PM intervals should be based on manufacturer recommendations, equipment criticality, operating conditions, and historical failure data. Start with OErocedure Filelines, then adjust based on your specific usage patterns and environment. Consider factors like duty cycle, environmental conditions, age of equipment, and regulatory requirements. Many organizations use a combination of time-based (calendar), usage-based (hours/miles), and condition-based triggers to optimize their PM schedules.

Preventive maintenance follows fixed schedules regardless of equipment condition, while predictive maintenance uses real-time condition monitoring to determine when maintenance is needed. PM is calendar or usage-based (e.g., oil changes every 3,000 miles), while PdM uses technologies like vibration analysis, thermal imaging, or oil analysis to predict failures. Both strategies complement each other in a comprehensive maintenance program, with PdM helping to optimize PM intervals.

Key performance indicators (KPIs) for PM effectiveness include: PM compliance rate (% of PMs completed on time), Mean Time Between Failures (MTBF), equipment availability/uptime percentage, maintenance cost per unit of production, ratio of planned vs. unplanned maintenance work, and overtime hours. Track these metrics monthly and look for trends. A successful PM program typically shows 90%+ PM compliance, increasing MTBF, and a shift from 80% reactive to 80% planned maintenance.

Common challenges include resistance to change from reactive maintenance culture, initial resource investment requirements, determining optimal PM intervals, maintaining PM schedule compliance during busy periods, and tracking/analyzing maintenance data effectively. Solutions include starting with critical equipment first, using CMMS software for automation, providing training on PM benefits, establishing clear accountability, and celebrating early wins to build momentum. Digital platforms like HVI help overcome many of these challenges through automation and visibility.