Abstract
The success of most of today’s businesses is tied to the efficiency and effectiveness of their core processes. Yet, two major challenges often prevent optimal processes: First, the analysis techniques applied during the optimization are inadequate and fail to include all relevant data sources. Second, the success depends on the abilities of the individual analysts to spot the right designs amongst a plethora of choices. Our deep Business Optimization Platform addresses these challenges through specialized data integration, analysis and optimization facilities. In this paper, we focus on how it uses formalized process optimization patterns for detecting and implementing process improvements.
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References
Castellanos, M., Casati, F., Dayal, U., Shan, M.C.: A comprehensive and automated approach to intelligent business processes execution analysis. Distributed and Parallel Databases 16(3), 239–273 (2004)
Champy, J.: Reengineering Management. HarperCollins, New York (1995)
Hammer, M., Champy, J.: Reengineering the corporation: a manifesto for business revolution. Brealey, London (1993)
Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann, San Francisco (2006)
Leyman, F., Roller, D.: Production Workflow. Prentice-Hall, Englewood Cliffs (2000)
Niedermann, F., Maier, B., Radeschütz, S., Schwarz, H., Mitschang, B.: Automated process decision making based on integrated source data. In: Proceedings BIS 2011 (2011)
Niedermann, F., Radeschütz, S., Mitschang, B.: Deep business optimization: A platform for automated process optimization. In: Proceedings of the 3rd International Conference on Business Process and Services Computing (2010)
Niedermann, F., Radeschütz, S., Mitschang, B.: Design-time process optimization through optimization patterns and process model matching. In: Proceedings of the 12th IEEE Conference on Commerce and Enterprise Computing (2010)
Radeschütz, S., Mitschang, B.: Extended analysis techniques for a comprehensive business process optimization. In: Proceedings KMIS (2009)
Radeschütz, S., Niedermann, F., Bischoff, W.: Biaeditor - matching process and operational data for a business impact analysis. In: Proceedings EDBT (2010)
Reijers, H.A., Mansar, S.L.: Best practices in business process redesign: an overview and qualitative evaluation of successful redesign heuristics. Omega 33(4), 283–306 (2005)
Sharp, A., McDermott, P.: Workflow Modeling: Tools for Process Improvement and Applications Development. Artech House Publishers, Boston (2008)
Van der Aalst, W.M.P.: Re-engineering knock-out processes. Decision Support Systems 30(4), 451–468 (2001)
Wiener, N.: Cybernetics: Control and communication in the animal and the machine. MIT Press, Cambridge (1948)
zur Mühlen, M.: Workflow-based process controlling: foundation, design, and application of workflow-driven process information systems. Logos Verlag (2004)
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Niedermann, F., Radeschütz, S., Mitschang, B. (2011). Business Process Optimization Using Formalized Optimization Patterns. In: Abramowicz, W. (eds) Business Information Systems. BIS 2011. Lecture Notes in Business Information Processing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21863-7_11
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DOI: https://doi.org/10.1007/978-3-642-21863-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21829-3
Online ISBN: 978-3-642-21863-7
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