An explosive growth in new technologies to gather and use data to optimise service delivery will increasingly drive asset-based services, as MARK BREWER explains.

By 2020, 25 percent of asset- intensive companies will adopt IoT (Internet of Things) and ‘digital twin’ technologies to optimise service. These technologies are poised to have a huge impact on services – reducing costs, maximising data analytics and extending the lifespan of products.

Previously when, for example, an elevator broke down, the customer would have to phone a service engineer reactively. This approach is highly inefficient as the individual engineer may have little idea of what is wrong with the equipment, leading to a low first-time fix rate and a disappointed customer. With IoT sensors, the asset or machine becomes ‘smart’ and can send data back to the service centre enabling diagnostics to determine issues that may arise a day, week or month ahead.

It is no surprise that predictive maintenance is where the big benefits are first being realised from IoT and a report from IoT Analytics forecasts a compound annual growth rate for predictive maintenance of 39 percent by 2022, with annual technology spending reaching US$10.96 billion that year.

Now let us add in the concept of digital twins, which represent physical objects in the digital world. Previously, the manufacturer’s knowledge of a product stopped once it left the factory. But now, because of the feedback made possible through IoT, you can start to learn the usage, behaviour and performance of these products in the real world, and even make engineering changes to improve them over time.

This is a hugely important shift that helps complete the feedback loop, leading to smarter product design, more efficient service and better performing products.

You can even monitor customer usage patterns in order to modify or remove unpopular features over time.

Such an approach is already being applied in the automobile sector, where connected cars send back huge amounts of data to be analysed. This is used to engineer better machines, as well as alerting when and where faults may start to appear.

The good news is that it can also be applied retrospectively to legacy products. Construction machine manufacturer Caterpillar has plenty of pieces of equipment that are 10 to 20 years old. But it has been able to fit them with smart sensors to measure tyre pressure, temperature, oil levels and so on.

It is a win-win for customer and service organisations alike, minimising equipment downtime, enhancing product development and improving service efficiency. The approach is said to have saved Caterpillar millions already.


AI-powered voice assistants represent a second major opportunity for service organisations. Many calls into a service helpdesk are uncomplicated queries, like establishing opening hours or determining when an engineer is due to arrive, which means they are simple enough to be answered by a bot.

This drives significant potential for companies to connect AI-powered voice assistants behind the scenes to enterprise software with capabilities such as self-service diagnostics or scheduling optimisation engines, to automatically offer appointment slots. This can both make businesses more effective and lighten the load for a stretched contact centre agent workforce.

One company that is addressing this market is Amazon, which recently launched Alexa for Business. We can expect this to be a catalyst for the deployment of voice-activated service calls in the coming years.

This AI-powered approach is going to get increasingly important, not just in terms of the quality of service you can deliver, but in the context of growing skills shortages. Looking further forward, not only will Alexa provide services to the end user, but think of how a voice-activated step-by-step maintenance procedure could be of tremendous value to a service engineer: “Alexa, what is the next step after removing the motor assembly?”

The latter is not to be underestimated. In the recent global IFS Digital Change Survey, which surveyed 150 decision-makers in the service industry, ‘recruiting/training/retaining skilled technicians’ was rated as the greatest inhibitor to growing service revenue, with over a quarter (28 percent) of organisations claiming to feel either slightly or totally unprepared to deal with the skills deficit.


We are also going to start seeing a lot more augmented reality (AR) experiences used to put the customer in control of operating or servicing their own products. Just think of a Nespresso machine or a Dyson vacuum cleaner. Both companies have invested significant sums in helping consumers – with the aid of their smartphone and a QR (quick response) code – to access visually overlaid step-by-step instructions on usage and repair.

The same kind of model could be applied to more complex systems within an industrial environment, including engines, boilers or even an entire manufacturing line, providing detailed and highly customised plans for users to work from – without any of the superfluous information usually found in manuals. This raises another benefit – AR experiences do not require language translation.

This AR vision shares many of the same benefits as the IoT, digital twin and AI approaches listed above. It will help maximise the time of a limited pool of skilled workers, but also create a better customer experience.

Many consumers would rather perform their own routine fix than take half a day off work to wait for a technician, for example. We can’t underestimate the Apple effect here: with AR being built into iOS handsets, it’s only a matter of time before the firm democratises and monetises such capabilities via an intuitive, user-friendly platform. As well as downloading apps and music, think of downloading an AR experience.


There is clearly plenty of opportunity to drive a better customer experience, but for organisations to reap the benefits a few things need to happen. It is important not to think of vanguard technology as an end goal in itself.

First, make a value-based business case for any new approaches. That may mean wanting to increase first-time fix rates, offer new outcome-based contract types or simply reduce costs by ensuring technicians or maintenance workers are only dispatched when strictly necessary.

Once you have established the business case you may need to break down traditional organisational siloes between engineering, design and service. An AI-assistant or AR experience is only as good as the data you are able to populate it with. It works in two ways, though, as the feedback from product sensors will help design and build better products going forward.

Ultimately, you need the people, processes, data and systems all optimised to capitalise on these emerging approaches to reap the full benefits.

Mark Brewer is global industry director for Service Management at IFS.

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