Towards an Integrative Big Data Analysis Framework for Data-driven Risk Management in Industry 4.0

Tim Niesen, Constantin Houy, Peter Fettke, Peter Loos

In: Proceedings of the 49th Annual Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences (HICSS-2016) 49th January 5-8 Kauai HI United States Pages 5065-5074 IEEE Computer Society 2016.


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.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz