Interview with Raffaele Caliò

“I joined the Service in 2017 to help the R&D area develop innovative products linked to servitization and industry 4.0.

We initially launched an augmented reality service, using supports such as HoloLens glasses to conduct remote assistance with a view to solving problems that occur in the field. This pilot project was short-lived as it was overtaken by the development of everyday devices like smartphones and tablets; consequently, the service is now provided quickly and easily via video call apps.

The development of the cMaster software was a major step forwards: this platform centralises machine data harvesting and makes excellent use of scalability, reliability and the performance of cloud environments. Since these systems (e.g. Amazon Cloud) provide continuous, reliable and easy-to-install service, users can thus count on true agility. The adoption and implementation of this software helped us take a big step towards innovation, thus improving our overall Service offering.

What makes cMaster so special is that we’ve developed a proprietary software product, built around our customers’ needs. Customers can log onto this centralized web interface using their credentials and get a clear real-time picture of how their machines and lines are performing. Warnings are also displayed. Users can view machine status and statistical data, which can be filtered and logged in a structured manner. For example, they can immediately see what type of production was in progress a month ago. Data – including real-time data – is always there, always accessible. Machine performance warnings provide valuable information, allowing users to analyse any recurring events and take appropriate action.

Further added value comes in the form of the digital machine maintenance table: this provides clearly-organised, objective data that helps us plan targeted maintenance, read any warnings, interpret them and include them in the monthly productivity report.

These advanced maintenance programmes bring us closer to the customer and, consequently, improve the efficiency of our machines. Before such programmes were introduced, data could be taken out of context and interpreted subjectively. Not any more: now it’s rock-solid and fully updated. This greatly improves our production analysis capacity, optimising past data”

“Cloud-based systems and broadband, plus easy access to some remote assistance platforms, have exponentially accelerated the innovative development of our advanced maintenance processes.”

“Yes indeed. While cMaster checks and gathers data on a customer-by-customer basis, our Control Room lets us aggregate it: this is our own internal control panel. For example, the Control Room monitor alerts us to any warnings; this means we can proactively contact customers to solve any problems in real time, all while developing valuable, integrated KPIs.

In our industry, Machine Learning has yet to be fully developed as our customers have many different ways of working and behaviour cannot easily be generalised.
However, in Service we’ve started to analyse the working conditions of the machines: if, for example, we see a lot of warning sequences, cMaster tells us whether it’s a potential false alarm or a serious, potentially line-stopping situation. On this basis we’ve developed a basic Machine Learning routine to provide customers with support in the form of specific notifications (e.g. for PLC battery replacement).

As regards KPIs, we’re able to compare a specific machine to the operational average of the family of machines to which it belongs. Consequently, we can then analyse how to improve its performance. All such functions are included in the advanced maintenance services.

We’re also working to gives customers the ability to purchase consumables from cMaster. This would let them skip some steps and make the process faster and more efficient. In parallel, in the future we also foresee the development of an e-commerce spare parts website with a smart, user-friendly interface.

On the HMI (Human-Machine Interface) side, we’re working on a direct problem-reporting alarm that activates the assistance service which, in turn, will take care of issues more efficiently and proactively.”