Telecom tower on hight mountain niederhorn Switzerland

The world of inspection, diagnosis & repair (service) is entering a new stage in its evolution, driven by the adoption of technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wearables and the machine learning algorithms designed to turn this stream of rich data into actionable intelligence.

One would expect the number of site visits to fall significantly as routine inspection becomes automated and generic asset service ‘schedules’ are replaced by interventions based on an individual assets performance.

Today, a company inspecting & repairing power station cooling towers may just be transitioning from technicians climbing towers to drone-based surface inspections, with a single drone operator carrying out the work that would previously take 6 people. The images are viewed by experts back at base, looking for significant cracks that would justify repair work. This use of drones is clearly driving a worthwhile productivity gain. Such inspections may only have been carried out annually, because of the cost involved, but could now be repeated more frequently.

Now imagine a scenario where fully autonomous drones, stationed on site, undertake routine inspections daily, transmit data to a central control, and then park themselves again. Not only does this further reduce the cost of inspection but the new ability to digitally monitor changes in the condition of an asset over such short intervals (for example, identifying the development of micro line cracks) allows the development of new software classifiers that can recognise damage before the deterioration is significant enough for visual clues to be picked up. Using the repair histories of similar assets, the classifier could recommend an unscheduled early re-surfacing in order to avoid an earlier than scheduled full refurbishment. On high-value assets, such intelligence-driven preventative maintenance has the potential to reduce the total maintenance cost over the asset life-cycle, driving up RoI.

This is a glimpse of the technology landscape that your service business will be operating in, in the near future. We refer to this marriage of automated real-time data collection & automated decision making as ‘Service 3.0’.

So, what was ‘Service 1.0’?

Gartner[1] estimated that digitising a paper-driven service process by introducing a suitable software application typically delivers a 25% improvement in productivity. The driver for change is usually a desire to reduce re-keying of data and to speed up invoicing by reducing the time-delay associated with waiting for job-sheets to get returned to the office. The key technology advance required to realise these efficiency gains was the use of mobile software applications to allow the digital capture of data from the field. This is what we refer to as ‘Service 1.0’ and a recent survey by Salesforce[2] indicates that 78% of field service companies now have some form of mobile capture capability.

Many service companies have moved beyond Service 1.0 by implementing software driven processes that improve the customer experience and ensure that field employees are complying with both relevant legislation and the proscribed procedures for the work. Ensuring that only employees suitably qualified get allocated to a job and that they follow approved processes and record evidential data for audit processes, is viewed as a critical management objective in today’s litigious business world. This is what we refer to as ‘Service 2.0’ and the key technological advance is the ability to develop sophisticated data capture workflows for mobile device users that can be edited within hours instead of weeks. This is beyond the Service 1.0 technologies underpinning most field service ‘apps’ that have limited customisability.

We believe that adapting to a Service 3.0 world will require not only fresh-thinking about the way service companies create value but a software framework that can manage data reception via multiple channels and allow models developed from machine learning on that data to be employed. For most service business, new tools and expertise will be required.

At Collabaro, we have the required tools & a vision for delivering Service 3.0 that we would be delighted to share with you.

References:

1. How to build a business case for field service management software investment (Gartner 2017)

2. Connected Services Report (Salesforce 2016)

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