An Inferential Knowledge Model for the Digitalisation and Automation of Business Process Analysis

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Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "An Inferential Knowledge Model for the Digitalisation and Automation of Business Process Analysis", 
							Author= "Anne Füßl, Volker Nissen, Franz Felix Füßl and Simon Dopf", 
							Doi= "https://doi.org/10.30844/wi_2020_w1-fuessl", 
							 Abstract= "In the course of digitalisation, the service sector is undergoing rapid change, which is partly questioning the existing business models and influencing the conditions of competition. The consulting industry, too, is confronted with corresponding challenges despite the positive sales trend. The use of advanced technology-based tools based on artificial intelligence or analytical approaches could sustainably improve the competitive situation of consulting providers. Consulting projects often require a comprehensive analysis of business processes. The knowledge-based system iKnow presented here has mechanisms of automated inference as well as machine learning approaches. In contrast to process mining, it can also be used to support and partially automate process analysis if no evaluable log data is available. In this article, the previous concept of an inference-capable business process analysis tool is further developed, its usefulness is demonstrated and evaluated using an example process. With the help of a modeled knowledge base for process analysis, BPMN models can be automatically examined for weak points on the basis of analysis criteria and suitable improvement measures can be determined. Machine learning approaches can be used to continuously improve the analysis results.

", 
							 Keywords= "Automated Business Process Analysis, Knowledge Modeling, Machine Learning, Digital Consulting Technologies, Virtualisation of Consulting", 
							}
					
Anne Füßl, Volker Nissen, Franz Felix Füßl and Simon Dopf: An Inferential Knowledge Model for the Digitalisation and Automation of Business Process Analysis. Online: https://doi.org/10.30844/wi_2020_w1-fuessl (Abgerufen 16.04.24)

Abstract

Abstract

In the course of digitalisation, the service sector is undergoing rapid change, which is partly questioning the existing business models and influencing the conditions of competition. The consulting industry, too, is confronted with corresponding challenges despite the positive sales trend. The use of advanced technology-based tools based on artificial intelligence or analytical approaches could sustainably improve the competitive situation of consulting providers. Consulting projects often require a comprehensive analysis of business processes. The knowledge-based system iKnow presented here has mechanisms of automated inference as well as machine learning approaches. In contrast to process mining, it can also be used to support and partially automate process analysis if no evaluable log data is available. In this article, the previous concept of an inference-capable business process analysis tool is further developed, its usefulness is demonstrated and evaluated using an example process. With the help of a modeled knowledge base for process analysis, BPMN models can be automatically examined for weak points on the basis of analysis criteria and suitable improvement measures can be determined. Machine learning approaches can be used to continuously improve the analysis results.

Keywords

Schlüsselwörter

Automated Business Process Analysis, Knowledge Modeling, Machine Learning, Digital Consulting Technologies, Virtualisation of Consulting

References

Referenzen

1. Cole, T.: Digitale Transformation. Warum die deutsche Wirtschaft gerade die digitale Zukunft verschläft und was jetzt getan werden muss! Vahlen, München (2015)
2. Downes, L.; Nunes, P. F.: Big Bang Disruption. In: Harvard Business Review, 91, pp. 44– 56 (2013)
3. Hamidian, K.; Kraijo, C.: DigITalisierung – Status quo. In: Keuper, F.; Hamidian, K.; Verwaayen, E.; Kalinowski, T.; Kraijo, C. (eds.): Digitalisierung und Innovation, Springer, Wiesbaden (2013)
4. Werth, D.; Greff, T.; Scheer, A.-W.: Consulting 4.0–Die Digitalisierung der Unternehmensberatung. In: HMD Praxis der Wirtschaftsinformatik, 53, pp. 55–70 (2016)
5. Bughin, J.; Catlin, T.; Hiert, M.; Willmott, P.: Why digital strategies fail. In: MCKinsey Quarterly, January 2018 (2018)
6. Nissen, V.: Digital Transformation of the Consulting Industry – Introduction and Overview. In: Nissen, V. (eds.): Digital Transformation of the Consulting Industry – Extending the Traditional Delivery Model, Springer (2018)
7. Nissen, V.; Seifert, H.; Blumenstein, M.: Virtualisierung von Beratungsleistungen. Qualitätsanforderungen, Chancen und Risiken der digitalen Transformation in der Unternehmensberatung aus der Klientenperspektive. In: Deelmann, T.; Ockel, D. M. (eds.): Handbuch der Unternehmensberatung, Berlin (2015)
8. Füssl, F.: Entwicklung eines Modells zur Anwendung inferenzfähiger Ontologien im Software Engineering. Dissertation an der TU Ilmenau, Fak. IA, FG Swarch-Pl (2016)
9. Füssl, A.; Füssl, F.; Nissen, V.; Streitferdt, D.: A Reasoning Based Knowledge Model for Business Process Analysis. In: Nissen, V. (eds.): Digital Transformation of the Consulting Industry – Extending the Traditional Delivery Model, Springer (2018)
10. van der Aalst, W.: Process Mining. Data science in action. Springer, Berlin, Heidelberg, New York, Dordrecht, London (2016)
11. Jochem, R.; Balzert, S.: Prozessmanagement. Strategien, Methoden, Umsetzung. Symposion Publ, Düsseldorf (2010)
12. Peffers, K.; Tuunanen, T.; Rothenberger, M. A.; Chatterjee, S.: A Design Science Research Methodology for Information Systems Research. In: Journal of Management Information Systems, 24, pp. 45–77 (2007)
13. Schreiner, M.; Hess, T.; Benlian, A.: Gestaltungsorientierter Kern oder Tendenz zur Empirie? Zur neueren methodischen Entwicklung der Wirtschaftsinformatik. Arbeitsbericht, Institut WI und Neue Medien, Fak. BWL, LMU München (2015)
14. Krishnamoorthy, C.; Rajeev, S.; Chen, W.: Artificial Intelligence and Expert Systems for Engineers. Boca Raton: CRC Press (2018)
15. Füssl, A.; Nissen, V.; Füssl, F.; Dopf, S.: Entwicklung eines Werkzeugs zur Geschäftsprozessanalyse auf Basis eines inferenzfähigen Wissensmodells. In: Bruhn, M.; Hadwich, K. (eds.): Forum Dienstleistungsmanagement – Automatisierung und Personalisierung von Dienstleistungen, (in press) (2020)
16. Allweyer, T.: BPMN 2.0 – Business Process Model and Notation. Einführung in den Standard für die Geschäftsprozessmodellierung. BOD, Norderstedt (2015)
17. Klettke, M.; Meyer, H.: XML & Datenbanken: Konzepte, Sprachen & Systeme. dpunkt.verlag, Heidelberg (2003)
18. Skulschus, M.; Wiederstein, M.; Winterstone, S.: XSLT, XPath und XQuery. Comelio- Medien, Berlin (2011)
19. Ertel, W.: Grundkurs Künstliche Intelligenz. Eine praxisorientierte Einführung. GWV Fachverlage GmbH, Wiesbaden (2009)
20. Russell, S. J.; Norvig, P.: Artificial Intelligence. A Modern Approach. Pearson, Boston (2010)
21. Nissen, V.; Füssl, A.; Werth, D.; Gugler, K.; Neu, C.: On the Current State of Digital Transformation in the German Market for Business Consulting. In: Nissen, V. (eds.): Advances in consulting research. Recent findings and practical cases, pp. 317–339, Springer International Publishing, Cham (2019)

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