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

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.