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Perth County Ingredients reduces downtime with CMMS and predictive maintenance
Rockwell Automation’s Fiix CMMS cuts reactive maintenance by 54% and lowers costs at Canadian egg processor.
www.rockwellautomation.com
Perth County Ingredients (PCI) supplies dried egg products to global food, beverage and nutrition markets. Operating in a processing environment with aging infrastructure, the company faced growing maintenance challenges that threatened uptime and cost control.
Maintenance activities were entirely reactive, with 100% of work performed on a run-to-failure basis. Critical assets such as boilers and chillers required frequent attention, and after-hours emergency callouts were common and costly. PCI needed to transition from “firefighting mode” to a structured, proactive maintenance strategy.
Implementing a computerized maintenance management system
To address these issues, PCI deployed the Fiix computerized maintenance management system from Rockwell Automation. The implementation included mobile-enabled access via tablets, allowing technicians to retrieve work orders, standard operating procedures and asset histories directly at the point of use.
QR codes attached to equipment enabled rapid identification and documentation access. Facility-wide Wi-Fi upgrades ensured uninterrupted connectivity, reducing troubleshooting time and improving response speed.
Integrating real-time machine data
To strengthen predictive capabilities, PCI partnered with Majik Systems to connect plant programmable logic controllers (PLCs) directly to the CMMS. The integration provided continuous monitoring of vibration, temperature, pump current, torque and other machine-health indicators.
When parameters drift outside predefined thresholds, the system automatically generates work orders. The integration, completed in two weeks without manual data entry requirements, gives maintenance staff real-time asset visibility, even outside normal working hours.
Measurable operational improvements
Following the deployment of Fiix CMMS and real-time integration, PCI achieved quantifiable results:
- 54% reduction in reactive maintenance
- 47% decrease in after-hours maintenance call-ins
- More than $40,000 in annual maintenance cost savings
- Threefold return on project investment
- Reduced frequency of pump and bearing failures
These improvements allowed maintenance teams to redirect labour from emergency repairs to planned and value-added activities. The enhanced visibility also improved contractor coordination and informed capital planning decisions.
Expanding predictive maintenance across sites
Building on condition-based maintenance success, PCI has begun using Fiix’s Asset Risk Predictor to identify early warning signs such as rising pump torque or temperature trends. This enables proactive scheduling of interventions before issues escalate into critical failures, supporting uptime and product quality.
The results at PCI were sufficiently strong that its parent organisation expanded the system to seven additional facilities, centralising oversight and standardising maintenance practices across multiple sites.
The case demonstrates how integrating a CMMS with real-time equipment data can significantly reduce reactive maintenance, lower costs and establish a scalable foundation for predictive maintenance in food processing operations.
Expanding predictive maintenance across sites
Building on condition-based maintenance success, PCI has begun using Fiix’s Asset Risk Predictor to identify early warning signs such as rising pump torque or temperature trends. This enables proactive scheduling of interventions before issues escalate into critical failures, supporting uptime and product quality.
The results at PCI were sufficiently strong that its parent organisation expanded the system to seven additional facilities, centralising oversight and standardising maintenance practices across multiple sites.
The case demonstrates how integrating a CMMS with real-time equipment data can significantly reduce reactive maintenance, lower costs and establish a scalable foundation for predictive maintenance in food processing operations.
