Businesses have always tried to do more with less, using resources to make processes, assets, and employees as efficient as possible. Technology and data are a big part of that, but the explosion of IoT and artificial intelligence has made them even more powerful in businesses.
With IoT devices and AI, businesses can gather more information than ever before but, more importantly, they can also analyze that data efficiently, turning raw information into actionable insights. Enterprise asset management is one of the areas that’s been profoundly affected by this technology. With the right data and analytics, businesses can now create predictive asset maintenance schedules to stop problems before they start.
Predictive maintenance is preceded by predictive asset maintenance analytics, which is a key tool for upgrading your maintenance schedule. Predictive asset maintenance analytics taps into historical and current data from IoT devices and your EAM system to model asset health now as well as predict its changes in the future. Businesses like utilities, logistics, and transportation companies, are already using those tools to better maintain and monitor their critical assets.
Companies that successfully implement predictive asset maintenance analytics see a number of benefits, including:
- Reduced asset lifecycle costs
- Lowered risk of asset failure
- Decreased risk of outages
- Improvements in compliance
- Helps create more accurate long-term financial planning
While results vary greatly by company, some projections on predictive maintenance show major improvements – with up to a 90% reduction in catastrophic asset failures.¹
Unfortunately, most businesses don’t have the tools to implement predictive asset maintenance and see these benefits. The good news is that can be easily fixed. To help your company realize these benefits and successfully incorporate predictive maintenance, follow these four steps.
4 Steps to Applying Predictive Asset Maintenance Analytics in Your Business
1. Choose an Analytics Application
Since most businesses only have access to traditional analytics that don’t make use of IoT data or AI algorithms to process that data, your first step is to select an enterprise asset analytics application. Look specifically for an application that works well with your IoT assets and integrates seamlessly into your current asset management system. The system should correlate data from a number of different systems and streams, using AI to more effectively process information.
2. Create Dashboards to Highlight Insights
Once you’ve implemented an analytics application, you should set up dashboards and other visualization tools. These tools will give you real-time and historical views of data that make it easy to spot patterns and problems. Your team can use that information to draw actionable insights into the what, where, why, and when questions that come up in asset management.
3. Develop a Predictive Maintenance Plan
Data isn’t any good unless you use it to take action. Use your data and the patterns you found (or that your application highlighted for you) to make a predictive maintenance plan. The best analytics applications will help you pinpoint at-risk assets and analyze the impact those assets will have on your operation if they fell. Prioritize maintenance and repair work based on that information, using it to service assets before critical failures.
4. Track Results and Make Adjustments
If you implement predictive asset maintenance and analytics correctly, your business should see major returns in the form of increased asset lifespan, greater reliability, and better asset investment. Never just take those results for granted. Consistently measure your efforts to see if you’re getting results and make improvements to your plan when necessary.
A few points to watch to see if you’re making an impact are changes in your asset lifespan, how workloads have changed for maintenance crews and whether your asset maintenance has become more efficient. It would also be sensible to compare the frequency of asset failure before and after predictive asset maintenance analytics was installed.
Focus on Business Results
To ensure your predictive asset maintenance is effective, record baseline numbers and see how those numbers change after you follow these four steps. In the five years before you implemented predictive analytics, how long did your assets typically last? How often did they need maintenance? How does that change in the five years after you implement analytics?
If you don’t see changes in asset lifespan and maintenance needs, adjust your approach. Use asset management data analytics to dig deeper into your data to find the most useful information, then apply that information to help your business improve.