The service industry is at an inflection point. Over the next 24 months, technology will drive digital transformation in many companies striving to improve and predict revenue streams while also improving operational efficiency.
A recent study conducted by WBR Insights shows that nearly three-quarters of respondents provide service on equipment that is sensor-enabled. The ability to collect usage patterns and document asset performance can support a predictive model, facilitating the ability to sell assets as a service and not just as capital equipment with aftermarket service delivery.
Customers in many cases want the predictability that comes from paying for a defined outcome. But for the services organisation, profits will drive this trend.
Data collected by IFS reveals that creating more value by adding services to products or even replacing a product with a service -- so-called "servitisation" -- can increase service profitability by almost 20%.
But some investments in technology are needed to enable this transformation. What kind of technology? The WBR report also looks at the specifics of how technology can lead to more effective management of the workforce.
BIG DATA, BIG RESULTS
Devroy: Upbeat about intelligent scheduling
Any sensor-enabled device can generate huge amounts of data points. The ability to gather, analyse, distill and communicate this data can reap huge financial and operational rewards.
When sensor-enabled equipment detects a failure, a diagnostic code can be generated. Knowledge systems can be incorporated to determine the part or component failure involved, recommend a part replacement, perhaps even download repair instructions, schematics and part location to a field technician.
I recently saw this in action at a WBR trade show. It was a standalone IoT reporting system, but a very effective real-world application of digital transformation that empowers technicians and service partners with useful repair information prior to a service call.
Done properly, the incorporation of AI techniques and machine learning can determine the operational health of an asset. The ability to predict failure or degradation over time can set the stage for recommended proactive maintenance, a service call, part replacement or recalibration of a device. This allows the equipment provider or authorised service agent to sell performance contracts that can be managed to margin, with a high level of confidence.
These sensors can also harvest usage data, including asset throughput, procedures performed, or hours of operation. Once again, performance-based contracts can be generated that guarantee what the customer will receive from the equipment.
These usage contracts require continual updates on asset usage and health as its performance directly correlates to anticipated revenue.
Mobile technology will then push this information to the technician, or to a team assigned to a job. All of this data will be distilled so that it can be disseminated directly to the people who need it most.
Service managers will monitor activity and service technicians can use this data to do their jobs better. The data may take different forms, from prescriptive analytics that suggest how a problem should be dealt with, to visual representations of what is going on inside the equipment.
The ability to use augmented reality for repair purposes is one example of how a technician may visually interact with data -- by viewing an overlay of parts or components as they look on the physical machine.
Team collaboration may also allow an expert in a central location to see what a technician in the field is seeing, and to use augmented reality to advise his associate remotely. This will enable a service organisations to do more with less and smooth the transition from a shrinking, ageing workforce to a newer, younger but less experienced workforce.
RIGHT TECH, RIGHT TIME
Finally, intelligent scheduling tools will put people in the right place at the right time. Once again, using artificial intelligence techniques, these systems will position a technician in the right place to be more successful in service delivery.
Why is AI necessary here? Because identifying and dispatching the right technician given fluctuating demand is too complex a problem for any human dispatcher.
In the absence of AI-driven scheduling, urgent jobs coming in that should take precedence over those already scheduled will probably get pushed further out. Dispatchers will struggle to make scheduling decisions based on service-level agreements and other contractual requirements, and will not be certain which technician has the skills, tools and materials to handle which job.
The location of the technician is constantly changing, so identifying the closest one to an urgent service call is difficult.
When intelligent algorithms make these decisions, the service business can make commitments to customers, utilise technicians in the best way possible and improve the customer experience.
Tom Devroy is a product evangelist for Enterprise Service Management with IFS, a multinational enterprise software developer.
