They are the unexpected situations that every car owner dreads: a breakdown on a rural road, a blown tire on your evening commute, a dead battery in the middle of winter.
But events like these usually aren’t the result of a sudden development in our vehicles. Rather, they are due to standard wear and tear that occurs over time, with factors like temperature, road conditions and component degradation all playing a role. The problem is, we can’t see any of this – so it all goes undetected until it manifests into a breakdown.
Today, however, the emergence of smarter and more connected cars is helping make breakdowns and other headaches a thing of the past.
For example, smart-car predictive services can monitor key components and, if an issue is detected, proactively alert the driver to take the vehicle in for servicing. Some smart cars can even receive firmware updates that improve vehicle performance or address known issues – all without the owner needing to bring the vehicle into a dealership.
A similar phenomenon is happening in the industrial world – only instead of smart cars, it’s smart machines.
Pushing Performance to a New Level
Smart machines harness the power of connected technologies, including Internet of Things (IoT) devices, cloud computing, and information and analytics tools.
Using these technologies, smart machines can collect data that has long been trapped inside machines and contextualize it into actionable information.
Because they deliver seamless connectivity, smart machines can share this information across all levels of an organization – or even out to the machine manufacturer – while also enabling new kinds of collaboration.
All of this can make smart machines an overwhelming concept for some machine builders. With so much potential, they simply don’t know where to start or what to do. For this reason, it can be helpful to understand how end users are already benefiting from smart machines today.
For example, some machine builders are using embedded sensors to track different aspects of machine performance.
This can help in identifying issues before they become downtime events. Others are using mobile devices to connect plant-floor workers with outside experts to speed up troubleshooting. And others are using remote access to monitor and improve the performance of machines that are spread out across multiple locations.
It’s also important that machine builders understand how they, too, can benefit from developing and selling smart machines.
Opportunities for OEMs
End users aren’t the only ones who historically have had limited visibility into machines. Most machine builders, in fact, don’t have any insights into how their machines perform after they’ve been delivered or commissioned.
This leaves machine builders in the dark when it comes to understanding how their solutions are meeting customers’ requirements.
The outcome is that most support is reactionary versus proactive and scheduled, which makes it difficult for machine builders to manage their service teams and understand exactly what kind of services would better address customer needs.
Now, machine builders can tap into data-collection and reporting capabilities to track every smart machine deployed in the field.
They can use this data to analyze machine performance, what is affecting it, and make improvements to current or future machine designs.
Furthermore, the ability to collect data and connect to end-user networks creates entirely new service opportunities for machine builders.
They can use remote monitoring, for example, to continually monitor their customers’ machines to improve support and troubleshooting, and even to reduce downtime events. This not only creates new revenue streams, but it also allows machine builders to expand on their relationships with customers and become partners in production.
Looking to the future, the value of smart machines – for the machine builder and the end user – will only continue to grow as new and more powerful capabilities take hold. This will likely include more sophisticated analytics for the implementation of predictive and prescriptive capabilities.
It may even include machine learning, in which the machine can track its own performance over time and make adjustments without human intervention.
However, it’s important to remember that the transition from legacy machines to smart machines is a journey.
Sometimes, the most important stage in a journey is the first step. If you’re looking to take your first step, visit this resource