Challenges of smart business process management: An introduction to the special issue
Introduction
Today's business world is complex and characterized by an extensive division of labor. Products and services are designed and delivered with various actors being involved within the providing organization and beyond. In order to deliver products and services in a smooth way, it is of utmost importance that the coordination between the different actors inside and outside the providing organization is well defined. A first step towards a smooth operation is achieving transparency of the business process that results in product and service delivery. This transparency can be achieved by documenting the business process including the various actors involved, the activities they perform, the events and decisions that influence the progress, and the information that is produced and consumed [1], [2].
Division of labor in business processes calls for coordination support by the help of information systems. The specific class of information systems that explicitly supports business processes is often referred to as process-aware information systems [3]. Office automation systems [4], [5], workflow management systems [6], [7], and recent business process management systems [1], [2] all support process execution based on a specification of the process as a formal business process model.
Business process management is concerned with all management activities around business processes. In the past, activities in relation to business process management have been conducted by process analysts, process managers and process engineers in a labor-intense fashion with hardly any automatic support except for generating the system configuration from the executable process model. This has been changing in recent years. Various smart techniques have been developed to automate or provide intelligent support for process stakeholders in various stages of business process management. This special issue provides ten excellent examples of these recent developments towards smart business process management. This editorial presents them in an overarching framework and connects them with the broader spectrum of recent contributions on smart business process management.
Section snippets
Business process management
In this section, we distinguish three different levels of business process management. Fig. 1 shows these three levels and their connections. The top level is often referred to as multiprocess management. It is concerned with the identification of the major processes of an organization and the regular evaluation of the priorities assigned to these processes. These activities interrelate with questions of strategic management and the overall process organization. The products of multiprocess
Smart business process management
The Oxford dictionary provides the following three connotations for smart: (i) being clean and tidy, (ii) showing quick-witted intelligence and (iii) being quick.1 All these meanings together have become prominent attributes of information technology and analysis techniques in various application domains referred to as smart home, smart health, smart city, smart energy or smart mobility. What is common to these smart technologies is that they
Future research on smart business process management
The research reported in this special issue provides a solid foundation for future research into smart business process management. This research will have to address challenges within the three levels of business process management and across them.
There is potential for future research within levels. On the level of the process repository, it is striking to note that research on the integration of repositories with external knowledge resources has come to a pause. Around the year 2000, the MIT
Acknowledgements
We thank the authors of the papers collected in this special issue and the editor-in-chief James Marsden for his comments on this editorial. The work of Jan Mendling has received funding from the EU H2020 programme under the MSCA-RISE agreement 645751 (RISE BPM).
Jan Mendling is a Full Professor with the Institute for Information Business at Wirtschaftsuniversitt Wien (WU Vienna), Austria. His research interests include various topics in the area of business process management and information systems. He has published more than 300 research papers and articles, among others in ACM Transactions on Software Engineering and Methodology, IEEE Transaction on Software Engineering, Information Systems, Data & Knowledge Engineering, and Decision Support
References (50)
- et al.
Similarity of business process models: metrics and evaluation
Inf. Syst.
(2011) - et al.
Refactoring large process model repositories
Comput. Ind.
(2011) - et al.
Automatic service derivation from business process model repositories via semantic technology
J. Syst. Softw.
(2015) - et al.
Overcoming individual process model matcher weaknesses using ensemble matching
Decis. Support. Syst.
(2017) - et al.
Analyzing control flow information to improve the effectiveness of process model matching techniques
Decis. Support. Syst.
(2017) - et al.
Data-driven process prioritization in process networks
Decis. Support. Syst.
(2017) - et al.
Best practices in business process redesign: an overview and qualitative evaluation of successful redesign heuristics
Omega
(2005) - et al.
The Structured Process Modeling Method (SPMM) What is the best way for me to construct a process model?
Decis. Support. Syst.
(2017) - et al.
Discovering work prioritisation patterns from event logs
Decis. Support. Syst.
(2017) - et al.
ProcessProfiler3D: a visualisation framework for log-based process performance comparison
Decis. Support Syst.
(2017)
Fodina: a robust and flexible heuristic process discovery technique
Decis. Support. Syst.
Retrieving batch organisation of work insights from event logs
Decis. Support. Syst.
Predicting process behaviour using deep learning
Decis. Support. Syst.
Detecting flight trajectory anomalies and predicting diversions in freight transportation
Decis. Support. Syst.
Government innovation through social media
Gov. Inf. Q.
Fundamentals of Business Process Management
Business Process Management
Process-Aware Information Systems: Bridging People and Software Through Process Technology
Office Automation: Revolution or Evolution?
Sloan Manag. Rev.
Office Automation: A Social and Organizational Perspective
An overview of workflow management: from process modeling to workflow automation infrastructure
Distrib. Parallel Databases
Production Workflow - Concepts and Techniques
Process Mining - Data Science in Action
What's in a smart thing? Development of a multi-layer taxonomy
Four strategies for the age of smart services
Harvard Business Review
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Jan Mendling is a Full Professor with the Institute for Information Business at Wirtschaftsuniversitt Wien (WU Vienna), Austria. His research interests include various topics in the area of business process management and information systems. He has published more than 300 research papers and articles, among others in ACM Transactions on Software Engineering and Methodology, IEEE Transaction on Software Engineering, Information Systems, Data & Knowledge Engineering, and Decision Support Systems. He is a member of the editorial board of seven international journals, member of the board of the Austrian Society for Process Management (http://prozesse.at), one of the founders of the Berlin BPM Community of Practice (http://www.bpmb.de), organizer of several academic events on process management, and member of the IEEE Task Force on Process Mining. His Ph.D. thesis has won the Heinz-Zemanek-Award of the Austrian Computer Society and the German Targion-Award for dissertations in the area of strategic information management.
Bart Baesens is a professor at KU Leuven, Belgium, and a lecturer at the University of Southampton, United Kingdom. He has done extensive research on big data & analytics, customer relationship management, web analytics, fraud detection, and credit risk management. His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, ...) and presented at international top conferences. He is author of the books Credit Risk Management: Basic Concepts, Analytics in a Big Data World and Fraud Analytics using Descriptive, Predictive and Social Network Techniques. His research is summarized at http://www.dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their big data, analytics and credit risk management strategy.
Abraham Bernstein is a Full Professor and Chair of the Department of Informatics as well as director of the Digital Society Initiative at the University of Zurich (UZH), Switzerland. His current research focuses on various aspects of the semantic web, knowledge discovery, crowdsourcing, and mobile/pervasive computing. His work is based on both social science (organizational psychology/sociology/economics) and technical (computer science, artificial intelligence) foundations. Mr. Bernstein is a Ph.D. from MIT and also holds a Diploma in Computer Science (comparable to a M.S.) from the Swiss Federal Institute in Zurich (ETH).
Michael Fellmann is a Junior Professor for Information Systems at the Faculty of Computer Science and Electrical Engineering of the Rostock University. His research areas include Business Process Management, Semantic Technologies and Service Science. He obtained a doctoral degree from Osnabrück University in 2012. Michael Fellmann is speaker of the working group “Semantic Technologies in Business Process Management” being part of the EMISA subgroup of the German Informatics Society (GI).