{"id":115448,"date":"2026-11-11T11:21:04","date_gmt":"2026-11-11T10:21:04","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=115448"},"modified":"2026-11-11T11:22:04","modified_gmt":"2026-11-11T10:22:04","slug":"an-inferential-knowledge-model-for-the-digitalisation-and-automation-of-business-process-analysis","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/an-inferential-knowledge-model-for-the-digitalisation-and-automation-of-business-process-analysis\/","title":{"rendered":"An Inferential Knowledge Model for the Digitalisation and Automation of Business Process Analysis"},"content":{"rendered":"\n

Abstract<\/strong> <\/p>\n\n\n\n

In the course of digitalisation, the service sector is undergoing rapid \u00a0change, which is partly questioning the existing business models and \u00a0influencing the conditions of competition. The consulting industry, too, is \u00a0confronted with corresponding challenges despite the positive sales trend. The \u00a0use of advanced technology-based tools based on artificial intelligence or \u00a0analytical approaches could sustainably improve the competitive situation of \u00a0consulting providers. Consulting projects often require a comprehensive \u00a0analysis of business processes. The knowledge-based system iKnow presented \u00a0here has mechanisms of automated inference as well as machine learning \u00a0approaches. In contrast to process mining, it can also be used to support and \u00a0partially automate process analysis if no evaluable log data is available. In this \u00a0article, the previous concept of an inference-capable business process analysis \u00a0tool is further developed, its usefulness is demonstrated and evaluated using an \u00a0example process. With the help of a modeled knowledge base for process \u00a0analysis, BPMN models can be automatically examined for weak points on the \u00a0basis of analysis criteria and suitable improvement measures can be determined. Machine learning approaches can be used to continuously improve the analysis \u00a0results.<\/p>\n\n\n\n

Research Motivation in the Context of Digital Consulting<\/strong> Technologies<\/strong><\/p>\n\n\n\n

Digitalisation can be described as a change from real processes and services to \u00a0information system based services [1]. Today it forms the basis of many innovative \u00a0business models which aim at the flexibilisation, individualisation and autonomisation \u00a0of services [2\u20134]. Bughin et al. [5] defines digitalisation as the ability to network people, devices and physical objects independently of time and place. The improved processing and analysis capability of information from large data sets leads to a \u00a0higher level of automation of processes and decisions. \u00a0<\/p>\n\n\n\n

Consultancies are faced with the challenge of reacting to digital changes and using  new technologies to innovatively transform classic consulting processes and business  models and to adapt to the changed consulting market [6]. One possible approach is  the virtualisation of consulting processes. This means the personal on-site contact  between consultant and client is reduced to a reasonable minimum through the use of  technology [7]. The extreme case of a purely technology-based consulting approach  can be described as full automation [4, 7]. This requires suitable IT artefacts. The  generic tool iKnow presented in this article is based on a formalised knowledge  model, mechanisms of automatic inference and machine learning approaches [8]. Due  to the knowledge-intensive character of the consulting industry, iKnow can be used  specifically for knowledge processing and decision support within consulting  projects. In a first project, the analysis of business processes will be considered [9].  <\/p>\n\n\n\n

In contrast to process mining, it is not<\/em> assumed that the business processes under  consideration are IT-supported and that the log data can be evaluated for this purpose.  The results of process mining include the reconstruction and analysis of processes in  order to determine implicit and hidden process information from data of IT systems  [10]. In contrast to business process simulation, no simulation-relevant process data is  required. The simulation usually supports the assessment of dynamic process behavior  and achieves quantitative time- and cost-related result data, which concern the  improvement of the interaction between human-task-technology [11]. Neither  simulation nor process mining consider soft and qualitative influencing factors such  as implicit process knowledge of the process participants. Without supplementary  procedures, they also do not allow automatic interpretation of the results obtained or  decisions on process improvements [9]. In contrast, iKnow should serve as the basis  for a knowledge-based and inferentially capable business process analysis tool and  enrich the automated process analysis with context-related information and expert  knowledge on the respective process.<\/p>\n\n\n\n

In this article, the previous concept is further developed and the evaluation of \u00a0iKnow for automated business process analysis in the context of consulting tasks is \u00a0presented. On the basis of defined analysis criteria, Business Process Model and \u00a0Notation (BPMN) models can be examined for weak points and then suitable \u00a0improvement measures can be determined automatically. With the help of iKnow’s automated learning approaches, context-related process information and expert  knowledge can be integrated so that the automated business process analysis can be  continuously improved over time.<\/p>\n\n\n\n

Methodically, the processing of the contribution can be assigned to design oriented \u00a0business informatics. The design science research approach according to Peffers et al. \u00a0[12] is chosen as the framework. After the description of the research motivation, the \u00a0basics of iKnow as well as core elements of the existing concept of the business \u00a0process analysis tool are presented. The formal model in iKnow is adapted and extended in chapter 3 according to the requirements of an automated business process \u00a0analysis. The methodological development of the artifact is based on a formal-deductive analysis [13]. Considering the limited space, the main focus of this paper \u00a0will be on the conceptual design and in particular on the demonstration of the \u00a0feasibility and usefulness of the artifact for automated business process analysis. Finally, a prototype evaluates the designed tool using an example of a business \u00a0process. The prototype itself and the evolutionary procedure for developing the \u00a0prototype application are not described. The last chapter sums up the findings gained \u00a0and points out subsequent research work.<\/p>\n\n\n\n

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