Engineering Innovation in
Digital Industry
A suite of solutions to manage the huge amount of industrial data coming from interconnected factory devices for supporting manufacturing monitoring and control.
Approach & Solution
Approach
Manufacturing companies are continuously facing the challenge of redesigning and adjusting their systems to produce goods adapted to specific requirements and produced under the minimum required production rate, guaranteeing high quality and limiting the use of resources.
Therefore, reducing waste, scraps and defects, as well as production costs and lead times is crucial to increase productivity.
Moreover a successful smart factory needs to manage data-related processes along the entire data life cycle, including data collection, storage, distribution, analysis, use, and deletion, to ensure high data quality at all times.
The i4Q Project aims to provide a complete solution able to manage the huge amount of industrial data for supporting manufacturing online monitoring and control.
Therefore, reducing waste, scraps and defects, as well as production costs and lead times is crucial to increase productivity.
Moreover a successful smart factory needs to manage data-related processes along the entire data life cycle, including data collection, storage, distribution, analysis, use, and deletion, to ensure high data quality at all times.
The i4Q Project aims to provide a complete solution able to manage the huge amount of industrial data for supporting manufacturing online monitoring and control.
Solution
The i4Q Project's response consists of an IoT-based Reliable Industrial Data Services (RIDS), a complete suite composed of 22 i4Q Solutions, founded on a modular Framework, rooted in a Reference Architecture.
The i4Q RIDS aims to support the complete flow of industrial data, starting from data collection to data analysis, simulation and prediction. It provides solutions to ensure data quality, security and trustworthiness.
Engineering is responsible of the design of the Reference Framework which incorporates the business, usage, functional and implementation viewpoints and supports the validation actvities in the WHIRLPOOL and BIESSE pilots. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 958205.
The i4Q RIDS aims to support the complete flow of industrial data, starting from data collection to data analysis, simulation and prediction. It provides solutions to ensure data quality, security and trustworthiness.
Engineering is responsible of the design of the Reference Framework which incorporates the business, usage, functional and implementation viewpoints and supports the validation actvities in the WHIRLPOOL and BIESSE pilots. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 958205.
Results
Industrial data management system
AI-based data analytics tools
Software process quality diagnostics
Simulation models for the plant reconfiguration
Procedure for certification and audit
Project value
Process performance
Cost cutting
Innovation
Enabling Technologies
Blockchain
Cloud
AI & Advanced Analytics
IoT
Digital Twin
Project Team
Research & Innovation