Dunlop Aircraft Tyres Transitions from Reactive to Predictive Maintenance with CMMS

Dunlop Aircraft Tyres, a UK-headquartered manufacturer of aircraft tyres for platforms including Airbus, Boeing, Bombardier, and Embraer, was running its maintenance operations on a foundation of paper records, Excel spreadsheets, and a Microsoft Access database with no scheduling or planning capabilities. A shortage of on-site engineers meant the engineers present carried double the normal workload, leaving no capacity for proactive maintenance planning. In an aerospace-grade manufacturing environment where component reliability directly affects flight safety, this reactive posture created persistent operational risk and daily firefighting that constrained production throughput.

Dunlop selected Fiix CMMS — a Rockwell Automation product — partly because Rockwell already controlled most of the plant floor equipment via PLCs, making the integration a trusted, lower-risk step. Fiix replaced the Access database with a centralised system for maintenance scheduling, resource allocation, and historical work order tracking accessible from mobile devices on the shop floor. After approximately one year of baseline data collection, Dunlop added the Fiix Asset Risk Predictor (ARP), a predictive ML module that ingests sensor data (including temperature and pressure readings from adjacent systems) to generate asset risk scores. The system is configured to issue automatic work orders when a predefined number of amber-level risk alerts occur within a short window — removing manual judgment from time-critical intervention decisions and enabling cross-system root cause analysis.

Dunlop began seeing measurable operational improvements within the first few months of deployment. Daily maintenance reports for plant managers — previously compiled manually — are now generated automatically, delivering significant time savings reported as 'tremendous' by Plant and Controls Engineer Michael Thomas. Key outcomes include:

• Automated MTBF tracking reviewed in weekly maintenance meetings, replacing ad hoc data gathering
• Improved data quality free from manual entry errors, enabling more accurate scheduling and resource allocation
• Zero full-blown asset failures recorded since deploying the Asset Risk Predictor
• Faster troubleshooting via cross-system correlation of risk scores with historical temperature and pressure data

Better decision-making through daily and weekly analytics has replaced the previous reactive cycle.

• Starting with a CMMS before adding predictive ML is a practical sequencing strategy — one year of clean work order history made the risk prediction model meaningfully more useful.
• Vendor alignment matters in brownfield environments: Dunlop's existing trust in Rockwell Automation PLCs reduced procurement friction and smoothed integration.
• Automatic work order generation from risk thresholds is a critical design choice — it eliminates the human delay in acting on predictive alerts.
• Mobile accessibility is a prerequisite, not a nice-to-have, when engineer coverage is thin across a facility.
• Predictive maintenance ROI in aerospace is measured in failure prevention: the absence of unplanned downtime is itself the headline outcome.