Business Process Management Games: Learning from Business Process Optimization Research

[TimotheusKampik]

Introduction 

A key challenge in Business Process Management (BPM) is making business process improvement tangible and actionable. The most important aspects of this challenge are the following: 

1. Making BPM accessible, fostering a BPM culture. 

1. Making BPM actionable, facilitating process improvements and performance gains. 

While the former aspect is a prerequisite for the latter, the two are necessarily intertwined. Without an organizational BPM culture that ensures buy-in and BPM literacy among all relevant stakeholders, meaningful and effective change cannot be achieved, even with a theoretically mature toolbox at hand. Still, in our data-driven world, advanced intelligence approaches are required to optimize processes in complex organizations in a sustainable manner, under the responsible considerations of efforts and risks. This interconnection also manifests itself in classical symptoms of poor BPM, such as the traditional ivory tower center of process excellence, modeling “wallpapers” of process models that are detached from operational reality or the more contemporary problem of expensive process mining projects that lead to fancy dashboards but not to substantial change. 

To address this challenge, we draw upon the research results and experience of Professor Remco Dijkman, a seasoned business process optimization research leader at Eindhoven University of Technology. Specifically, in internal pilots of SAP Signavio BPM Academy, we use the BPM Game developed by Remco and his group to bring colleagues who are new to BPM (think of software engineers coming from a different domain, such as consumer software) up-to-speed regarding the objectives and challenges of modern BPM. Beyond that, we are glad to expand our horizon and learn more about business process optimization research that – in contrast to the often-emphasized work utilizing discrete approaches such as Petri nets – draws from a wide variety of quantitative methods in well-established research domains such as operations research to address the complex challenges of real-world BPM in an interconnected world. Below, we provide a glimpse into both aspects of the fruitful exchange. 

Prof. Remco Dijkman with SAP experts and senior managers in front of the Berlin SAP Office.

The BPM Game 

The BPM game [1] is a gamified business process simulator that centers around improving a business process, initially developed to provide BPM students with a more tangible, hands-on learning experience. In contrast to traditional business process simulators, the BPM game has the following key differentiating characteristics: 

• It is easier to use, in that it focuses on more than process models and event logs, instead covering the broader business context in a way that non-academics would intuitively expect. 
• It focuses on a realistic business process optimization scenario and tells a broader business story, beyond mere technicalities of process model and event log. 
• The simulator that underlies the game is prone to diverge from the “model-based” behaviors that have been explicitly specified. For example, if the process model specifies behavior that does not correspond to what the customer expects or according to which employees cannot do their work, the virtual employees in the simulator will try to “fix” the process by running extra tasks. With this, the game provides a more realistic simulation of the actual business environment (relative to traditional simulators), in which work is not always executed in the exact way that a process model or resource schedule specifies. 

So far, SAP Signavio has run the game with hundreds of colleagues, receiving tremendously positive feedback from users. We expect that the underlying ideas can be useful for customers (i.e., BPM practitioners) as well, in that the game can help upskill employees in terms of BPM knowledge in a realistic yet entertaining manner, thus fostering a BPM culture. 

Opening the Technical Toolbox for Next-Generation BPM

Looking further into the future, we are excited about several lines of research that Remco’s group is advancing, applying tools and methods from AI, computer science, and operation research in a novel and innovative way to business process management. 

Stochastic programming for process outcome prediction and optimization [2]. Utilizing probabilistic approaches (classical, as well as machine learning-based) when planning and optimizing organizational operations helps generate more realistic assessments, better informed decisions, and ultimately reduced operational costs.  

Counterfactual analysis for identifying process improvements [3]. Utilizing both expert knowledge and data, process patterns that affect process performance and whose enforcement (or avoidance) can thus yield to better business results can be identified, bridging the gap from insights about a process to actions that affect meaningful change. 

Policy learning for business decisions and resource assignments. Using reinforcement learning approaches, future business process simulators and execution systems may learn the best or most realistic resource assignment or decision policies. 

We are looking forward to diving deeper into these topics and to get inspired for next-generation product capabilities. 

The Road Ahead 

Drawing from the research and experience of academic experts such as Remco is crucial to stay ahead of the curve in the fast-moving field of business process management & intelligence software. We are looking forward to continuing the exchange with Remco and his group, not only to ensure that all SAP Signavio colleagues are up-to-speed on the basics of BPM, but also to facilitate innovation with respect to the capabilities we provide to our customers.

References

[1] Dijkman, R., & Peters, S. The Business Process Management Game. Business Process Management Demos. 2019.

[2] V. Gumuskaya, W. van Jaarsveld, R. Dijkman, P. Grefen, A. Veenstra. Integrating stochastic programs and decision trees in capacitated barge planning with uncertain container arrivals. Transportation Research. 2021. 

[3] Vazifehdoostirani, M., Genga, L., Lu, X., Verhoeven, R., van Laarhoven, H., & Dijkman, R. M. Interactive Multi-interest Process Pattern Discovery. Business Process Management. 2023.