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Using Predictive Maintenance to Improve Service Experience

The primary goal of a maintenance organization is to maximize asset availability and reduce downtime. When equipment experiences downtime, it is costly to the user experience and to the business operation. With the rise of IoT Smart Homes, customers are increasingly expecting issues to be addressed, before the need arises.

There are two common traditional approaches to maintenance: 

  1. Reactive (run-to-failure) maintenance
  2. Preventative maintenance 

The logic of reactive/run-to-failure management is simple and straightforward. When a machine breaks down, you fix it. Preventive maintenance is time-driven. In other words, maintenance tasks are based on elapsed time of operation that is based on statistical or historical data for the type of equipment.

Preventative Maintenance

Preventative maintenance schedules are based on a mean-time-to-failure (MTTF). While this approach has the advantage of being simple to plan, it also has drawbacks: 

  • Maintenance may happen too late, resulting in equipment damage and danger for workers
  • It may be carried out when it isn’t necessary.
  • Both these approaches do little to minimize the disruption of service

Condition-based Maintenance

Condition-based maintenance is an approach that drives maintenance actions based on the real-time condition of the equipment that is typically monitored through inspection or using data from embedded sensors. Maintenance only begins after the machine begins to show signs of failure. The necessary maintenance intervention may not be optimal for production scheduling.

Predictive Maintenance Using IoT

Enter predictive maintenance, which uses IoT and/or connected sensors with analytics and machine learning to predict, at the earliest point in time possible, the maintenance actions that will be required at some point in the future. Predictive maintenance can also predict the optimal time to service equipment, so maintenance is performed as closely as possible before an expected need for maintenance, or, “just in time”.  

Effective predictive maintenance solutions do a great job with the following: 

  • Increasing uptime
  • Reducing operating/repair costs
  • Eliminating surprise breakdowns

Unlike preventative maintenance, which seeks to decrease the likelihood of a machine’s failure through the performance of regular maintenance, predictive maintenance relies on data to determine a machine’s likelihood of failure before that failure occurs.

Moving from Repair and Replace to Predict and Fix 

This allows manufacturers to move from a repair and replace model to a predict and fix maintenance model using predictive analysis. 

Predictive analysis relies on data, statistics, machine learning, artificial intelligence, and modeling to make predictions about future outcomes. These characteristics make predictive maintenance a digital initiative.

The success of predictive maintenance models depends on three main components:

  1. Having the right data (relevant, sufficient and quality) available
  2. Framing the problem appropriately
  3. Evaluating predictions properly, in addition to data collection and data transmission

As computing capabilities, machine learning, and even artificial intelligence become fully integrated into APM, predictive models for maintenance will become more exact.

Predictive Maintenance Has Changed Field Service Management

When field service management integrates with APM and predictive maintenance, together they drive an extremely compelling case to close the automation gap in service. Through its analytics, the predictive maintenance platform can automatically generate work orders that are based on pre-set conditions for imminent failure, deprecated performance, and more. 

These work orders could contain extended details, including desired technician skills, best time of the day/week to execute the work order, follow-up steps, and more. The APM system can contribute by providing data about service history, prior failures, part replacements and breakdown rates, and usage data that it’s already monitoring and reporting.

The nature of the integration will enable a successful predictive maintenance solution. This allows manufacturers to optimize asset performance and focus more on achieving business outcomes.

Benefits of Implementing Predictive Maintenance

Manufacturers that implement predictive maintenance garner a number of benefits, depending upon need and application. Benefits may include:

  • Reduction of unscheduled downtime caused by system failures
  • Increased production efficiency, capacity, and quality
  • Lower maintenance costs and extended equipment lifespan
  • Increased technician utilization
  • Reliable and stable operations that boost safety and save labor costs

Predictive maintenance can drive higher customer satisfaction rates by enabling field service companies to set the right expectations regarding the day, time, length, and duration of the outage. This enables proactive maintenance when servicing IoT smart homes.

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