Case study
An Italian company that makes machines for packaging, bottling and filling food products.
~20 M€
34 machines
challenges
Manufacturers of assets (machines) that typically perform tasks as part of a production process, like NC milling systems, packaging machines, robots or industrial printers, are required to design products that seamlessly integrate with Industry 4.0 and support their customers’ digital transformation initiatives, while enabling them to improve product reliability, service levels and new, “as a service” business models. To achieve this, close monitoring of these assets is crucial to ensure maximum availability and optimal performance. For example: production machines status and performance data is essential to ensure effective operation and optimised service schedules. Historical and real-time data from sensors, field buses, PLC’s and logs are the basis for applying analytics on a cloud level for predictive and preventive maintenance.
solution
IoT-based solutions continually collect, compute and communicate vital data from field assets to the cloud. Eurotech’s ReliaGATE IoT Gateways are adept at collecting and aggregating data from as different sources as vehicle field buses (e.g. Modbus, OPC-UA, S7, etc.), and sensors. The established communication channels are bi-directional, and control information can be sent back to the field assets, as well. Digital twins of the field assets, APIs and no-code programming of the IoT Gateway ensure efficient application development. Solid security implementation, advanced logging features and OTA (Over-The-Air) device management are foundational elements not only for a reliable service delivery to the customer, but also for the implementation of new applications and business models. The seamless integration with the AWS services facilitates efficient data storage and processing in the cloud.
results
Data is made available not only to Galdi, the asset manufacturer, but also resides on the AWS cloud for analytics, business logic and visualisation purposes, increasing situational awareness & operational efficiency. According to Deloitte, on average, predictive maintenance alone increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25%.
