Espresso 4.0 by
Wizata
How is an industrial Internet of Things platform different from smart monitoring solutions like SCADA or similar others?
What is the difference? Let's take a step back. If I'm a process engineer, if I'm looking at machines and looking at monitors of what's happening in the machines, SCADA does the job well, right?
It gives me the information I need. Vibration, the energy consumption, it's a way of, in real-time, looking at what's happening in your facilities.
How does this compare to when you want to introduce digital and IoT platforms? How is it different?
We're not competing and don't want to replace these tools. We are just an IoT platform that offers something more.
IIoT Completes the SCADA
An IIoT platform offers something more on top of what SCADA, for example, offers. And yet one of the main functionalities or one of the main values this IIoT platform can bring is being able not only to check your data in real-time and get insights, which is already quite valuable for some clients but also being able to actually transform this data, to deeply use this data to create something new, like, for example, re-engineer your features.
Reengineering the features means that you not only have your sensors' data, but you are also able to produce new data, like, for example, delta ratios. You compute and transform your data to do further calculations to trigger or unhide information you otherwise won't have.
Secondly, the opportunity or the possibility to integrate artificial intelligence will bring value to the system, process optimization, or process control.
For example, integrating a quick anomaly detection system, an algorithm for an anomaly detection system, or something a little bit more advanced, like automating a furnace or a kiln.
SCADA or tools like SCADA are the first step to understanding or monitoring what's happening in your facilities and your process.
If you want to go a step further if you want to dive into what that data is telling you and how you can use the history of the data to understand what's going to happen in the future. To try to predict when failures will happen by studying what types of anomalies happen and why they happen. A root cause analysis based on those anomalies and then try to create predictive models, and using the data that your process and machine are already recollecting to create a more efficient process instead of just visualizing it.
Getting More Bussines Value from Your Data
Solutions like SCADA offer something very valuable: visualizing the data coming from your production and your assets. But our platform excels at getting more business value from the data you're getting and visualizing through SCADA. So they layer on top of each other and add additional value, and offer interesting and pretty cool features like predictive analytics.
With predictive analytics, you can use some of the machine learning models and AI capabilities to predict things related to deep asset health and performance and things that will affect the quality of your output.
The idea is to create a self-optimizing solution that learns from itself as the process goes along and the more data there is. It looks backward, reiterates, and learns from past anomalies and mistakes during production.
It's a model that continuously improves based on your knowledge, your know-how, and your day-to-day activity inside the process.
Value of the Knowledge Transfer
Something else valuable that the platform brings is you have all of these operators, engineers, and people on the floor who have been working in the same facility for years. They know so much about the process.
And these people will move on to new jobs or retire, and you have a lot of knowledge that will be lost. You have to transfer that knowledge to another engineer, but it comes with an experience like when specific problems happen, they must react. And that's something that AI models can integrate.
To help facilitators to understand when these problems arise, what to do, how to react and what to do, and how to prevent them. So that's also an added value of the knowledge transfer that you can pass on through your teams and digitalize that knowledge.
With IIoT, you also get data-driven decision-making compared to a smart monitoring tool where you can only visualize your data in a dashboard or in real time. No amount of data, good as it is, or intelligence or algorithms will help you if you're not putting them into practice and not relying on and trusting your technology to give you the information and acting upon it.
IIoT is Not Only for Large Enterprises
So far, we've talked about pretty complex systems and the end goals of what digitalization is trying to achieve.
But let's take a step back and look at small and medium enterprises. I'm not talking about super small and micro, maybe medium companies with the machine floor of not an insignificant amount of assets, all of which range in the pricing between 30,000 €100,000, maybe more.
They can likewise benefit from an IIoT platform that connects all their assets and creates a digital replica of the machine floor. They can not only monitor the data coming from the production, something that's not present or prevalent in such industries because they don't have SCADA or the finance and budgets that the big companies have.
So, they first see all of their assets performing in a digital form in a digital replica. Secondly, they can have something like an anomaly detection system. Maintenance issues and failures are not that big of a deal for such manufacturers, but they certainly play some role. And having a solution can cut down on the costs and downtime they're having, especially once those solutions are made more accessible, affordable, and democratized.
