Auflistung nach Autor:in "Krumeich, Julian"
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- ZeitschriftenartikelAdvanced User Assistance Systems(Business & Information Systems Engineering: Vol. 58, No. 5, 2016) Maedche, Alexander; Morana, Stefan; Schacht, Silvia; Werth, Dirk; Krumeich, Julian
- ZeitschriftenartikelPrescriptive Control of Business Processes(Business & Information Systems Engineering: Vol. 58, No. 4, 2016) Krumeich, Julian; Werth, Dirk; Loos, PeterThis paper proposes a concept for a prescriptive control of business processes by using event-based process predictions. In this regard, it explores new potentials through the application of predictive analytics to big data while focusing on production planning and control in the context of the process manufacturing industry. This type of industry is an adequate application domain for the conceived concept, since it features several characteristics that are opposed to conventional industries such as assembling ones. These specifics include divergent and cyclic material flows, high diversity in end products’ qualities, as well as non-linear production processes that are not fully controllable. Based on a case study of a German steel producing company – a typical example of the process industry – the work at hand outlines which data becomes available when using state-of-the-art sensor technology and thus providing the required basis to realize the proposed concept. However, a consideration of the data size reveals that dedicated methods of big data analytics are required to tap the full potential of this data. Consequently, the paper derives seven requirements that need to be addressed for a successful implementation of the concept. Additionally, the paper proposes a generic architecture of prescriptive enterprise systems. This architecture comprises five building blocks of a system that is capable to detect complex event patterns within a multi-sensor environment, to correlate them with historical data and to calculate predictions that are finally used to recommend the best course of action during process execution in order to minimize or maximize certain key performance indicators.
- KonferenzbeitragRealizing the predictive enterprise through intelligent process predictions based on big data analytics: A case study and architecture proposal(Informatik 2014, 2014) Krumeich, Julian; Schimmelpfennig, Jens; Werth, Dirk; Loos, PeterToday's globalized economy forces companies more than ever to constantly adapt their business process executions to present business situations. Companies that are able to analyze the current state of their processes and moreover forecast its most optimal progress as well as proactively control them based on reliable predictions will be a decisive step ahead competitors. The paper at hands examines, based on a case study stemming from the steel manufacturing industry, which production-related data is currently collectable using state of the art sensor technologies forming a potential foundation for a detailed situation awareness and derivation of accurate forecasts. An analysis of this data however shows that its full potential cannot be utilized without dedicated approaches of big data analytics. By proposing an architecture for implementing predictive enterprise systems, the article intends to form a working and discussion basis for further research and implementation efforts in big data analytics.
- KonferenzbeitragViewpoint-based modeling – Towards defining the viewpoint concept and implications for supporting modeling tools(EMISA 2012 – Der Mensch im Zentrum der Modellierung, 2012) Fischer, Klaus; Panfilenko, Dima; Krumeich, Julian; Born, Marc; Desfray, PhilippeViewpoint-based modeling is an important recent development in software engineering. It is likely to boost the wider use of modeling techniques because it allows to tailor existing tools with respect to the different stakeholders in software design. The paper reports on results from the VIBAM project in which viewpoint concepts are investigated. We give an overview of the most important contributions from literature regarding viewpoint concepts from which we derived the position that we take in the VIBAM project. After presenting VIBAM's position we derive features that we consider important for tools that support viewpoint features. We plan to integrate these features in the commercial modeling tools MODELIO and MEDINI ANALYZE to the end of the VIBAM project.