Knowledge Awareness and Prediction of Man Machine Material and Method in Manu...
Knowledge Awareness and Prediction of Man Machine Material and Method in Manufacturing
Manufacturing is the driving force of Europe's economy, providing over €6,553 billion in GDP. However against a background of climate change legislation, volatile energy prices and increased environmental awareness, modern manufac...
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31/12/2013
SAP SE
13M€
Presupuesto del proyecto: 13M€
Líder del proyecto
SAP SE
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Fecha límite participación
Sin fecha límite de participación.
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Información proyecto KAP
Líder del proyecto
SAP SE
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
13M€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
Manufacturing is the driving force of Europe's economy, providing over €6,553 billion in GDP. However against a background of climate change legislation, volatile energy prices and increased environmental awareness, modern manufacturing must encompass a focus on eco-efficiency. Given the current economic situation, this must be achieved without the need for large capital expenditure. Adding information technology to an already existing production facility is a cost-effective investment. The KAP project will deliver energy management standards and a technology framework for next generation, sustainable manufacturing. KAP stands for Knowledge of past performance, combined with Awareness of the present state, which together can support Prediction of future outcomes. This philosophy forms the basis of a framework that will enable every existing resource to be used as efficiently as possible through the effective co-ordination of man, machine, material and method. To achieve this goal the project will define a range of sustainable manufacturing standards. Measurements will be gathered through a factory-wide network of sensors. Complex Event Processing (CEP) and data stream analysis will compute on-the-fly production performance indicators (PPIs) for real-time monitoring. Data mining in combination with OLAP will support problem diagnosis and resolution. Computational learning techniques will create a self-improving system for operational control. The inclusion of energy management makes the interpretation of system data an even greater challenge. Perceptually efficient visualisations will communicate PPI's to decision makers in a format that will reduce cognitive workload and improve situation awareness. A well-balanced consortium of research centres, academic and industry partners provides an ideal opportunity to realise the innovations proposed by the project. In terms of impact, partners estimate reductions of over 5% p.a in waste and energy and 10% in time to market.