Predictive Analytics – Asset Lifecycle Management

Utility management in power transmission and distribution field is a vital task. Since the end-customer is also one of the stakeholders when it comes to power consumption sector, the companies that undertake the transmission and distribution facility have to make sure to maintain negligible downtime. It is a loss to the company and frustration to the end-user if the grid downtime is high. In this scenario, the only solution that helps the companies to keep the grid up most of the time is Predictive analytics.

Companies need to overcome so many challenges to keep the downtime of the grid low. So far, the T&D companies have been managing the grid in a traditional manner; with aging assets and low returns. Now, with the implementation of predictive analytics, the situation changes drastically. Companies can predict the failure of equipment in advance and order for replacement. When the customer expectations are rising for an unhindered power supply scenario and governments across the globe are pushing down the throat of the companies to reduce carbon targets, it is very clear that the situation has put the companies at a juncture where they have to focus on the asset maintenance part.

Since asset maintenance plays a vital part in reducing the downtime and maximizing the uptime, T&D companies are taking the help of predictive analytics to take care of the grid asset lifecycle: from ‘asset investment planning’ to ‘operation and maintenance through decommissioning and disposal/ replacement’. If an asset fails, the consequence of the failure creates a snowball effect on the T&D company such as expense of the asset in service, collateral damage cost, regulatory penalty, disposal of damaged asset, lost revenue, and other intangible costs.  

By implementing predictive analytics, T&D companies get the following benefits when it comes to managing assets: 

1. Extend asset life  
2. Bring more predictability to asset performance
3. Help to plan and prioritize maintenance activities
4. Reduce asset lifecycle cost
5. Enhance business process
6. Improve productivity
7. Improve customer satisfaction
8. Improve power reliability based on planned outages
9. Reduce unexpected asset failure cost [leading expense component of any asset]
10. Improve forecasting and scheduling of assets

In summary, predictive analytics helps T&D companies manage their assets well, reduce grid downtime, and delight their customers by providing maximum grid uptime.