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Traditional maintenance is built primarily on a reactive and preventive approach. When something breaks, you fix it. When it’s time to change the oil, you change it.
The smart maintenance model includes reactive and preventive approaches, but goes further with remote, condition-based monitoring, predictive maintenance, and cognitive maintenance. This post, crafted from an excerpt from Microsoft’s Smart Maintenance eBook, explores each approach in detail, which is right for your business, and when.
Reactive maintenance works well for tools and items that are part of the supply chain but aren’t likely to cause disruption if they go offline. Every plant or manufacturing facility has items like these that fall outside the rigors of a more advanced maintenance program.
Use reactive maintenance with items that:
What you need to make it work:
The preventive approach, which has been around for decades, might be the first maintenance methodology based on data. Changing the oil in vehicles every 3,000 miles, for example, is based on evidence showing that a lot of engine problems can be avoided if the oil is used for only a certain number of miles. With data showing the 3,000-mile mark to be optimal under normal conditions, we can create a preventive maintenance schedule.
As the foundation that other maintenance approaches build on, preventive maintenance means fixing and maintaining before failure can happen.
Use preventive maintenance with items that:
What you need to make it work:
This approach refines preventive maintenance by implementing wireless sensors that relay data to a maintenance manager. Now instead of performing preventive inspections on a monthly schedule, for example, maintenance can be performed whenever the data says it’s necessary.
With the power of sensors and data collection, preventive maintenance becomes a sophisticated, more accurate, and efficient practice. Integrating sensors and data collection also lays the groundwork for more advanced maintenance approaches and turns machinery and parts into Internet of Things (IoT) devices so they can be monitored from anywhere.
Use remote condition-based monitoring with items that:
What you need to make it work:
Accurate predictions rely on quality data. Predictive maintenance brings together data and technology to accurately inform the maintenance schedule.
With the groundwork laid for remote condition-based monitoring, we’re ready to advance into smart maintenance. Up to this point, the maintenance approaches described have fit a specific need, but they’re limited in their usefulness. The digital feedback loop that’s part of smart maintenance means we can be predictive, anticipating equipment failure or maintenance needs based on both historical data and near real-time data. Then we can act to prevent failure before it happens.
Use predictive maintenance when:
What you need to make it work:
Here we’ve reached the pinnacle of the smart maintenance model. Cognitive maintenance means your program is able to think ahead with much more specificity and accuracy than the predictive maintenance model can.
As the most technologically advanced approach to maintenance, cognitive maintenance helps ensure that equipment is in good working order. But it also helps optimize your workforce, production, sales, and customer satisfaction by eliminating downtime and increasing throughput.
Cognitive maintenance is best for companies that:
What you need to make it work:
Want to learn more about Smart Maintenance?
Join Microsoft and HSO industry experts as we discuss the key drivers and maintenance metrics your organization should be watching in a post-pandemic era. We’ll cover the top maintenance models used by leading manufacturers today, when to use them, and how to solve your toughest maintenance challenges with best-in-breed solutions.
Read on to learn how you can get past the proof-of-concept phase and ensure that your IoT project is fully adopted and supported by the business.
How to Get Your Maintenance IoT Project Up and Running
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