Business Process Management Systems: Evolution and Development Trends

Szelągowski, Marek; Lupeikiene, Audrone

doi:10.15388/20-INFOR429

Informatica

Business Process Management Systems: Evolution and Development Trends

Volume 31, Issue 3 (2020), pp. 579–595

Marek Szelągowski Audrone Lupeikiene

https://doi.org/10.15388/20-INFOR429

Pub. online: 24 September 2020 Type: Research Article

Open 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.

References

Antunes, P., Mourao, H. (2010). Resilient business process management: framework and services. Expert Systems with Applications, 38(2). https://doi.org/10.1016/j.eswa.2010.05.017.

Ariouat, H., Andonoff, E., Hanachi, C. (2016) In: Do Process-based Systems Support Emergent, Collaborative and Flexible Processes? Comparative Analysis of Current Systems, Procedia Computer Science: Vol. 96. pp. 511–520.

Armistead, C., Pritchard, J., Machin, S. (1999). Strategic business process management for organisational effectiveness. Long Range Planning, 1(32), 96–106.

Appian (2018). Dynamic Case Management. Retrieved from https://www.appian.com/wp-content/uploads/2018/07/ap_dynamic_case_mgmt_whitepaper_web.pdf [15.04.2020].

Bider, I., Prejons, E. (2014). Design science in action: developing a modeling technique for eliciting requirements on business process management (BPM) tools. Software and Systems Modeling, 14, 1159–1188. https://doi.org/10.1007/s10270-014-0412-6.

Caplinskas, A., Lupeikiene, A., Vasilecas, O. (2002). Shared conceptualisation of business systems, information systems and supporting software. In: Haav, H.-M., Kalja, A. (Eds.), Databases and Information Systems II. Kluwer Academic Publishers, pp. 109–121.

Caplinskas, A. (2009). Requirements elicitation in the context of enterprise engineering: a vision driven approach. Informatica, 20(3), 343–368.

Deming, W.E. (1986). Out of the Crisis. Cambridge. MIT, Center for Advanced Engineering Study.

DiCiccio, C., Marrella, A., Russo, A. (2015). Knowledge-intensive processes characteristics, requirements and analysis of contemporary approaches. Journal on Data Semantics, 4(1), 29–57.

Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A. (2016). Fundamentals of Business Process Management. Springer, Heidelberg.

Forrester (2009). Dynamic Case Management – An Old Idea Catches New Fire. Published: 28 December 2009.

Forrester (2010). The Forrester Wave™: Business Process Management Suites, Q3 2010. Published. Published: 26 August 2010.

Forrester (2011). The Forrester Wave: Dynamic Case Management, Q1 2011. Published: 31 January 2011.

Forrester (2013). The Forrester Wave: Business Process Management Suites, Q1 2013. Published: 11 March 2013.

Forrester (2014). The Forrester Wave: Dynamic Case Management, Q1 2014. Published: 28 March 2014.

Forrester (2016). The Forrester Wave: Dynamic Case Management, Q1 2016. Published: 2 Feb 2016.

Forrester (2018). The Forrester Wave: Cloud-Based Dynamic Case Management, Q1 2018. Published: 8 March 2018.

Forrester (2019). The Forrester Wave: Digital Process Automation For Wide Deployments, Q1 2019. Published: 12 March 2019; updated 25 March 2019.

Gartner (2010). Magic Quadrant for Business Process Management Suites. ID: G00205212. Published: 18 October 2010.

Gartner (2012). Magic Quadrant for Intelligent Business Process Management Suites. ID: G00224913. Published: 27 September 2012.

Gartner (2015a). Magic Quadrant for BPM-Platform-Based Case Management Frameworks. ID: G00262751. Published: 12 March 2015.

Gartner (2015b). Magic Quadrant for Intelligent Business Process Management Suites. ID: G00258612. Published: 18 March 2015.

Gartner (2016a). Magic Quadrant for Intelligent Business Process Management Suites. ID: G00276892. Published: 18 August 2016.

Gartner (2016b). Magic Quadrant for BPM-Platform-Based Case Management Frameworks. ID: G00276724. Published: 24 Oct 2016.

Gartner (2017). Magic Quadrant for Intelligent Business Process Management Suites. ID: G00315642. Published: 24 Oct 2017.

Gartner (2019a). Magic Quadrant for Intelligent Business Process Management Suites. ID: G00345694. Published: 30 January 2019.

Gartner (2019b). Top 10 Strategic Technology Trends for 2020 ID: G00432920. Published: 21 October 2019.

Gartner (2019c). Strategic Roadmap for Postmodern ERP. Published. ID: G00384628. Published: 31 May 2019.

Gartner Glossary (2020). Business Process Management Suites (BPMSs). Retrieved from https://www.gartner.com/en/information-technology/glossary/bpms-business-process-management-suite [24.02.2020].

Guay, M. (2016). Postmodern ERP Strategies and Considerations for Midmarket IT Leaders. Retrieved from http://proyectos.andi.com.co/camarabpo/Webinar%202016/Postmodern%20ERP%20strategies%20and%20considerations%20for%20midmarket%20IT%20leaders-%20Gartner.pdf [23.08.2020].

Hammer, M. (2015). What is business process management? In: vom Brocke, J., Rosemann, M. (Eds.), Handbook on Business Process Management (2nd ed.). Springer-Verlag, Berlin, Heidelberg, pp. 3–16.

IBM (2020). What is IBM Business Automation Workflow? Retrieved from https://www.ibm.com/products/business-automation-workflow [15.04.2020].

