MACHINE LEARNING FOR INDUSTRY SOLUTIONS

OPTIMIZATION OF PROCESSES

SOLUTIONS

55%
Reduction of critical machine failures
35%
Increase in machine availability
30%
Increase in mean time between failures (MTBF)
45%
Reduction of maintenance costs

INDUSTRIES

Automotive

The sector that has always receiving the greatest implementation of ML technologies

Construction

Optimization systems for infrastructure maintenance and prediction of future failures.

Steel

Especially susceptible to connect with quality inspection systems due to critical processes.

Food/Farming

ML technologies allow real time control the state of crops and predict quality incidents.

Experts in industrial data analytics and integration

COVERING THE WHOLE PROCESS

Industrial Data Analytics

Industrial Data Analytics  is a solution that includes 3 main modules: Integration + Big Data Module + Predictive Analytics. The system integrates and processes the different information sources of the production line on a Big Data architecture in real time (Automation & PLCs / Sensors / Databases / IoT Devices / GMAO Systems / MES / ERP) and executes predictive analytics and process optimization systems according to the needs of the production process (Predictive Maintenance / Predictive Quality / Energy Efficiency / Supply Chain Optimization).

The system allows executing predictive analytics verticals on Industrial IoT Platforms or Corporate Management Systems such as PI Osisoft, Nexus Integra or SAP HANA among others.

MAIN CUSTOMERS

PARTNERS NETWORK

Sensory for predictive analytics

Specialist partners in sensor installation for predictive maintenance

IoT Networks

IoT networks installation for joint communication of measurement sensors

Systems interoperability

Our systems are integrated in IoT platforms, MES Systems, or Corporate Tools to achieve a unified management

Automation

Engineering specialized in automation and electro-mechanical systems for industry

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The project “PieceOfCake” has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 817240.