Detail Project and Funding

Operational Knowledge from Insights and Analytics on Industrial

OK-INSAID

Funded by: Ministero dell’Istruzione, Università e Ricerca (MIUR)  Calls: PON e FSC – Fabbrica Intelligente – domanda ARS01_00917
Start date: 2018-07-01  End date: 2021-01-31
Total Budget: EUR 9.834.393,00  INO share of the total budget: EUR 200.000,00
Scientific manager: Adriano Rippa   and for INO is: Avino Saverio

Organization/Institution/Company main assignee: Engineering Ingegneria Informatica S.p.A.

other Organization/Institution/Company involved:
CEFRIEL S.CONS.R.L.
Consorzio CALEF
EKA Srl
GE Avio s.r.l.
SACMI COOPERATIVA MECCANICI IMOLA SOCIETA’ COOPERATIVA
Tera Srl
Università degli Studi di PALERMO
Università del SALENTO

other INO’s people involved:

Abstract: OK-INSAID proposes scientific, technological, and application innovation in Industrial Data Analytics to help redesign of actual manufacturing networks and processes by leveraging data and analytics to achieve a step change in value creation, by transforming existing manufacturing processes and business models.
To this aim it will integrate and demonstrate the potential of Big Data technologies to deliver new digital services in the industrial sector.
OK-INSAID recognizes the potential of industrial data that is far to be exploited by industries: data is potentially available; industries are not sufficiently able to extract the value (sometimes hidden) “inside” them.
To this end, OK-INSAID will adopt and evolve state-of-the-art technology and define new data driven methods for industrial applications.
OK-INSAID proposes a novel approach to industrial analytics based on coordination, synchronization, and collaboration among analytics in cloud and at the edge.
The approach will be supported by a reference architecture and a reference implementation to adopt in order to develop novel hybrid cloud-edge industrial analytics for Industry4.0.
The specific innovations introduced by the project are:
•Novel models and methods for industrial data ingestion and integration from many different heterogeneous sources to create enterprise-level industrial data spaces;
•Novel algorithms and data science methods for generating value and operational knowledge from Industrial Big Data coming from the above-mentioned sources.
The focus will be on (near) real-time analytics and stream processing.
•Novel industrial analytics services, by integrating the developed algorithms into applications that exploit the distributed data processing and analytics model and the potential and the information value of enterprise-level industrial data spaces.
•Advanced methods for industrial data security, in order to assess possible vulnerabilities (i.e. data breaches, data theft, etc.) and implement proper protection measures and counter-measures on industrial data.
•Advanced data visualization methods for providing the insights, value, and operational knowledge extracted from data available to relevant users and stakeholders, including novel user interfaces for wearable, mobile personal devices, augmented/virtual reality, etc.
The OK-INSAID approach, architecture and reference implementation will be validated and demonstrated in operational environments provided by AVIO, SACMI, CRF/FCA.