Predictive maintenance uses data and analytics to identify when equipment is likely to fail. It helps companies act before a problem happens. This reduces downtime, prevents breakdowns, and protects production performance.
Sensors, machine data, and historical trends are used to track changes in condition. When the data suggests an issue may occur, maintenance can be planned in advance. This reduces emergency repairs and increases equipment reliability.
Predictive maintenance is an important part of Industry 4.0. It reduces cost and improves operational stability.
How this applies to WorkfloPlus
WorkfloPlus does not perform predictive analytics itself. However, it plays a key role in supporting predictive maintenance. WorkfloPlus captures real-time job execution data as work is completed. That data can feed into other systems that perform predictive analysis.
In addition, WorkfloPlus guides maintenance teams through standardised maintenance tasks. This ensures jobs are completed correctly and helps to collect consistent evidence, which makes predictive insights more accurate.
