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Business Process Management Systems: Evolution and Development Trends
Volume 31, Issue 3 (2020), pp. 579–595
Pub. online: 24 September 2020      Type: Research Article      Open accessOpen Access
Received
1 April 2020
Accepted
1 September 2020
Published
24 September 2020

Abstract

One of the results of the evolution of business process management (BPM) is the development of information technology (IT), methodologies and software tools to manage all types of processes – from traditional, structured processes to unstructured processes, for which it is not possible to define a detailed flow as a sequence of tasks to be performed before implementation. The purpose of the article is to present the evolution of intelligent BPM systems (iBPMS) and dynamic case management/adaptive case management systems (DCMS/ACMS) and show that they converge into one class of systems, additionally absorbing new emerging technologies such as process mining, robotic process automation (RPA), or machine learning/artificial intelligence (ML/AI). The content of research reports on iBPMS and DCMS systems by Gartner and Forrester consulting companies from the last 10 years was analysed. The nature of this study is descriptive and based solely on information from secondary data sources. It is an argumentative paper, and the study serves as the arguments that relate to the main research questions. The research results reveal that under business pressure, the evolution of both classes of systems (iBPMS and DCMS/ACMS) tends to cover the functionality of the same area of requirements by enabling the support of processes of different nature. This de facto means the creation of one class of systems, although for marketing reasons, some vendors will still offer separate products for some time to come. The article shows that the main driver of unified software system development is not the new possibilities offered by IT, but the requirements imposed on BPM by the increasingly stronger impact of knowledge management (KM) with regard to the way business processes are executed. Hence the anticipation of the further evolution of methodologies and BPM supporting systems towards integration with KM and elements of knowledge management systems (KMS). This article presents an original view on the features and development trends of software systems supporting BPM as a consequence of knowledge economy (KE) requirements in accordance with the concept of dynamic BPM.

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Biographies

Szelągowski Marek
marek.szelagowski@dbpm.pl

M. Szelągowski, PhD, Eng. – is an associate professor, researcher at the Systems Research Institute, Polish Academy of Sciences. His current research interests range across (dynamic) BPM, adaptive case management and knowledge management and IT solutions supporting them.

Lupeikiene Audrone
audrone.lupeikiene@mif.vu.lt

A. Lupeikiene, an associate professor, is a researcher at the Institute of Data Science and Digital Technologies of Vilnius University. Her main research interests include information systems, information systems engineering, knowledge and cyber-social systems.


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Keywords
BPM dynamic BPM DCM ACM iBPMS systems unification

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