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Predictive Maintenance Is On the Mind for Most Companies

Packaged goods companies are implementing or considering a variety of predictive maintenance technologies, and OEMs are looking for ways to match demand.

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PMMI Business Intelligence, 2022 Challenges and Opportunities for Packaging and Processing Operations

The majority of surveyed companies are currently considering, planning, or implementing predictive maintenance systems in some form, according to PMMI Business Intelligence’s 2022 report “Challenges and Opportunities for Packaging and Processing Operations.”

PMMI asked attendees at October’s PACK EXPO International to participate in a pre-show survey about plans for predictive maintenance in their operations. The results showed a variety of technologies sparking interest among consumer packaged goods companies, and a corresponding response among equipment manufacturers to meet demand.

The State of Predictive Maintenance

Current predictive maintenance activities among CPGs included implementing thermography solutions, full equipment monitoring and computerized maintenance management system software. OEMs noted an interest in adding predictive maintenance to their systems and testing it with customers.GraphicPMMI Business Intelligence, 2022 Challenges and Opportunities for Packaging and Processing Operations

“We are considering adding this as an optional feature on equipment,” one OEM said, noting their company is curious to learn how this would benefit packagers’ daily line operation and maintenance staff and budget.

Vibration analysis, thermography and holistic solutions such as equipment health monitoring sensors were popular among companies as potential technologies to implement in the next three to five years.

Other technologies popular among respondents included:

  • Parts room setup and organization
  • Oil monitoring analysis
  • Risk and reliability software
  • Machine level fault codes
  • Tracking hours of use until downtime
  • Outage planning and scheduled total productive maintenance
  • Risk minimization
  • Best practice information
  • Mean time between failures data
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