Isik, O., van der Bergh, J., Martens, W. (2012). Knowledge intensive business processes: an exploratory study. In: 45th Hawaii International Conference on System Sciences, pp. 3817–3826. https://doi.org/10.1109/HICSS.2012.401.

Jochem, R., Geers, D., Heinze, P. (2011). Maturity measurement of knowledge-intensive business processes. The TQM Journal, 23(4), 377–387.

Kemsley, S. (2011) The changing nature of work: from structured to unstructured, from controlled to social. In: Lecture Notes in Computer Science Business Process Management, pp. 2–2.

Kirchmayr, M., Müller, M., Penzenstadler, B., Sikora, E., Weyer, T. (2006). Essenzieller REMsES-Leitfaden. Requirements Engineering und Management Softwareintensiver Eingebetteter Systeme, REMsES-Konsortium, 36 pp.

Klun, M., Trkman, P. (2018). Business process management – at the crossroads. Business Process Management Journal, 24(3), 786–813.

Le Clair, C., Miers, D., Moore, C., Fowler-Cornfeld, E. (2011). Dynamic Case Management: Definitely Not Your Dad’s Old-School Workflow/Imaging System. Forester Report, 28 September 2011.

Marjanovic, O., Freeze, R. (2012). Knowledge-intensive business process: deriving a sustainable competitive advantage through business process management and knowledge management integration. Knowledge and Process Management, 19(4), 180–188.

Mendling, J., Pentland, B., Recker, J. (2020). Building a complementary agenda for business process management and digital innovation. European Journal of Information Systems, 29. https://doi.org/10.1080/0960085X.2020.1755207.

Moore, C. (2017). Seven trends in adaptive case management (BPM) software. Retrieved from https://www.digitalclaritygroup.com/2017-seven-trends-adaptive-case-management-software/ [19.03.2020].

Olding, E., Rozwell, C. (2015). Expand Your BPM Horizons by Exploring Unstructured Processes. Gartner Technical Report G00172387.

Palmer, N. (2013). Thriving on adaptability: how smart companies win in data-driven world. In: Fisher, L. (Ed.), Intelligent BPM Systems: Impact and Opportunity. Future Strategies Inc., Ligthouse Point (USA), pp. 17–32.

Pegasystems (2020). Pega Platform. Retrieved from https://www.pega.com/products/pega-platform [31.03.2020].

Pucher, M. (2010a). The Difference between DYNAMIC and ADAPTIVE. http://acmisis.wordpress.com/2010/11/18/the-difference-between-dynamic-and-adaptive/ [19.02.2020].

Pucher, M. (2010b). The Difference between ACM and BPM. Retrieved from https://acmisis.wordpress.com/2010/10/01/the-difference-between-acm-and-bpm/ [21.02.2020].

Pucher, M. (2012). How to link BPM governance and social collaboration through an adaptive paradigm. In: Fisher, W.L. (Ed.), Social BPM: Work, Planning and Collaboration Under the Impact of Social Technology. Future Strategies Inc., Ligthouse Point, pp. 57–76.

Pucher, M., Ruhsam, C., Kim, T. (2014). Towards a pattern recognition approach for transferring knowledge in ACM. In: 2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations.

Richter-von Hagen, C., Ratz, D., Povalej, R. (2005). Towards self-organizing knowledge intensive processes. Journal of Universal Knowledge Management, 2, 148–169. 2005.

Rodrigues, D., Baiao, F., Santoro, F., Netto, J. (2015). Towards a context-based representation of the dynamicity perspective in knowledge-intensive processes. In: 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD). https://doi.org/10.1109/CSCWD.2015.7230952.

Smith, H., Fingar, P. (2002). Business Process Management – The Third Wave. Meghan-Kiffer Press.

Szelągowski, M. (2014). Becoming a learning organization through dynamic business process management. Journal of Entrepreneurship, Management and Innovation (JEMI), 10(1), 147–166.

Szelągowski, M. (2019). Dynamic BPM in the Knowledge Economy: Creating Value from Intellectual Capital. Lecture Notes in Networks and Systems (LNNS), Vol. 71. Springer International Publishing, Berlin,

Szelągowski, M. (2020). Knowledge and process dimensions. VINE Journal of Information and Knowledge Management Systems, ahead-of-print No. ahead-of-print. https://doi.org/10.1108/VJIKMS-09-2019-0150.

Szelągowski, M., Berniak-Woźny, J. (2020). Business processes nature assessment matrix – a novel approach to the assessment of business process dynamism and knowledge intensity. Advances in Production Engineering & Management (APEM).

Taylor, F.W. (1911). The Principles of Scientific Management. Harper & Brothers, New York.

Trkman, P. (2009). The critical success factors of business process management. International Journal of Information Management, 30(2), 125–134.

Ukelson, J. (2010). Adaptive Case Management over Business Process Management. Retrieved from http://it.toolbox.com/blogs/lessons-process-management/adaptive-case-management-over-business-process-management-40002 [7.04.2016].

van der Aalst, W., Weske, M., Grunbauer, D. (2005). Case handling: a new paradigm for business process support. Data & Knowledge Engineering, 53, 129–162.

vom Brocke, J., Zelt, T., Schmiedel, T. (2015). On the role of context in business process management. International Journal of Information Management, 36(3), 486–495.

Zelt, S., Recker, J., Schmiedel, T., Brocke J, v. (2018). A theory of contingent business process management. Business Process Management Journal. https://doi.org/10.1108/BPMJ-05-2018-0129. DOI.

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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