Bottero Machines Monitoring

Identify operation patterns


Bottero needs a method to collect data from the machines installed in final customers facilities in order to identify usage patterns, faults frequency, and workloads. Also, Bottero wants to provide to final customers a system to monitor their own machines and the production efficiency.

About Bottero

Bottero offers a complete range of solutions in the field of automatic machines for glass processing from standard products for medium-small companies up to designing and construction of entire production lines for large international industrial groups.

Bottero is the only company in the world able to provide experience and high technology in any field of the glass processing industry by supplying proper equipment apt to each specific production, from flat monolithic and laminated glass to the production of bottles and jars, from complete production lines for float and laminated glass sheets to packaging lines.

The Solution

An AWS backend infrastructure and an edge agent were designed in order to ingest data from machines during the utilization. The aim of the application is to collect data from multiple machines to detect faults, alarms and also to monitor production condition (e.g., number of pieces produced, amount of workload, ...). In the implemented solution, the edge agent integrated into Bottero’s machines sends all the data collected during the operation to a centralized backend through a Kinesis stream. The agent logic is able to manage machines disconnection and sends data acquired during disconnection periods when the connection is restored. Data ingested, stored and analyzed in the AWS backend are available for visualization through BI dashboards.


With the implemented solution Bottero is able to monitor machine operations and consequently identify operation patterns that are useful to advice final customer of practical actions to improve the efficiency of a machine and also to evaluate improvements on the design of their own products based on real utilization patterns. Also, the solution provide a method to identify machine faults and alarms through a centralized monitoring tool.