The Process Improvement Explorer (PIX)
Automated Discovery and Assessment of Business Process Improvement Opportunities
ERC Advanced Grant, 2019-2024
Main investigator: Marlon Dumas, University of Tartu
Collaborators: Fabrizio Maggi, Fredrik Milani, Wil van der Aalst (RWTH Aachen), Saso Dzeroski (Jozef Stefan Institute), Avigdor Gal (Technion), Marcello La Rosa (University of Melbourne), Jan Mendling (WU Vienna), Hajo A. Reijers (Utrecht University)
Business processes are the operational backbone of modern organizations. Their continuous improvement is key to the achievement of business objectives, be it with respect to efficiency, quality, compliance, or agility. Accordingly, a common task for process analysts is to discover and assess process improvement opportunities, i.e. changes to one or more processes, which are likely to improve them with respect to one or more performance measures.
Current approaches to discover business process improvement opportunities are expert-driven. In these approaches, models and execution data are used to assess opportunities derived from experience and intuition rather than to discover them in the first place. Moreover, as the assessment of opportunities is manual, analysts can only explore a fraction thereof.
PIX will build the foundations of a new generation of process improvement methods that do not exclusively rely on guidelines and heuristics, but rather on a systematic exploration of a space of possible changes derived from process execution data. Specifically, PIX will develop conceptual frameworks and algorithms to analyze process execution data in order to discover process changes corresponding to possible improvement opportunities, including changes in the control-flow dependencies between activities, partial automation of activities, changes in resource allocation rules, or changes in decision rules that may reduce wastes or negative outcomes. Each change will be associated with a multi-dimensional utility, thus allowing us to map a process improvement problem to an optimization problem over a multidimensional space. Given this mapping, PIX will develop efficient and incremental methods to search through said spaces in order to find Pareto-optimal groups of changes.
The outputs of the project will be embodied in a first-of-its-kind tool for automated process improvement discovery, which will lift the focus in the field of process mining from analyzing as-is processes to designing to-be processes.