Business Process Management used to be a completely theoretical discipline based on tedious and fractional data collection, lots of assumptions and little validation. However the integration of new software, IT systems, and business process technology now makes it possible to manage business processes in a completely different, fact-based manner.
Manfred Reichert has devoted his work to researching different aspects of Business Process Management, in particular Next Generation Process Management technology. He studies enterprise demands, trends and challenges for technologies in the field such as, e.g., adaptive and flexible processes, process lifecycle management, data-driven and data-centric processes, and mobile process support. For his work, he has received numerous awards including the BPM Test of Time Award, Merckle Forschungspreis, IFIP TC2 Manfred Paul Award, and doIT Software Award. He is also a member of the Steering Committee of the BPM conference series and a longstanding academic partner with the Celonis Academic Alliance. We have asked him to talk to us about current trends and topics for Next Generation Process Management technology.
In today’s dynamic business world, the success of an enterprise depends on its ability to react to environmental changes in a quick and flexible way including regulatory adaptations (e.g., Sarbanes-Oxley), changes in customer behavior, or changes due to business process reengineering efforts. Business agility, therefore, constitutes a competitive advantage to address business needs like faster time-to-market, increasing product variability and tightly aligned business and IT. As a consequence, improving the efficiency and quality of their business processes and optimizing their interactions with partners and customers have become crucial success factors for organizations.
BPM suites and platforms usually deal with the enactment of large numbers of business cases, whereas process mining focuses on the analysis of the event logs created by such process-aware information systems. Process Mining technology is groundbreaking as it has dramatically changed the way we look at the business processes implemented in enterprise information systems. Instead of conducting complex code analyses or costly workshops with process stakeholders to uncover the implemented processes, the models of these processes can be automatically discovered from the event logs of the information systems.
When I first got in touch with Process Mining I was fascinated by the various algorithms developed for discovering process models from event logs or for checking the conformance of a given process model with the actual process behavior as observed in an event log.
During the last decade BPM has been established as a broad discipline, covering topics that range from formal methods in computer science to techniques in information systems engineering to management science methods. To accommodate for this diversity, the BPM conference series, which is the premium scientific event in the BPM field, established three tracks a couple of years ago: Foundations, Engineering and Management.
Major trends that Business Process Technology was facing during the last decade, include the migration of BPM platforms to the cloud, the rapid growth of tools, algorithms, and use cases in the Process Mining field, the development of highly flexible BPM suites (e.g., case handling tools, adaptive process engines), and the introduction of BPMN 2.0 as the standardized language and notation for business process modeling – a breakthrough for both academia and industry. As a researcher, I am particularly happy to see that many of the methods, concepts, and technologies developed in the BPM field and presented at the aforementioned BPM conference series have matured to the point allowing for their integration into professional Business Process Technologies.
Good question! We will see groundbreaking BPM trends in the next decade to come. Due to the increasing maturity of the Internet of Things, for example, the gap between the digital processes run in an information system and the ones actually happening in the physical world can be filled. Resources and assets involved in the execution of business processes will be equipped with sensors, and interactions between end-users and processes can take place from everywhere via mobile devices (e.g. smartphones or data glasses). In turn, the knowledge of the state of physical process entities will enable the discovery of deviations between digital and physical processes as well as their realignment in case of observed discrepancies (e.g., by dynamically adapting the digital processes). This also further levels the ground for Process Mining, which uses data-driven insights.
There are other disruptive technologies that might change the BPM field. For example, blockchain technology and smart contracts offer promising opportunities for the secure and robust automation of cross-organizational processes without a central party as a single point of trust (and failure) and better integration of collaboration insights. The better collaboration along a business process is supported, the more value will be created in terms of cost reduction, increased productivity, or customer satisfaction.
Finally, there will be a spike in demand for low-code BPM suites. Many business units do not employ software developers such that it is very hard for them to use a BPM suite that is functional, but needs considerable coding efforts. With data-centric and -driven approaches to BPM , low-coding is no longer a vision, but close to reality.
Although Business Process technology has become increasingly mature during the last decade, there still exist organizational barriers hindering its uptake. In particular, many enterprises are unable to tear down their departmental silos, causing huge costs in business process engineering projects and counteracting operational excellence and customer experience initiatives. Thus, the greatest challenge will be to overcome these silos – only then business process technologies can perform to their fullest potential. Note that this also necessitates a modernization of the legacy systems still running in many enterprises. Process mining could enable the discovery of the processes implemented by these systems and, thus, foster their migration to modern software architectures.
Process Mining is the key technology for enabling comprehensive process analytics as well as for creating process awareness in enterprises. Only when creating such awareness on the side of the decision makers and process participants, business process automation initiatives will have a reasonable foundation.
The importance of Process Mining within the technology landscape will increase. Process mining functionalities will be incorporated into BPM suites as well as into process-aware information systems (e.g. ERP systems) in order to enable online process analytics (e.g., online conformance checking). When further augmenting event logs with data of the physical entities (e.g., resources) involved in a business process, sophisticated real-time analytics of these processes and their resources will become possible, enabling operational excellence and sophisticated customer experience.
AI and ML have already been applied in Process Mining, e.g., to predict process risks or process throughput times based on historical event data. While such predictive monitoring approach enables forecasting what will happen in the future, prescriptive analytics utilizes ML to provide intelligent recommendations for the optimal next steps in a business process to drive desired outcomes or to accelerate process execution. Finally, there are ambitious endeavors utilizing AI and ML in order to enable the automated, data-driven optimization and evolution of business processes.
In general, AI and ML will make BPM suites smarter by continuously evaluating process-related data enabling better predictions and providing more meaningful forecasting indicators. The latter might assist decision makers in achieving better corporate performance.
In spite of several success stories on the uptake of workflow systems, the latter have not been widely adopted due to the rigidity enforced by them. To increase process flexibility and to cope with the dynamic nature of business processes we have been working on a wide range of technology solutions, which resulted in AristaFlow, a next generation process management technology that enables full process lifecycle support. AristaFlow can handle exceptions, situationally change the execution of running business cases on-the-ﬂy, efﬁciently deal with uncertainty, and support the controlled evolution of business processes over time. With PHILharmonicFlows, we have developed a data-centric and data-driven business process technology, where the availability of data drives process execution, instead of the completion of activities as in the case of activity-centric processes. This leads to an increased ﬂexibility as the order of activity execution is largely up to the user, as long as the deﬁned constraints are adhered to. Both technologies annihilate the picture of process-aware systems (e.g. workflow systems) as being rigid, unyielding types of information systems.
Contemporary technologies and trends like the Internet of Things, Blockchain, Mobile Devices, Data-driven Processes, AI and ML, and many more will change the way business processes can be supported at the operational level in future. Let us start doing smart things with these still imperfect and (partially) stupid technologies to enhance Process Mining, rather than do stupid things with smart technologies tomorrow.
Thank you very much for the interview!
If you want to learn more about Process Flexibility and Process-aware Information Systems check out Manfred Reichert et al.’s book on Enabling Process Flexibility in Process-Aware Information Systems.
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