Breakdowns and system downtimes reduce the efficiency and productivity of operations. And the more complex the infrastructure, the bigger the impact when a crucial service component malfunctions.
By using the right maintenance KPIs, field service organizations can avoid unexpected downtime. Predictive and preventive maintenance models with finely-tuned maintenance cycles make it possible to prevent damages before they occur.
This requires service measures that are as seamlessly integrated into the industrial manufacturing processes as possible in order to ensure that production operations run as smoothly as possible, even during maintenance.
Maintenance KPIs for field service: The IoT effect
Field service management software helps maintenance teams efficiently plan and implement maintenance KPIs. Recent technological developments like the Internet of Things and data analysis based on big data offer system operators new opportunities for proactive service.
Across the globe, operational data from machines and system components is constantly being sorted in the cloud. This not only makes it possible to provide targeted optimization of production processes, it also enables the reliable prediction of potential malfunctions.
4 maintenance KPIs for optimal field service
Every systems operator strives to have a long-lasting system that combines the highest levels of efficiency and productivity with maximum accessibility. All this with as little need for repairs, maintenance and retrofitting as possible.
Thanks to big data and comprehensive data analysis, field service maintenance and repairs are becoming even quicker and more precise on the basis of various key performance indicators (KPIs), which can be determined from amassed machine data and used to provide clear input on system conditions.
1. MTBF = Mean time between failures
MTBF indicates the average operational times between failures of reparable machine units. The measured value depends heavily on the operating conditions on site (ambient temperatures, start/stop cycles, servicing intervals, etc.). In that sense, the MTBF serves as an indicator of a system’s reliability. The higher the MTBF value, the more reliable the system.
2. MTTR = Mean time to repair
The average repair time after a system breakdown. This value indicates how long it takes on average to detect and locate a failure and replace the defective component. As such, the MTTR delivers important information about general system availability. The MTTR value should be as small as possible.
3. MDT = Mean down time
The mean down time refers to the average amount of time needed to repair a failure after a breakdown. Unlike the MTTR, the MDT includes the entire duration of repairs and maintenance as well as any delays caused by arrival and delivery times, replacement part logistics, and failed attempts during unplanned maintenance. The system is not operational during the MDT. Therefore, the MDT value should be as small as possible.
4. OEE = Overall equipment effectiveness
The OEE indicator makes it possible to draw conclusions regarding productivity and the added value of a system, but also regarding unexpected losses during measured operational periods without planned downtimes (e.g., planned repairs, breaks, weekends). As such, the OEE provides decisive information for optimizing production processes and overall productivity.
The factors used to measure overall equipment effectiveness are availability, service, and quality. However, since resources and processes differ widely from company to company, the OEE indicator is only relevant for the respective company and shouldn’t be generalized. Also, measures taken to improve the OEE value don’t necessarily result in increased efficiency and revenue. A logical balance between effort and utility must be taken into account.
Maintenance KPIs pave the way to performance gains
Key indicators are gaining increasing importance, not only for business management, but also for the technical sector and system maintenance where they open up numerous opportunities for identifying and exploiting potential improvements.
The right key indicators are not only useful for identifying potential improvements, but also “cost guzzlers.” This in turn facilitates the implementation of extensive maintenance controlling.
Comprehensive, ongoing analysis of machine and operational data is crucial for the success of such measures. High-performance and intuitive software solutions for field service management can then build on this to offer users wide-ranging support for the planning of maintenance and repair work, which also contributes considerably to greater transparency and improved performance.