Areas to Consider for Predictive Analytics
Performance: Monitoring current asset performance against historical data can provide insights into the risk level and into whether the asset is operating within expected parameters. Deviations from the norm can be highlighted for action. This can help increase the lifetime of the asset, as well as aid in the planning of both maintenance and procurement of new assets. Understanding the implications of the data can enable better decisions on actions to be performed.
Areas of Rapid Growth: Look at data for rapidly developing areas and use predictive analytics to anticipate short- and long-term demands on supplies, so that infrastructure can be in place to meet new requirements, without impacting current customers.
Making Happier Customers: Outages make for unhappy customers. Some are unavoidable (extreme weather, for instance), but how they are handled can reduce customer impact. Making sure planned maintenance is communicated is essential. However, using predictive analytics to reduce emergency maintenance of high-risk assets and mobilizing teams for weather-related expected outages can help reduce the number of outages and time without service for the customer. This can also save you money.
On the other hand, we also can use predictive analytics to assess customer behavior, by looking at supply management predictions, allowing a better plan to use assets more economically. We can also look at a customer’s ability to pay, based on predictive models, and provide help via payment options, thereby reducing the risk of defaults on payments.
Improved Safety: Predictive asset analytics can proactively address potential safety risks, looking at data from inspection processes and determining whether behavior or assets are contributing to risks. Using the results, activities, and processes can be put in place to mitigate the risk of injury and damage, thereby reducing costs.