RP3.1 Enhanced visualisation of complex manufacturing and production data – As data volumes on the shopfloor and at plant levels continue to increase and manufacturing systems become more integrated, maintaining situation awareness and coping with information overload pose a serious challenge. Future ICT solutions should focus on novel visualisation techniques which will abstract relevant data from real-world resources and business systems, and display relevant information to knowledge workers and decision makers. These data-visualisation systems should be role based, maintaining a level of abstraction and anonymity based on viewer access levels. RP3.1 would implement the key ICT recommendations CL5 Collaborative knowledge management for value creation, CN8 social network for HMI and IN10 Intelligent visualisation of big data. Industrial challenges Increasing volumes of data are produced during Potential outcomes different production phases that need to be processed for better comprehension; More efficient and faster decision making through Simulations, predictions and optimisations of factory accurate and filtered information; and product lifecycles are key factors to guarantee Better factory knowledge and increased efficiency and sustainability; and competitiveness through an informed workforce; Visualisation should support the seamless integration New of service related to data visualisation of lifecycle steps and provide agile methods to techniques; and recondition manufacturing processes and productions Cost savings and timely detection of exception plans. conditions and faults. ICT research requirements algorithms for developing engaging and immersive visualisations; and Reducing the complexity of high volume data Mobile apps for workers who use data-push through appropriate data clustering and mechanism to display KPIs and exception visualisation techniques; conditions. interaction methods including means for multi-cultural interactions data schema mapping – and translation; Holistic approaches for visualisation of Ambition Radar TRL models and simulation results of manufacturing systems for better human understanding; Impact Factor 3D visualisation of data with zoom-in and zoom-out browsing capabilities and mixed reality techniques; 2.2 Visualisation sharing using hosted social Horizon 2020 collaboration platforms which enable workers and By 2016 By 2018 By 2020 decision makers to discuss and brainstorm factory problems and process improvement strategies; Leveraging new user-interface technologies such as Silverlight, and next-generation
RP3.1 – Enhanced visualisation of complex manufacturing and production data As data volumes on the shopfloor and at plant levels continue to increase and manufacturing systems become more integrated, maintaining situation awareness and coping with information overload pose a serious challenge. Future ICT solutions should focus on novel visualisation techniques which will abstract relevant data from real-world resources and business systems, and display relevant information to knowledge workers and decision makers. These data-visualisation systems should be role based, maintaining a level of abstraction and anonymity based on viewer access levels. RP3.1 would implement the key ICT recommendations CL5 Collaborative knowledge management for value creation, CN8 social network for HMI and IN10 Intelligent visualisation of big data. Industrial challenges Increasing volumes of data are produced during different production phases that need to be processed for better comprehension; Simulations, predictions and optimisations of factory and product lifecycles are key factors to guarantee efficiency and sustainability; and Visualisation should support the seamless integration of lifecycle steps and provide agile methods to recondition manufacturing processes and productions plans. Potential outcomes More efficient and faster decision making through accurate and filtered information; Better factory knowledge and increased competitiveness through an informed workforce; New of service related to data visualisation techniques; and Cost savings and timely detection of exception conditions and faults. ICT research requirements Reducing the complexity of high volume data through appropriate data clustering and visualisation techniques; interaction methods including means for multi-cultural interactions – data schema mapping and translation; Holistic approaches for visualisation of models and simulation results of manufacturing systems for better human understanding; 3D visualisation of data with zoom-in and zoom-out browsing capabilities and mixed reality techniques; Visualisation sharing using hosted social collaboration platforms which enable workers and decision makers to discuss and brainstorm factory problems and process improvement strategies; Leveraging new user-interface technologies such as Silverlight, and next-generation algorithms for developing engaging and immersive visualisations; and Mobile apps for workers who use data-push mechanism to display KPIs and exception conditions. Ambition Radar TRL Impact Factor 2.2 Horizon 2020 By 2016 By 2018 By 2020
001-ActionPlanTD33FinalRoadmap.docx