TY - CHAP A1 - Wostmann, Rene A1 - Schlunder, Philipp A1 - Temme, Fabian A1 - Klinkenberg, Ralf A1 - Kimberger, Josef A1 - Spichtinger, Andrea A1 - Goldhacker, Markus A1 - Deuse, Jochen T1 - Conception of a Reference Architecture for Machine Learning in the Process Industry T2 - 2020 IEEE International Conference on Big Data (Big Data): 10.12.2020 - 13.12.2020 Atlanta, GA, USA N2 - The increasing global competition demands continuous optimization of products and processes from companies in the process industry. Where conventional methods of Lean Management and Six Sigma reach their limits, new opportunities and challenges arise through increasing connectivity in the Industrial Internet of Things and machine learning. The majority of industrial projects do not reach the deployment or are isolated solutions, as the structures for data integration, training, deployment and maintenance of models are not established. This paper presents the conception of a reference architecture for machine learning in the process industry to support companies in implementing their own specific structures. The focus is on the development process and an exemplary implementation in the brewing industry. KW - Big data KW - Companies KW - Industrial Internet of Things KW - Industries KW - Machine learning KW - Optimization KW - process industry KW - Production KW - Reference architecture KW - Six sigma KW - Training Y1 - 2020 SN - 978-1-7281-6251-5 U6 - https://doi.org/10.1109/bigdata50022.2020.9378290 SP - 1726 EP - 1735 PB - IEEE ER -