Most field service organisations strive to deliver exceptional service. But field service professionals know that this is a complex endeavour, where decision-making needs to adjust to frequent and unpredictable changes in conditions. Even slight changes such as unseasonable weather, the introduction of a new product line, or a new customer can have a sudden and significant effect, causing service levels to drop, and leaving operational teams unclear as to which factors caused the decline, and unsure as to how to respond.
Get the reaction to an emerging threat wrong – too great or too small a response – and the balance of the operational ‘ecosystem’ can be destroyed. Recovering that balance and, with it, the conditions required for exceptional service, can prove costly and time consuming.
In my role I get the opportunity to work with some exceptional management teams, delivering field service across a range of industry sectors and geographies. I am constantly reminded just how difficult this role is. Managers describe having to make decision after decision, every day, based on imperfect insight into the operation.
Data, but not insight
Most organisations are capturing a variety of data from across their field service delivery, such as workload planning data, resource availability, schedule efficiency, service outcomes and customer satisfaction levels. Most are embarking on initiatives, such as IoT to increase the range and type of data captured.
Despite the effort organisations place on data capture, few of the management teams I speak with feel that the data is delivered to them in a form which adequately supports their decision-making. Too often, these teams have to spend time gathering and aligning data from disparate sources, and formatting it so they can use it. Despite this hard work, management teams still struggle to isolate the root causes behind exceptions and service issues. And time spent wrangling with data is time that can’t be spent at the frontlines of the operation, with engineers and customers.
Holistic, real-time next-gen applications
There are trends in current technology, which – if harnessed appropriately – can deliver the level of decision support the whole field service team needs. To be considered truly effective, decision-support applications for field service need to take a holistic and real-time approach.
To be considered ‘holistic’, applications need to draw on large, disparate datasets from a variety of sources. Valuable insights into the performance of an operation typically lie at the intersections of these datasets, but these insights are difficult to uncover with traditional computing. Getting value from IoT data represents the latest – and potentially largest – of these challenges. But big data technologies, available at scale from cloud providers such as Amazon, make deriving these insight increasingly possible.
Truly holistic applications also consider performance trends across datasets, in both long and short time horizons. Technologies such as machine learning and predictive analytics are proving capable of identifying underlying patterns of field service performance that were too complex for traditional applications to recognise. Combining this deep understanding of long-term performance with the computing power to highlight exceptions in real time, these applications are capable of providing clear direction as to the correct course of action to address service issues.
I think it’s a really exciting time to be working in this industry: these developments mean that field service professionals have the potential to achieve the exceptional levels of service they have envisaged, regardless of the challenges that each day brings.