Increasing asset longevity is one of the four main goals of enterprise asset management, along with uptime, cost control, and safety. But increasing asset lifespan isn’t easy. New and legacy assets have different needs, but must work together and integrate with the same central management platform.
Logistics and utility companies are particularly challenged because both industries are dealing with significant shifts, such as moving toward renewable energy. How do you effectively manage all your assets and improve lifespan despite those industry shifts and different asset categories?
The answer may be in the data. By leveraging asset management data analytics, enterprises can work to increase asset lifespan.
Four Features of Asset Management Data Analytics that Improve Longevity
Data analytics alone won’t improve asset lifespan, but the way you use its information can. Here are four features of data analytics in enterprise asset management tools that you can use to improve longevity.
1. Integration and Correlation of Internal and External Data
As asset managers gain access to more data, they can make better decisions. With an asset management data analytics platform, data from disparate asset management systems, IT systems, and other third-party applications can be integrated and correlated into a single system.
By leveraging real-time and historical data from multiple platforms, asset managers are much more likely to spot problems and identify the right solution immediately.
2. Predictive Analytics
It’s not enough to react to problems if you want to increase asset longevity, asset managers need to identify potential problems before they cause outages and long-term damage. In doing so, they can address issues before they threaten the asset’s lifespan.
Data analytics platforms facilitate predictive maintenance by calculating risk scores and survival curves. This leads to more accurate long-term planning and allows managers to optimize maintenance schedules so that they only service assets when they need to and before problems occur. Known as predictive maintenance, this can reduce unexpected and catastrophic asset failures by up to 90 percent.¹
3. Real-Time Visual Analytics
By compiling real-time data from multiple sources into a single view, you get a comprehensive look at asset performance. And with asset management data analytics that packages information into easy-to-understand visual formats, asset managers can react much faster to data trends. The faster they can react to service assets, the longer those assets will survive.
Take the utility industry, for example. Previously, utility managers had to go into the field to gather data from meters. Now, IoT devices and smart meters constantly transmit information, putting data at your fingertips. That information makes pinpointing and servicing at-risk assets much faster.
4. What-If Analytics
If asset managers are to increase asset lifespan as much as possible, they need to account for every scenario. Predictive analytics, while incredibly effective, only takes into account the present situation. What-if analysis is the natural next step.
A tool commonly available in data intelligence platforms with IoT and AI technology, what-if analysis provides asset predictions based on a range of different scenarios. Businesses often use what-if analysis to determine how much risk different scenarios hold so they can prioritize asset maintenance and replacement to improve uptime.
Data Analytics Improves Enterprise Asset Management Decisions
While many enterprise asset management solutions include basic reporting, data analytics applications move reporting to the next level. Instead of simply providing static information, asset management data analytics packages data to reveal trends and predicts future results so managers can develop a topline strategy.
The ultimate result is often a more holistic and accurate plan for maintaining asset performance and increasing longevity to improve business results.