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Titlebook: Advances in Production Management Systems. Smart Manufacturing for Industry 4.0; IFIP WG 5.7 Internat Ilkyeong Moon,Gyu M. Lee,Gregor von C

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Barry Doyle,Karol Miller,Poul M.F. Nielsenacturing costs and longer cycle time. Thus, in-line metrology is set at various steps to inspect the wafer in real time, which often causes lots of inspection costs and also increases cycle time. This study aims to develop a framework for in-line metrology sampling to determine the optimal sampling
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Computational Biomechanics for Medicineive, e.g. by bundling their products with smart services. In addition to limited resources, often the necessary knowledge is missing to select the right technologies and develop attractive services based on their functionality. Due to the resulting high risks, SMEs neglect the chances of digitizatio
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https://doi.org/10.1007/978-3-031-09327-2tailer. With QR, the retailer has an opportunity to reorder after the random demand is realized and the manufacturer is able to reproduce after that. We characterize the equilibrium decisions for each channel and investigate the impacts of QR on the decisions and profits. Through numerical analysis,
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Computational Biomechanics for Medicineeation of integrated systems, networked products and improvement of service portfolios. However, for many companies deriving and evaluating necessary measures to use Industrie 4.0 potentials represents a major challenge. This paper introduces the “acatech Industrie 4.0 Maturity Index” as an approach
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Computational Biomechanics for Medicinea through the technologies and elements of Industrie 4.0 open up promising potentials for increasing efficiency. This paper presents the impacts of Industrie 4.0 technologies and elements on Production Planning and Control that have been studied by a deductive research design. Through a quantitative
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Computational Biomechanics of the Hip Jointled by big data, pose completely new requirements and challenges for modern components of manufacturing systems. Challenges range from plug and produce to digital twin as well as reconfiguration. To meet the requirements of customers and industry alike, components within a manufacturing line need to
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https://doi.org/10.1007/978-3-319-99707-0artificial intelligence; Business Process Management (BPM); computer networks; Computer-Aided Design (C
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