Where Technology Can Help Field Service Companies Overcome Their Key Challenges

Jun 21, 2019 • Features • Cognito iQ • Future of FIeld Service • Dave Webb

Dave Webb, COO, Cognito reflects on what he thinks are the key challenges that field service companies are facing in today's business landscape and outlines how and where he believes technology can play a role in overcoming them

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In my experience, most field service organisations are striving to deliver exceptional service, a complex, dynamic endeavour where few things remain constant for long, and decision making needs to adjust constantly to changing conditions.

Pressures acting on the operation from within and externally from customers in the broader market create a constant long term demand for more efficient and effective service delivery. Whilst even slight changes to operating conditions, the sudden impact of unseasonable weather conditions, the introduction of a new product line or a new customer can have an immediate and significant impact on the dynamics of the operation.

Too often, just as service delivery indicators point to success something changes and operational teams can find themselves facing a wholly new dynamic, often without absolute clarity as to precisely what combination of factors triggered the change and are left therefore unsure as to how best to respond.

With the long term drive for efficiency and effectiveness stripping most operations of excess capacity, their ability to flex to meet these challenges are significantly restrained.

Judging the right levers to pull and just how hard to pull them is something of an art form, a gift for which managers of field service operations are often noted. Get the action to an emerging threat wrong, too great or too little a response and the intricate balance of the ecosystem can be thrown out. Recovering that balance and restoring the operation to the conditions necessary for exceptional service often proves a costly and timely affair.

In my role, I get the opportunity to work with some exceptional management teams delivering field service across a diverse range of industry sectors and different geographies. When I start talking with new organisations about their field service operations, I am constantly reminded just how challenging their role can be as they describe having to make a complex array of day-to-day decisions often with an imperfect insight to the status of the operational dynamic on which their decisions are based.

Most organisations are capturing a broad range of data from across the various dimensions of their field service delivery. From workload planning, resource availability, schedule efficiency, service outcomes, customer satisfaction levels and asset profitability, most are embarking on initiatives to increase the range of data they are capturing. One such example would be with the large denormalised data sets offered by the Internet of Things.

"In spite of the effort that organisations place on data capture, few of the management teams that I deal with feel the data is delivered to them in a form which adequately supports the complex operational decision making they have to perform daily..."

In spite of the effort that organisations place on data capture, few of the management teams that I deal with feel the data is delivered to them in a form which adequately supports the complex operational decision making they have to perform daily.

Too often, these management teams talk about spending a large proportion of their available time gathering and aligning data from disparate sources. Moreover, then manipulating that data into a form in which it is capable of delivering reliable insight and then manipulating that data into a structure in which it is capable of providing reliable insight into the performance of the operation.

For all the hard work required to create that insight, management teams are left further frustrated as the insights available rarely isolate the root cause behind the dynamics of the challenges at hand and truly support the decisions they must make in response.

Time lost wrestling with the data leads many to refer to a sense of management in the rear view mirror. Decisions based on out of date, expired data sets leave them with the feeling they are chasing echoes in operation. All of that effort expended on getting to the insight also takes its toll on time available to spend at the front lines of the operation with their engineers and out with the customers adding value.

As Dave Bohenski described in an earlier video in this series, there are trends within current technology which if harnessed appropriately I believe can deliver the level of decision support field service teams as a whole require - and when I say as a whole I mean management, field workers and often integrating the customer too into that picture. It can do so in real time and in ways that can release them to better perform their roles with the benefit of that insight as opposed to spending their time on data preparation or reacting to performance nudges that echo from events no longer relevant.

To be considered truly useful decision applications for field service need to:

Provide holistic, real-time insight into those dimensions of performance that influence the overall service delivery and
Help to guide proportionate operational response in ways that collectively engage the entire operational team.

To be considered holistic, these applications need to draw on large, often disparate data sets from a variety of sources and from a range of things that make up the ecosystem of a typical field service operation.

Valuable insights into the performance of operation typically lie at the intersections in the relationships between those 'things' and data sets. Traditionally technology has found it challenging to normalise seemingly incompatible forms of data, deriving value, for example from the IoT revolution represents the latest and perhaps arguably one of the largest to date of these data challenges. Big Data technologies make this traditionally computing challenge increasingly possible. With Cloud providers such as Amazon offering unparalleled compute and data storage performance at scale, applications that are designed to harness this capability are breaking down some of the traditional barriers to visibility within the operation.

"Technologies such as machine learning and data analytics are proving capable of identifying underlying patterns of performance in the field service operation that was too complex for traditional applications to identify..."

Applications also need to consider performance trends across those data sets in both long and short time horizons, if their decision to support is to be considered sufficiently holistic. Technologies such as Machine learning and data analytics are proving capable of identifying underlying patterns of performance in the field service operation that was too complex for traditional applications to identify. This is increasingly true as much larger data sets such as IoT have come online in recent years.

Combining the deep understanding of long term performance with the compute power to highlight and match this with exceptions in real-time, these applications are capable of providing operational teams with clear direction as to the correct course of action to take to address the specific dynamics of each challenge as it arises.

Effective team collaboration is key to delivering exceptional service. Applications centred on a single real-time data set have the opportunity to align teams across the operation around a shared understanding of service delivery performance. Responsive artificial intelligence technologies offer the chance to take feeds from predictive analytics, for example, and combine them with the awareness of real-time exceptions to provide clear and concise decision support within the operation.

Beyond simple advice, technology is making it increasingly possible for applications to automate so-called learned responses to familiar patterns of exceptions that occur, freeing operational teams to tackle other more valuable activities.

To be an effective stimulus for collaboration within the operation decision support applications need to deliver performance insight in ways suited to the different roles within the operation, e.g. engineering, planning teams, scheduling teams, customer service, spares and logistics, account management. One size certainly does not fit all where insight into the performance of the operation is concerned.

For the engineering teams, for example, this should be increasing via mobile technologies which seamlessly link responses to exceptions that arise into the standard flow of their activities. Gone are the constant interruptions for updates or the need to sync with the centre, replaced by less intrusive delivery mechanisms that compliment, not disrupt engineering activities by providing key customer facing resources with greater situational context and awareness around the performance of the operation.

This is increasingly made possible as virtual and augmented reality technologies become more prevalent. The traditionally complex and challenging nature of field service operations when combined with the enhanced capabilities offered by the latest Cloud-based technologies certainly makes it an exciting time to be involved in this industry.