Improving Operations with Predictive Models

In previous blogs, we have looked at the benefits of data quality and dashboards in the utility business. Now, we would like to discuss the cost benefits of using predictive analytics to look at operational data and to help improve business decisions. We can use predictive analytics to analyze historical and current data using rules and predictive models to anticipate future events.

The Current Predicament

With smart components and IoT collecting and sharing more data than was previously available, companies are given a huge decision-making advantage to use this data to predict, prevent or react more quickly to potential issues.

With aging infrastructure, aging workforce and strict regulations, the need to maintain infrastructure and avoid outages in supply have led to a need to collect and use this data at different levels of reporting. This blog will address how we can use these valuable snippets of data to better run your business.

Future Needs to Remain Competitive and Compliant

It isn’t headline news to know that scheduled maintenance is a lot more cost-effective than emergency maintenance.  It can reduce costs by 30%. Now that data is being collected from assets, there is the opportunity to analyze it and use it to predict failures and schedule preventative maintenance, avoiding unplanned, expensive outages and providing more control over the outcomes. This insight allows the focus to be on the most vulnerable and high priority assets and reduces emergency maintenance that takes resources away from scheduled and planned maintenance and may force the need to hire costly contract help.

Then we have the weather to contend with. Predictive Analytics can integrate your knowledge of your asset locations with severe weather information and help locate areas of concern. With further monitoring, crews can be mobilized in advance to deal with situations swiftly and reduce or prevent outages and the impact on cost to residential and business consumers.

Poorly maintained assets or ineffective responses to severe weather or extreme temperature swings can lead to regulatory fines if considered to be dealt with ineffectively. By using predictive analytics, we can provide better insights into areas of concern and aid with proactive planning and prevention activities.

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.

Conclusion

Although a relatively new concept, the use of predictive analytics in the Utilities industry is starting to get some traction. The realization that historical data, blended with current operational data, can be used intelligently to predict areas of potential risk, reduce operational cost, increase asset lifetime, improve safety and increase customer satisfaction is providing too many benefits for Utilities to ignore. Those on the side of early adoption with likely set the standard for profitability moving forward.

If this blog didn’t quite answer your question or you have other Business Intelligence questions or concerns, please feel free to contact us.

increase your knowledge

See more Business Intelligence insights or get future articles sent right to your inbox

increase your knowledge

Read out other Utilities Blogs