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Production Data Analytics - To identify productivity potentials

M. Subramaniyan. Department of Product and Production Development, Chalmers University of Technology, Maskingränd 2, 412 58 Göteborg, Master Thesis, (1-128 2015)

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Adaptive Stream-based Shifting Bottleneck Detection in IoT-based Computing Architectures.H. Najdataei, M. Subramaniyan, V. Gulisano, A. Skoogh, и M. Papatriantafilou. ETFA, стр. 993-1000. IEEE, (2019)A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines.M. Subramaniyan, A. Skoogh, H. Salomonsson, P. Bangalore, и J. Bokrantz. Comput. Ind. Eng., (2018)Production Data Analytics - To identify productivity potentialsM. Subramaniyan. Department of Product and Production Development, Chalmers University of Technology, Maskingränd 2, 412 58 Göteborg, Master Thesis, (1-128 2015)A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective.M. Subramaniyan, A. Skoogh, A. Muhammad, J. Bokrantz, B. Johansson, и C. Roser. Comput. Ind. Eng., (2020)Stream-IT: Continuous and dynamic processing of production systems data - throughput bottlenecks as a case-study.H. Najdataei, M. Subramaniyan, V. Gulisano, A. Skoogh, и M. Papatriantafilou. ISIE, стр. 1328-1333. IEEE, (2019)An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection.C. Roser, M. Subramaniyan, A. Skoogh, и B. Johansson. APMS (1), том 630 из IFIP Advances in Information and Communication Technology, стр. 683-689. Springer, (2021)

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копироватьудалитьдобавить публикацию в буферЗапись сообществапосмотреть историю данной записиURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Production Data Analytics - To identify productivity potentials

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