Smarter Decision-Making to Improve Field Service Management: The Implications of Analytics on Field Service Business Models

[A brief summary of the discussions that took place in a series of five Astea-sponsored “Blitz” sessions conducted at Copperberg’s Field Service Summit 2016 held at Oxford University, UK on 12 April, 2016.]

The attendees at Copperberg’s inaugural Field Service Summit 2016 Conference earlier this year in Oxford, UK represented a microcosm of the greater UK/Europe and global Field Service Management (FSM) communities. Although comprised of more than 100 major and niche Field Services Organisations (FSOs), from a variety of vertical industry segments, and offering oftentimes disparate services portfolios, the discussion participants shared a number of common thoughts regarding the key aspects of managing a services business – especially with regard to data analytics!

From the series of five “Blitz” interactive discussion sessions, a clear pattern of thoughts, perceptions, preferences and intentions were presented by leading UK/Europe services organisations beginning, of course, with an acknowledgement that since traditional service models are still being used by many (i.e., too many?) UK/Europe FSOs, the Customer Experience outcomes will need to improve across the board, as customers want more insight into all assets and services that impact their operations. It is safe to say that this will require an upgrade to existing data analytics capabilities – also, across the board.

For some, cross-training for older and more experienced engineers will be required; while for others, there will be a need to integrate the “newer” engineers (i.e., new hires, etc.) with the organisation’s more experienced, older, engineers who typically have already established long-term relationships with their respective customers. One discussion participant cited that his company’s field engineers are presently being backed-up by a second line in the organisation’s back office, comprised exclusively of experienced engineers. This has been very helpful thus far – especially in support of the company’s newest hires.

It was also universally acknowledged that it typically takes about two years or so to successfully transfer knowledge to new engineers, as cited by multiple session participants – and that most companies would like to see even more information gathered and distributed directly from the customer’s devices/assets. In any event, there was a general acknowledgement that there would be much to gain by performing more repairs/fixes remotely.

Other specific activities identified as being critical to the overall well-being of the services organisation included:

  • Selling services – driven by customer procurement and, therefore, essentially predicated on the basis of price (i.e., agreed by a quorum of attendees, that price directly impacts the bottom line).
  • Performing more fixes remotely – however, requiring that customers need to become more involved in the overall service process.
  • Generating more leads from the field – directly from the field engineers.
  • Problem Management/Root Cause Analysis – targeted to improve first time fix rates.

In general, it was also acknowledged that first time fix rates need to improve, overall. To do so, the best path forward would be to, first, try to resolve the issue remotely before dispatching a field engineer. In most cases, traditionally, service calls have been assigned directly to the field engineer, resulting in “too high” service costs. Performing fixes remotely is seen as the best example for addressing high service costs.

One company has implemented a connected services model that uses data gathered from the remote monitoring of the customers’ assets to help their field engineers to provide higher levels of support for their customers. They typically use the collected data gathered via remote monitoring to prevent otherwise unnecessary service visits – and asset downtime. However, in doing so, they cite the importance of mentoring new engineers by the company’s older, more experienced, engineers.

Other individual company case studies referenced include:

  • Company A – asked engineers to return from pension in order to (1) benefit from their collective retained asset knowledge, and (2) transfer that knowledge to the company’s newer engineers.
  • Company B – as each of the company’s engineers has already established their own respective “site ownership”, they are asked to assist in the transference of this knowledge to new engineers approximately six months before retirement, using an internal Wiki system that also includes instruction videos, etc.
  • Company C – recognises the importance for their field engineers to understand each customer’s individual and unique business processes and, as such, believes that the retention of knowledge relating to their customer base’s older assets is key for them to cultivate, retain and pass on to newer engineers. They are encouraged to plan for cultivating this knowledge, and working in conjunction with their customers with respect to suggesting upgrades and/or new systems, etc.
  • Company D – recognises that it is increasingly dealing with an ageing workforce, and that engineer “churn” has grown higher over the past four years, or so; as a result, there is a growing need to establish a framework for retaining this knowledge.
  • Company E – encourages field technicians/engineers to be part of their new product design review board. By doing so, they believe they can help to prevent situations where a new material or part is causing too much labor in the field when installing the product, etc. One example was cited where a cheaper part was manufactured/fitted to a new equipment model, causing three additional days of installation work due to the complexity and incompatibilities experienced during the final installation.

The importance of using Data – but, not necessarily Big Data – in support of the Field Service organisation was also discussed as one of the key components of Field Service Management (FSM). Also discussed was the growing importance and capabilities of the Internet of Things (IoT) side of the equation, particularly in terms of how it can be used to collect and generate vast amounts of data. While all participants expressed their preference for having that capability, most believed that they would also require a strong reporting department to generate and distribute the resultant reports for them – sanctioned and managed by the Service Department, rather than the Finance Department.

In all cases, the importance of data analytics was first and foremost in the minds of each of the participants. However, how to best manage the collection, analysis and distribution of the vast amounts of data that can be generated via the IoT represented the greatest challenges that they would expect to face moving forward.

[For more information on Data Analytics and a full array of Service Lifecycle Management (SLM) solutions for the Field Services segment, please visit the Astea Website at www.Astea.com.]

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