Smart Manufacturing: La Marzocco’s Digital Transformation

Practices used
AWS services involved
  • Amazon API Gateway
  • Amazon S3
  • AWS IoT Core
  • AWS Lambda
  • DynamoDB
  • Glacier

La Marzocco is an Italian company famous for its excellence in producing espresso coffee machines.

By partnering with beSharp, the company has optimized the efficiency of its production lines through performance measurement and data analysis.

The Challenge

  • Measuring production performance KPIs was very complex since most of the mechanical parts assembly process is still 100% manual.
  • The only available indicator for tracking production efficiency was the stationing time of each component at the assembly stations. Delays were measurable, but the causes could not be identified.
  • Data from each workstation was collected in silos and could only be viewed and analyzed in retrospect.

The Solution

By developing a data platform, beSharp enabled the customer to collect data in near real-time and ingest it into the cloud from sensors installed at assembly stations. The data is stored in a centralized database, historized, and correlated for visualization and monitoring purposes. Thanks to a scalable and resilient Cloud infrastructure, the solution can grow alongside the business, adapting to increasing workstations and production lines.

TECH POINT OF VIEW

beSharp developed for La Marzocco an Industry 4.0 platform leveraging serverless services like AWS Lambda and IoT solutions such as AWS IoT Core. The platform includes a real-time Big Data analysis solution using Amazon DynamoDB for time series collection, while CloudWatch Logs and Amazon OpenSearch handle logs aggregation and analysis. The high-performing infrastructure is built with Amazon API Gateway, AWS Lambda, and Amazon DynamoDB, complemented by Amazon S3 and Amazon Glacier for long-term, cost-effective data storage.

The Benefits

  • Continuous production monitoring (e.g., delays, component dwell time, unexpected stops).
  • Production processes optimization
  • Waste reduction.
  • Improved quality of life and reduced health risks for operators
  • Easy identification of sudden and systematic slowdowns, both at individual workstations and across the entire production line.
  • Future-proof infrastructure.

Case Study

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