2018 Service Prediction #1: IoT and Digital Twins to Optimise Service
3rd January, 2018
IoT and Digital Twins to optimise service and drive the sector toward a data-driven 2018
With an explosive growth in new technologies to gather and use data to optimise service delivery this will be at the heart of driving the service industry in 2018. IoT in combination with digital twins, AI-powered service organisations and equipping users to service their own assets are key trends impacting the service industry, predicts Mark Brewer, IFS Global Industry Director for Service Management.
Prediction #1 : By 2020 25% of asset-intensive companies will adopt IoT and digital twins to optimise service
The Internet of Things (IoT) and so-called “digital twin” technologies are poised to have a huge impact on the service sector; reducing costs, maximizing data analytics and extending the lifespan of products. Previously, when, for example, an elevator broke down, the customer would have to phone up a service engineer reactively. This approach is highly inefficient as the individual engineer may have little idea 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 is placed at the center, sending data back to the service center enabling diagnostics to determine issues that may arise in a day, week or month’s time. It is no surprise that predictive maintenance is where the big benefits are first realized from IoT by asset-intensive companies wanting to optimize their service efforts. The Predictive Maintenance report forecasts a compound annual growth rate (CAGR) for predictive maintenance of 39% over the time frame of 2016–2022, with annual technology spending reaching US$10.96 billion by 2022.
Now let us add in the concept of digital twins, which represents physical objects in the digital world. Previously, the manufacturer’s knowledge of a product stopped once it left the factory. But now, via the feedback made possible through IoT, you can start to learn the usage, behavior 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 analyzed and used to engineer better machines going forward, 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 equipment that is 10–20 years old. But it has been able to fit them with smart sensors to measure tire pressure, temperature, oil levels, and so on. It is a win-win for customer and service organisation alike; minimising equipment downtime and enhancing product development and improving service efficiency. The approach is said to have saved Caterpillar millions of dollars already.
Author: Mark Brewer
Global Industry Director for Service Management at IFS
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