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

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

Off-line Digital Manufacturing Process Optimisation on factory level


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

Title Decentralised architectures for optimised operations via virtualised processes and manufacturing ecosystem collaboration
Description DISRUPT aims to spearhead the transition to the next-generation manufacturing by facilitating the vision of a Smart Factory .
Comments 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
 D1.3 Use-cases and manufacturing goals Result title D1.3 Use-cases and manufacturing goals
 Periodic Reporting for period 1 - DISRUPT (Decentralised architectures for optimised operations via virtualised processes and manufacturing ecosystem collaboration) Result description a variety of production scheduling problems -innovative optimisation models for handling event-driven inbound logistics in a factory -initial testing and validation of the above on business cases -integration
 D2.3: The DISRUPT Platform Integration Plan Result comments Connected IT and OT MOM-SCADA systems connected Platform enabled optimisation
 Off-line Digital Manufacturing Process Optimisation on factory level Taxon title Off-line Digital Manufacturing Process Optimisation on factory level
 Added Value from user perspective Taxon description (mainly optimisation) and business models Based upon these mechanisms, added-value can be created in a variety of perspectives, such as the process perspective (what process or activity
 Data analytics Comments 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