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Amid economic uncertainty, there’s an increased need for transparency and predictability regarding maintenance management as manufacturers work to streamline processes and reduce overhead costs. To provide support and guidance for the manufacturing industry, Info-Tech Research Group has published a new report, Operational Efficiency Through Predictive Maintenance.

As many operations organizations lack visibility throughout the production process, there’s a demand for the enablement of better technology support to reduce downtime, avoid run-to-failure, and mitigate the ripple effects of catastrophic outages when equipment or assets go down. The new report supports operations leaders in their endeavors to improve maintenance planning.

Predictive Maintenance
Kevin Tucker, Principal Research Director/Manufacturing Research, Info-Tech Research Group

“Predictive maintenance is a powerful addition to a thorough, all-encompassing strategic plant maintenance program,” says Kevin Tucker, principal research director of manufacturing research at Info-Tech Research Group. “It is revolutionizing maintenance by avoiding unexpected and catastrophic failures that impair productivity, lower customer satisfaction, and drive up costs for services such as repairs and quality problems.”

Predictive maintenance uses data analysis tools and procedures to enable proactive management of technology, foreseeing issues before they arise, according to the report. Enabling this maintenance management method also prevents unscheduled reactive maintenance and incurring costs for excessive amounts of preventive maintenance.

Predictive Maintenance
A variety of maintenance approaches play a role in the evolution to predictive maintenance and beyond. These include prescriptive maintenance, predictive maintenance, condition-based maintenance, preventive maintenance, and reactive maintenance. (CNW Group/Info-Tech Research Group)


In the report, Info-Tech provides a breakdown of the tools to employ in the data collection and analytics of predictive maintenance, highlighting that the key to success lies within using the right tools to capture the correct real-time conditions and provide predictive monitoring data.

The suggested tools are:

  • Infrared thermography includes cameras that detect heat spots and provide a thermal image warning.
  • Acoustic monitoring uses sonic and ultrasonic levels of monitoring for sounds of liquid and air leaks.
  • Motor condition relies on real-time early condition monitoring for defects.
  • Eddy inductive current condition involves monitoring for eddy currents in conductors, which cannot be touched. Monitoring must be precise because the current may be moving at a rapid pace.
  • Vibration analysis offers pattern readings that can signify early onset imbalances.
  • Temperature monitoring refers to holistic tolerance-based temperature management.
  • Oil and grease analysis monitors for viscosity and foreign contaminants as well as leaks.
  • Internet of things (IoT), extended internet of things (xIoT), industrial internet of things (IIoT), and supervisory control and data acquisition (SCADA) are systems that provide data from a wide variety of proprietary meters.

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  • Computerized maintenance management systems (CMMs) are used to track and service assets.

As manufacturers work to make the most of their data to increase production operations’ uptime, reduce maintenance costs, and identify hazardous situations before they occur, Info-Tech advises facility managers in the manufacturing industry that established and historical maintenance management techniques cannot be replaced by predictive maintenance in all situations. While conventional run-to-failure and preventive programs must still be used in some capacity, research shows that the implementation of predictive maintenance alongside IIoT will help organizations optimize their data to provide a host of organizational benefits, including saving both time and money, increased safety, and improved workflows.

To access the full report, including case studies, download Operational Efficiency Through Predictive Maintenance here.

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