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2016 49th Hawaii International Conference on System Sciences (HICSS) (2016)
Koloa, HI, USA
Jan. 5, 2016 to Jan. 8, 2016
ISSN: 1530-1605
ISBN: 978-0-7695-5670-3
pp: 5065-5074
With the advent of Industry 4.0, industrial manufacturing systems constantly evolve into smart, interconnected production systems. Pervasive integration of information and communication technology into productional components results in massive amounts of various data. To meet the challenges that arise from an increasingly competitive market and more demanding customer requirements, technological drivers have to be leveraged in order to process data effectively. One important aspect in that regard is the efficient management of business processes and process risks. As an integrative concept in these areas is missing, we present a holistic framework for data-driven risk assessment based on real-time data. Besides a conceptual model, we provide a technical concept that combines methods for risk assessment with performance metrics and demonstrate a software implementation in the context of an exemplary use case scenario. Finally, we present the results of expert interviews and a discussion indicating future research directions.
Risk management, Industries, Production, Manufacturing, Context, Real-time systems
Tim Niesen, Constantin Houy, Peter Fettke, Peter Loos, "Towards an Integrative Big Data Analysis Framework for Data-Driven Risk Management in Industry 4.0", 2016 49th Hawaii International Conference on System Sciences (HICSS), vol. 00, no. , pp. 5065-5074, 2016, doi:10.1109/HICSS.2016.627
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