BUSRide spoke with Kevin Price of Infor, New York, NY, about actionable business intelligence that transit agencies can derive from enterprise asset management (EAM) systems. Price, who is based in Greenville, SC, has more than 17 years in Infor’s asset management business, holding roles in sales and service, as asset solutions director for the Infor Public Sector group, and now product director for Infor EAM, MP2, Spear Technologies, and Infor CloudSuite Facilities Management.
It depends on many different factors, the biggest being the risk variable of a given asset. Risk variables can include the asset’s age, the routes it drives and even whether there’s salt on the road. Understanding risk variables allows for efficient maintenance scheduling and will allow operators to automatically gauge vehicle availability and reliability. The availability and ability to schedule an asset really depends on its reliability at the time and how it’s maintained.
Why is it so important for organizations to define their own parameters for success before delving into EAM data?
The aforementioned risk assessment and liability survey is important because every environment is different. Vehicles behave and operate in Miami differently than they do in New York. Compound those different parameters by the variability in how operators use their fleets. Every operation is different, so no two sets of parameters for success are the same.
How can bus operators use EAM systems to leverage inspection data and keep their fleets running in a fixed, measurable way?
Operators maintain assets in different ways. The most basic method is reactive maintenance, or fixing things when they break down. Proactive maintenance involves understanding that regular maintenance is needed, but there’s not really a model in place. A third option is preventive maintenance, where maintenance is regularly scheduled and a policy is in place to enforce it.
We tackle maintenance with a predictive approach. Predictive maintenance incorporates analytical and inspection data to better understand when maintenance is required – to predict when an asset will fail. In our application, we do reliability-centered maintenance but we also do reliability planning and analysis, so we can take a lot of these inspection points and then use them to predict failure.
It’s very important to get to predictive maintenance. It’s not as easy as everyone would think, because it involves a lot of data point gathering. However, if you can get there in a sustained way, it’s going to have a positive impact. For example, once it switched from manual data entry to an EAM system, a third-party maintenance provider to the transit industry was able to free up 14 clerks, which alone covered the costs of their mobile EAM project. Another transit agency was able to reduce the average monthly number of equipment system malfunctions affecting its service levels by 32 percent over a three-year service period after implementing a predictive EAM system.
How will EAM develop in the near future? Will operators be able to manipulate even more disparate data points to better optimize their businesses?
Absolutely, operators will be able to do so much more work with their EAM systems. Systems will be able to combine more condition-based monitoring data that will be more readily available as the Internet of Things (IoT) matures.
In a basic EAM system, there are 1,400 points of connection that we can get to. If we multiply those connections by the number of new connections that are being developed, there will be 15 billion points of data that we can capture every day. This will give agencies a better decision-making tree with real-time capabilities.
I think a lot of that technology is available now, but it’s highly customized in the way it’s communicated. The future will be about making the technology generic in such a way that it’s available across the board. That should happen within the next decade.
Kevin welcomes your feedback and questions. Please don’t hesitate to email him at email@example.com.