Process Operators and Tools May Bridge the Gap to Predictive Maintenance
Grant Gerke | February 28, 2017
Jim Wentzel, dir of Global Reliability at General Mills has been on the conference circuit recently and has been discussing “contextuality” when it comes to manufacturing data in the food industry. In his discussions, Wentzel discusses General Mills “data journey” as a company — their own plants and contract manufacturing plants outside the enterprise — and is pushing for data transparency throughout the entire enterprise eco-system. That means various types of plant and enterprise data, such as plant floor , instrument, machine vibration, supply chain and even other plants mixed together to make efficient decisions.
That means a lot of business units — and external companies per Wentzel— coming together and possible changes in workforce responsibilities. One scenario would be to have process operators provide key insights on equipment health due to a better working knowledge and lifecycle history of a particular asset.
Peter Reynolds, contributing analyst for ARC Advisory Group discusses this scenario with his most recent post, “Predictive Maintenance or Predictive Operations?” Reynolds describes how operations can lean on better tools, processes and how condition-based monitoring goes only so far:
Both Prognostics and Condition-based monitoring are still reactive approaches and have been used widely for decades. Still, many companies struggle with making significant improvements in predicting failures and extending the life of critical assets.
He goes on to write:
Therefore one might come to the conclusion that any predictive maintenance or asset reliability strategy might begin with an overarching operations strategy and weigh heavily on the skills of the process engineer. The process engineer (and not the maintenance and reliability engineer), has the ability to interpret the process data across the spectrum of the process and any assets.
The rub is that operations, maintenance and even IT need to view enterprise via data in one IIoT platform, such as ThingWorx, Element Analytics, or many other offerings that can provide varying analytics to different groups.