor with the back office isn’t always easy. Machines generate data, but it is not always easy to access and evaluate this data within existing business processes. The challenge is integrating data streams from production plants into company applications.

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Machines, devices, sensors and people need to connect and communicate with one another seamlessly. There needs to be a virtual copy of the physical world in order to make sense of all the data to conceptualize the information. Technologies, such as artificial intelligence, need to be deployed to support decision making and problem solving making cyber systems as autonomous as possible.

There are also specific hurdles that need to be overcome. There needs to be ways to filter data so manufacturers’ proprietary information is kept private with 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 huge volumes of data with a very high level of performance and reliability.

When machine data can be used to perform predictive maintenance with a high level of precision, manufacturers can focus on differentiating products using digital capabilities like self-awareness of technical health. A manufacturer’s value can be measured not only by the quality of its shop floor processes, but also by how it protects its assets. This can be achieved by using predictive maintenance to extend equipment life and improve the efficiency of maintenance procedures.

Predictive maintenance is an essential part of the factory of the future. Manufacturers who automate not only manufacturing processes but also equipment maintenance, can benefit from a whole new level of production efficiency.

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