The Big Data challenge: Leveraging analytics to make better business decisions and enhance field service performance
Big Data is a buzz word making its rounds across a variety of industries and the field service sector is no exception. Gartner defines Big Data as high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.
Over the last 10 years, field service organisations have become overwhelmed by the relentless flow of information coming in from multiple sources, in various formats and through an array of tools. For example, in a typical field service business data will be coming in from GPS and vehicle-tracking systems, telematics, fleet management and workforce management. Merging and organising this ‘Big Data’ is so difficult that, in most businesses, it ends up sitting unused in applications and databases. However, many are now beginning to realise its sleeping intelligence and that they need to tap in to it to help make more informed business decisions.
The major challenge they face is how to make sense of the massive amounts of data they collect daily and tame this flow in order to extract valuable insights to help hone day-to-day operations and make long-term strategic decisions.
Performance Management Analytics (PMA) has come to the fore as a solution able to tackle the Big Data challenge. PMA provides field service managers with the visibility to analyse the productivity of their field service operations. For instance, the tool can help reduce unauthorised stops, minimise excessive speeding and idling, increase the number of jobs performed daily, and improve response times.
The Big Data Opportunity for Field Service
The ability to make sense of data can make the difference between a business that is good enough and one that stands out from the pack. When a company figures out how to review historical data about itself, identify patterns, and compile metrics and statistics to determine which assets and employees are the most productive, it can use those insights for predictive analysis and better business decisions.
The reward is higher customer satisfaction and profits. In a study commissioned by Trimble, The Road Ahead: The Future of Field Service Delivery, 80% of managers surveyed cited customer satisfaction as their top priority. A 2012 Aberdeen report highlighted the importance of customer satisfaction, finding that organisations with ratings of 90% or higher successfully retained at least 90% of customers, while those with ratings of 50% or lower retained only 26%.
Big data can play a major role here. Field service organisations that have deployed GPS, fleet and workforce management technologies already have the tools that help make sense of the information and make decisions to improve customer satisfaction. To accomplish this effectively, field service organisations must set specific goals, such as reducing overtime through route optimisation and cutting fuel costs through GPS tracking and fleet management systems.
Capture and Analysis
So much information flows back into dispatch centres and offices of field services organisations that letting it go unused actually hurts the business. Distilled properly, information through GPS, telematics, fleet management and workforce management tools provide concrete, actionable details, giving managers and dispatchers real-time visibility into fleet activities.
Systems set up to capture in-day exceptions, for instance, can save a company thousands of pounds by catching bad driver habits such as fueling cars with premium instead of regular fuel, making unscheduled stops, and ignoring pre-set routes optimised for time and fuel-savings.
Fleet and workforce management systems give managers the ability to review a day’s work and measure performance results against company standards. By leveraging Performance Management Analytics (PMA) tools, managers can identify top performers, determine which schedules and routes produce the best results, and compare results from one vehicle or worker against the entire fleet.
Performance analysis can also help with job assignments, helping managers match the skills of field technicians to specific service calls. This increases the prospect of first-time case resolution. According to Aberdeen, 26% of field visits fail to resolve the problem, requiring follow-up visits, and frustrating customers.
Telematics solutions can capture a wealth of useful information, from mechanical and emissions to driver safety habits, all of which can be collected and organised into easily digestible reports. Analytics reports, for example, can leverage telematics to provide stakeholders with information in easy-to-read, relevant snapshots highlighting operational areas that need immediate attention.
The basis of telematics was originally location, but location is now merely an enabling tool for a plethora of complex business applications. Analytics now let customers see everything from the most profitable jobs to success rates in meeting appointment times. We’re moving towards an era of ‘super information’ delivered by telematics which will see the impact of the technology surge.
Immediate and Long-term Benefits
With big data, knowledge leads to action. A field service manager who knows which drivers have bad habits is better equipped to evaluate those drivers, act to correct their behaviour and schedule training for individuals who need it. Up-to-date information on the health of vehicles leads to better maintenance, which in turn leads to safer vehicles, improved fuel consumption and less wear and tear.
Likewise, the ability to collect real-time information on traffic through GPS tracking empowers dispatchers to make decisions on the fly to change routes and avoid congestion. AVL (automatic vehicle location) and real-time information on the distance between customer stops leads to routing and schedule optimisation.
Those are the immediate benefits, but understanding big data also brings long-term advantages, as companies engage in strategic planning based on historical patterns and predictive analysis. Thanks to big data, organisations can conduct predictive analysis for more accurate planning. For example, for companies focusing on repair, using historical data about when a part is most likely to fail, enable them to do better planning for the future. This is called preventative maintenance, fixing or changing a part even before it fails. Furthermore, adjusting resources, modifying schedules, planning vehicle purchases and forecasting hiring needs become less about guessing and more about precise, well-researched planning. And that’s why field organisations need to take control of their information.
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