DISRUPT | Decentralised architectures for optimised operations via virtualised processes and manufacturing ecosystem collaboration

Summary

Industry 4.0 is the next developmental stage in the organisation of the manufacturing value chain. ICT-based systems will play a major role, mainly by creating a virtual copy of the physical world and facilitating decentralised structures through Cyber-Physical Systems (CPS). Over the IoT, CPS cooperate with each other and humans in real-time. Via the Internet-of-Services, internal and cross-organisational services are utilised by participants of the value chain.

DISRUPT aims to spearhead the transition to the next-generation manufacturing by facilitating the vision of a Smart Factory . The new era of manufacturing asks for flexible factories that can be quickly reprogrammed to provide faster time-to-market responding to global consumer demand, address mass-customisation needs and bring life to innovative products. The traditional automation pyramid seems unable to accommodate this transformation.

Our concept is to DISRUPT that pyramid by utilising the capabilities offered by modern ICT to facilitate (i) in-depth (self-)monitoring of machines and processes, (ii) decision support and decentralised (self-)adjustment of production, (iii) effective collaboration of the different IoT-connected machines with tools, services and actors (iv) seamless communication of information and decisions from and to the plant floor and (v) efficient interaction with value chain partners.

Within DISRUPT, each element of production is controlled via the IoT by its virtual counterpart. The data collected is analysed to detect complex events that trigger automated actions. DISRUPT offers a set of decision support tools based on three core modules (modelling, simulation and optimisation) and a secure and flexible plug-n-play platform that will allow engineers from different disciplines to collaborate in developing services. It will be cloud-based to accommodate the anticipated high data volume and computational needs, while offering accessibility via any device anywhere in the world.

More information & hyperlinks
Web resources: http://www.disrupt-project.eu
https://cordis.europa.eu/project/id/723541
Start date: 01-09-2016
End date: 31-08-2019
Total budget - Public funding: 3 468 313,00 Euro - 3 468 313,00 Euro
Cordis data

Original description

"Industry 4.0 is the next developmental stage in the organisation of the manufacturing value chain. ICT-based systems will play a major role, mainly by creating a virtual copy of the physical world and facilitating decentralised structures through Cyber-Physical Systems (CPS). Over the IoT, CPS cooperate with each other and humans in real-time. Via the Internet-of-Services, internal and cross-organisational services are utilised by participants of the value chain. DISRUPT aims to spearhead the transition to the next-generation manufacturing by facilitating the vision of a ""Smart Factory"". The new era of manufacturing asks for flexible factories that can be quickly reprogrammed to provide faster time-to-market responding to global consumer demand, address mass-customisation needs and bring life to innovative products. The traditional automation pyramid seems unable to accommodate this transformation. Our concept is to DISRUPT that pyramid by utilising the capabilities offered by modern ICT to facilitate (i) in-depth (self-)monitoring of machines and processes, (ii) decision support and decentralised (self-)adjustment of production, (iii) effective collaboration of the different IoT-connected machines with tools, services and actors (iv) seamless communication of information and decisions from and to the plant floor and (v) efficient interaction with value chain partners. Within DISRUPT, each element of production is controlled via the IoT by its virtual counterpart. The data collected is analysed to detect complex events that trigger automated actions. DISRUPT offers a set of decision support tools based on three core modules (modelling, simulation and optimisation) and a secure and flexible ""plug-n-play"" platform that will allow engineers from different disciplines to collaborate in developing services. It will be cloud-based to accommodate the anticipated high data volume and computational needs, while offering accessibility via any device anywhere in the world."

Status

CLOSED

Call topic

FOF-11-2016

Update Date

27-10-2022
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Comment: Better monitoring and optimisation in production will definitely lead to such reductions although this cannot be estimated yet; hence 5% has been placed as a lower bound.
Comment: To deploy analytics both at the lower level, to extract conclusions on machine operations and factory parameters and to detect complex events, and at the higher level to monitor the behaviour of processes and products. Data analytics and Complex Event Processing (CEP) modules [SAG] mainly at lower levers.
Comment:

CloudBoard: offers multiple views and access rights to different human actors Decision Support Toolkit: supports decisions authorised by humans, especially in the shop floor Enterprise and Factory models: accessible and re-configurable through user interaction

Comment: Meta-models [BOC] will comply to the Open Model Initiative and possibly contribute to it. Data Analytics and IoT/CPS modules [SAG] will seek to comply to existing manufacturing standards and possibly test their effectiveness
Comment: Data Analytics and Complex Event Processing will deal with semantic interoperability and security at lower data acquisition layers
Comment: Meta-models will comply to the Open Model Initiative and possibly contribute to it.
Comment: To establish proof-of-concept by demonstrating the above on real industrial cases from the automotive and the consumer durables industry and measuring the impact in terms of cost efficiency, time-to-production and resource consumption. Advanced control algorithms and protocols for CPS [CNR]
Comment: Novel optimisation algorithms coupled with simulation for scheduling, planning and production planning, including robustness and resource-awareness [AUEB]
Comment: Decision Support Toolkit will tackle mid- to large-scale decision problems
Comment: To implement the entire system as a bundle of cloud-based tools and services
Comment: To implement the entire system as a bundle of cloud-based tools and services. Asymmetric business models: automobile [CRF] and electronics [ARCELIC] manufactures, IT providers in modelling [BOC], analytics [SAG] and simulation [SIMPLAN]
Comment: To develop a reference implementation of a collaboration platform serving a multi-sided market ecosystem
Comment: To establish proof-of-concept by demonstrating the above on real industrial cases from the automotive and the consumer durables industry and measuring the impact in terms of cost efficiency, time-to-production and resource consumption
Comment: To establish proof-of-concept by demonstrating the above on real industrial cases from the automotive and the consumer durables industry and measuring the impact in terms of cost efficiency, time-to-production and resource consumption. Advanced control algorithms and protocols for CPS [CNR]
Comment: Novel optimisation algorithms coupled with simulation for scheduling, planning and production planning, including robustness and resource-awareness [AUEB]