Across the globe natural disasters are occurring at an alarming rate. In 2018 alone, we’ve witnessed the devastating effects of the Indonesian earthquake and tsunami, flooding and mudslides in Japan, flash floods in Jordan, Super Typhoon Mangkhut in the Pacific Ocean and numerous hurricanes that battered the Atlantic coast of the US – just to name just a few. While the impact of destructive weather on people’s lives takes priority, it also imposes pressure on companies where responding to customers’ needs in the most efficient and effective manner are of essence.
To help victims recover and companies get back to business, service teams are among the first responders, and while their tasks may have different characteristics depending on the industry they work within, their end goal is to restore normalcy as quickly as possible.
Minimising the impact of natural disasters like hurricanes or earthquakes, requires companies to have a strategy, and technology in place before a crisis arises to avoid or quickly recover from service disruptions. While there is no single way to protect consumers and businesses from the after-effects of a disaster, there are steps to ensure a faster response. One way companies can ensure they recover following a weather event is to incorporate the use of predictive analytics to help them plan for and successfully execute their strategy for natural disasters.
Planning for future events
Predictive planning and forecasting uses historical and real-time information to provide an accurate picture of the number of resources needed to cover the amount of anticipated work. By leveraging machine learning, artificial intelligence (AI) and predictive modeling, companies are able to forecast, with an acceptable level of accuracy, trends and future outcomes.
For example, predictive planning and forecasting can help a utility in Florida better prepare for hurricane season. By analysing historical data collected from past hurricanes, companies can make more accurate planning decisions, such as scheduling a reserve of field resources and equipment. Before the disaster occurs, predictive analytics identify the areas that suffered the most damage in past storms. This enables the strategic placement of resources – whether internal employees, contractors, or borrowed from another utility – so they can respond as fast as possible with the correct equipment and skills.
Executing in the aftermath
Once the resource plan is in place, and the storm has passed, the services organisation is in full restoration mode. Predictive analytics empowers the most efficient response to customer needs. By analysing data from past service incidents, companies can predict with a great degree of accuracy the time it will take for a service professional to complete a job. A host of contributing factors such as weather, job type, technician seniority and skillset, and day of week, are assessed to ensure maximum productivity and customer satisfaction. Historical and real-time traffic data are also incorporated to predict accurate travel times, ensuring customer commitments are met. The end result is a quicker reaction time and faster restoration of service for a positive impact on service continuity.
Following a natural disaster, property and casualty insurers will know how long it will take their adjusters to complete damage assessments. With more accurate insights into job duration and travel between sites, companies can cluster jobs in the same neighbourhood together, minimising travel, and ensuring as many customers can be seen in a day. For customers, the faster their damage has been assessed, the faster they will be able to start repairs, giving them peace of mind during a very stressful time.
From prioritisation to maintenance forecasting and schedule optimisation, companies that adopt predictive analytics capabilities will see increased efficiency and effectiveness during normal operations, as well as in emergency situations. Providing a great customer experience, especially during a severe weather event, can increase satisfaction through proactive communication and faster recovery times.
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