Before and after natural disasters, predictive analytics increases field service efficiency

2017 was a ​record setter for natural disasters​ and the United States saw several very devastating storms. So devastating in fact, that three Atlantic storm names ​have been retired from future use. Unfortunately, 2018 isn’t ​looking more promising​ so far. While the impact of destructive weather on people’s lives takes priority, it also imposes pressure on utilities like electricity, gas, water and sewage to restore services as quickly as possible to help people recover from these disasters.  
 
It is paramount that utilities have the technology, strategies and plans in place before storms hit to avoid and quickly recover from any service disruption. One technology that can help utilities recover more quickly from a weather event is through the use of predictive analytics within their repair and maintenance strategies. Predictive analytics use historical service data and machine learning to identify certain outcomes.  
 
By analyzing data from past service incidents, utilities can better manage resource capacity through a more accurate assessment of contributing factors to future performance, such as weather, job type, technician seniority, and day of the week. This information enables utilities to optimize scheduling, routing and job prioritization. The end result is  a quicker reaction time and faster restoration of service.
 
For example, predictive analytics can help a utility in Florida better prepare for hurricane season. By analyzing historical data collected from past hurricanes, utilities can make planning decisions, such as scheduling a reserve of field resources and equipment during hurricane season, to ensure there are enough resources in place to respond to urgent situations. After a disaster occurs, predictive analytics can identify the areas that suffered the most damage in past storms in order to  allow for the strategic placement of resources so they can respond as fast as possible with the correct equipment and skills. During massive outages, it enables utilities to know how long certain tasks or types of repairs took to fix. With more accurate insights into job duration, utilities can better schedule repairs and manage customer expectations around service restoration, helping to improve the customer experience during a very stressful time.  
 
For utilities, predictive analytics enable field service organizations to properly plan long-cycle work to be scheduled only outside of hurricane season. Similarly, it can ensure that the most important repair and maintenance tasks are completed before the next hurricane season arrives. This knowledge helps arm field service managers with information to enable better planning and resource utilization year round.

From prioritization to maintenance forecasting and schedule optimization, utilities that adopt predictive capabilities will see increased efficiency and optimization during normal operations as well as in emergency situations. Most importantly, predictive maintenance is a key tool in delivering exceptional customer experience. Providing a great customer experience, especially during a severe weather event, can increase satisfaction through better communication and faster recovery times enabled by predictive analytics. Predictive analytics provide the ability to more accurately develop the plans and response strategies for natural disasters as well as during normal day to day operations. Predictive analytics can help field service organizations at utilities weather any storm.

Steve Smith is the Vice President of Strategic Industries at ClickSoftware