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Comment: Real-time co-simulation methods will enable key stakeholders to simulate and forecast complex activities. Simulation will target increasing quality and flexibility as well as more efficient management of logistics by also decreasing service time and costs.
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Comment: Using the COMPOSITION platform, companies will be able to enter a supply chain and broaden their clientele, thus improving their economic performance. The COMPOSITION Marketplace will facilitate matching of demand and supply across the supply chain. Buyers and sellers will have close to perfect knowledge of offers, with searchable, low latency access to information. Agent technology and automated matching of requests offers lowers transaction costs. By providing a common interface for entering the marketplace, the platform also lowers the barriers of entry. In economics terms, these are all prerequisites for competitive market (from which everyone benefits).
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Comment: The reduction of scraps will be addressed in the sense of attempting to reduce unused material, rather than reducing the number of defective workpieces or rework operations. Furthermore, the project will reduce the amount of scraps by optimising the processes in a way that less material has to be scrapped because certain maximal idle times are not reached.
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Comment: Improvements of the manufacturing process by using Process Modelling and Monitoring Framework combined with Integrated Digital Factories Models will lead to better resource management and will reduce the use of resources. Optimising the process involving factories with recycling companies will generate economic savings. Monitoring the health of machinery and using predictive maintenance and reducing energy consumption will reduce not only manufacturing efficiency (less downtime & material waste) but also the environmental impact of manufacturing.
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Comment: The project will focus on the reduction of energy consumption via the detection of excessive consumption and anomalous equipment behavior. The health of equipment will be improved, using machine learning/deep learning algorithms for predicting failures & arranging preventative maintenance. It will be possible to monetise the results into suggestions for actions at specific processes. The possibility of letting the Marketplace matchmaker take externalities into account when rating offers and partners by providing it with information on e.g. environmental performance of marketplace actors, is being examined.
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Comment:
Human-machine interaction is put in the center, supporting the decision making process.
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