Industry 4.0 is moving fast and I’d like to let you in on a few very valuable developments about the data you’ve been collecting. It can now help you make better decisions. You can talk to your industrial data and it’s talking back to you, letting you know what’s working inside your operation and what’s not.
It’s exciting for me to see GrayMatter and our partners innovating by taking the data you’ve been collecting through sensors on industrial equipment and applying artificial intelligence and machine learning in the cloud so you can find insights on performance. Then you know exactly where to make improvements.
You need a system to sort through the haystack of data and pull needles out to focus your subject matter experts. That’s what we can do now.
It all starts by framing up the action strategy in three parts.
Start at the end and work backwards.
What return on investment do you want to see? You don’t need all the data you think you need. What information will help you solve the problems you want to solve? What’s the path to getting there? Having this roadmap first is critical, because otherwise a lot of time and money can be wasted.
You hear the term digital twin, but what does it really mean?
Simply put, creating a digital twin is the process of merging physical and digital worlds.
The process takes a physical machine and uses technology to get all the information about past states, present states and predictions. That information creates a digital model that’s alive – taking in a stream of data – using that to adjust so the model is personalized to be a precise representation of the asset.
The software version is used for what used to be a physical inspection – requiring people to be right next to the machine. The virtual version can be done from anywhere and at any time, expanding the value of those inspections and allowing them to have more of a real-time impact. It creates a constant inspection that allows the operators to predict failures sooner.
The digital model of a machine, built and run in a virtual environment used to be available only to the biggest companies with the largest budgets. But the Industrial Internet and an explosion in sensor technology have lowered the cost and broadened the access beyond the elite.
People are not only connected to people, they’re connected to every kind of device at home and now work. Manufacturers stand to win big from this. Factory floors are outfitted with tremendous amounts of sensors to collect data, but because that data has been locked up it hasn’t provided value.
The digital twin allows us to unlock that data and not just for one asset at a time. We can now model machines in groups – for example, a machine builder with thousands of machines installed across hundreds of customers – will now be able to operate best in class using digital twins.
There’s potential to unleash productivity and efficiencies like we’ve never seen before.
In order to move to more advanced use cases, such as adaptive diagnostics, condition-based maintenance or predictive failure, Industrial IoT systems need to know more than simply the current device state.
They need to know why. Knowing current device state only helps from a monitoring standpoint. While important, it’s really just the beginning of what we can expect out of IIoT systems. If we know why an asset exhibits a certain state, we can determine what conditions lead to that state and take proactive steps to prevent future occurrences.
This new layer of getting insight through behavioral information allows you to ask for more. You can search your data and get answers back right away. It’s like an instant messenger for operational technology.
Achieving this may mean digital twins built using multiple discrete machine learning algorithms potentially spread across multiple IoT platforms, not simply relying on one. Eventually, we should expect that digital twins will interact with one another in virtual space.
If you’re short on time, staffing or budget – GrayMatter can get you up and running to achieve value quickly. You know you need an Iot strategy in the near future, but may not know how to go about it. Rather than trying to design, source and build it yourself we can put the strategy in place in days or weeks.
You also don’t have to do everything at once, you can start with a limited selection of assets and scale up or down as you learn performance and asset behaviors.
Our strategy is a Salesforce version of a remote monitoring and diagnostics center that you can buy and implement incrementally.
We use data, predictive capabilities and machine learning to identify your best and worst performers in each asset group. Your operators are automatically alerted to the worst performers, then they use an intuitive web interface, to turn the worst into the best.
Continuous improvement becomes expected, simplified, and routine. As your team builds new improvements or optimized settings, they can be scaled out, automatically, to every instance of a specific machine or piece of equipment.
The complex algorithms that can leverage your data are pre-built so anyone can start creating the models and analytics to generate insights. One person no longer holds the keys to data, with this unique platform everyone gets a better understanding of your businesses processes, so you’re not focusing on the math to bring the insight, you’re focusing on creating better outcomes for your customers.
You need to think big to truly transform your organization, but you also have to start acting on your data today.
We’re anxious to spread the word about how easy this is and to un-complicate it for you. Let me know if you’d like to discuss further.
– Jim Gillespie, GrayMatter CEO.
Click to download the case study to start acting on your data:
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