|Bibliography||Gröger, Christoph; Schwarz, Holger; Mitschang, Bernhard: Prescriptive Analytics for Recommendation-based Business Process Optimization. |
In: Abramowicz, Witold (ed.); Kokkinaki, Angelika (ed.): Proceedings of the 17th International Conference on Business Information Systems (BIS), 22-23 May, 2014, Larnaca, Cyprus.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
176, pp. 25-37, english.
Springer, May 2014.
Article in Proceedings (Conference Paper).
|CR-Schema||H.2.8 (Database Applications)|
|Keywords||Prescriptive Analytics, Process Optimization, Process Warehouse, Data Mining, Business Intelligence, Decision Support|
Continuously improved business processes are a central success factor for companies. Yet, existing data analytics do not fully exploit the data generated during process execution. Particularly, they miss prescriptive techniques to transform analysis results into improvement actions. In this paper, we present the data-mining-driven concept of recommendation-based business process op-timization on top of a holistic process warehouse. It prescriptively generates ac-tion recommendations during process execution to avoid a predicted metric de-viation. We discuss data mining techniques and data structures for real-time prediction and recommendation generation and present a proof of concept based on a prototypical implementation in manufacturing.
|Department(s)||University of Stuttgart, Institute of Parallel and Distributed Systems, Applications of Parallel and Distributed Systems|
|Entry date||April 15, 2014|