17 08

As manufacturers become increasingly connected, their systems, machines, sensors and other devices are generating a wealth of new data, and given the sheer volume of data generated, that isn’t easily analyzed.

It is a challenge that traditional manufacturing systems are not designed for – and manufacturers are missing out on valuable insights as a result.

Machine learning (ML) and Artificial Intelligence (AI) technology can help, when implemented in support of an IoT strategy and validated through a strategic experiment that proves the potential value. 

Enterprises can integrate predictive maintenance models into their manufacturing systems to actively monitor asset health & send alerts at optimal maintenance periods.

Then going one step further, AI and model predictive control techniques can be implemented to automatically set the appropriate machine parameters allowing operators to focus on more pressing needs to keep a manufacturing line running optimally.

End-to-end automation provides an overall increase in labor productivity and helps plants operate at their optimal maintenance cost.

For example, the predictive models integrated with Computerized Maintenance Management Systems (CMMS) can trigger automated work orders based on production schedules, resource availability and machine health conditions – a true end-to-end solution.

Plant management derives value through production planning, asset lifecycle costing, improved throughput and resource allocation optimizations.

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