ProcessGAN: Supporting the creation of business process improvement ideas through generative machine learning

Abstract

Business processes are a key driver of organizational success, which is why business process improvement (BPI) is a central activity of business process management. Despite an abundance of approaches, BPI as a creative task is time-consuming and labour-intensive. Most importantly, its level of computational support is low. The few computational BPI approaches hardly leverage the opportunities brought about by computational creativity, neglect process data, and rely on rather rigid improvement patterns. Given the increasing amount of process data in the form of event logs and the uptake of generative machine learning for automating creative tasks in various domains, there is huge potential for BPI. Hence, following the design science research paradigm, we specified, implemented, and evaluated ProcessGAN, a novel computational BPI approach based on generative adversarial networks that supports the creation of BPI ideas. Our evaluation shows that ProcessGAN improves the creativity of process designers, particularly the originality of BPI ideas, and shapes up useful in real-world settings. Moreover, ProcessGAN is the first approach to combine BPI and computational creativity.