Abstract
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 optimization on top of a holistic process warehouse. It prescriptively generates action recommendations during process execution to avoid a predicted metric deviation. 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.
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References
Muehlen, M.Z., Shapiro, R.: Business Process Analytics. In: Vom Brocke, J., Rosemann, M. (eds.) Handbook on Business Process Management 2, pp. 137–158. Springer, Berlin (2010)
Kemper, H.-G., Baars, H., Lasi, H.: An Integrated Business Intelligence Framework. Closing the Gap Between IT Support for Management and for Production. In: Rausch, P., Sheta, A.F., Ayesh, A. (eds.) Business Intelligence and Performance Management, pp. 13–26. Springer, London (2013)
McCoy, D.W.: Business Activity Monitoring. Gartner Research Note (2002)
Melchert, F., Winter, R., Klesse, M.: Aligning Process Automation and Business Intelligence to Support Corporate Performance Management. In: Americas Conference on Information Systems (AMCIS), pp. 4053–4063. Assoc. f. Information Sys., New York (2004)
Radeschütz, S., Mitschang, B., Leymann, F.: Matching of Process Data and Operational Data for a Deep Business Analysis. In: Interoperability for Enterprise Software and Applications (IESA), pp. 171–182. Springer, Berlin (2008)
Gröger, C., Schlaudraff, J., Niedermann, F., Mitschang, B.: Warehousing Manufacturing Data. A Holistic Process Warehouse for Advanced Manufacturing Analytics. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 142–155. Springer, Heidelberg (2012)
Erlach, K.: Value stream design. The way to lean factory. Springer, Berlin (2011)
Gröger, C., Niedermann, F., Mitschang, B.: Data Mining-driven Manufacturing Process Optimization. In: World Congress on Engineering (WCE), pp. 1475–1481 (2012)
Han, J., Kamber, M., Pei, J.: Data Mining. Morgan Kaufmann, Waltham (2012)
Dapperheld, M.: Entwicklung analysebasierter Optimierungsmuster zur Verbesserung von Fertigungsprozessen. Master Thesis, University of Stuttgart (2013)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Evans, J.R., Lindner, C.H.: Business analytics. Decision Line 43, 4–6 (2012)
O’Brien, J.A., Marakas, G.M.: Management information systems. McGraw-Hill, New York (2011)
van der Aalst, W., Schonenberg, H., Song, M.: Time prediction based on process mining. Information Systems 36, 450–475 (2011)
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, 239–273 (2004)
Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M.S.M.: Business Process Intelligence. Computers in Industry 53, 321–343 (2004)
Kang, B., Lee, S.K., Min, Y.-B., Kang, S.-H., Cho, N.W.: Real-time Process Quality Control for Business Activity Monitoring. In: Computational Science and Its Applications (ICCSA), pp. 237–242. IEEE, Los Alamitos (2009)
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender systems. Cambridge University Press, New York (2011)
Giarratano, J.C., Riley, G.: Expert systems. Thomson Course Technology, Boston (2005)
Grob, H.L., Bensberg, F., Coners, A.: Rule-based Control of Business Processes - A Process Mining Approach. Wirtschaftsinformatik 50, 268–281 (2008)
Niedermann, F., Radeschütz, S., Mitschang, B.: Business Process Optimization Using Formalized Optimization Patterns. In: Abramowicz, W. (ed.) BIS 2011. LNBIP, vol. 87, pp. 123–135. Springer, Heidelberg (2011)
Schonenberg, H., Weber, B., van Dongen, B.F., van der Aalst, W.M.P.: Supporting Flexible Processes through Recommendations Based on History. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 51–66. Springer, Heidelberg (2008)
van der Aalst, W.M.P., Pesic, M., Song, M.: Beyond Process Mining: From the Past to Present and Future. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 38–52. Springer, Heidelberg (2010)
Conforti, R., de Leoni, M., La Rosa, M., van der Aalst, W.M.P.: Supporting Risk-Informed Decisions during Business Process Execution. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 116–132. Springer, Heidelberg (2013)
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Gröger, C., Schwarz, H., Mitschang, B. (2014). Prescriptive Analytics for Recommendation-Based Business Process Optimization. In: Abramowicz, W., Kokkinaki, A. (eds) Business Information Systems. BIS 2014. Lecture Notes in Business Information Processing, vol 176. Springer, Cham. https://doi.org/10.1007/978-3-319-06695-0_3
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DOI: https://doi.org/10.1007/978-3-319-06695-0_3
Publisher Name: Springer, Cham
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