Automated Process Improvement: Status, Challenges, and Perspectives

Abstract:

Business processes are the operational backbone of modern organizations. Their continuous management and improvement is key to the achievement of business objectives. Accordingly, a common task for managers and analysts is to discover, assess, and exploit process improvement opportunities. Current approaches to discover process improvement opportunities are expert-driven. In these approaches, 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 of the overall space of improvement opportunities.

Recent advances in machine learning and artificial intelligence are making it possible to move from manual to automated (or semi-automated) approaches to business process improvement. This talk will present a vision for the emerging field of AI-driven automated process improvement. The talk will focus on three families of methods: (1) predictive process monitoring; (2) robotic process mining; and (3) search-based process optimization.

Predictive process monitoring methods allow us to analyze ongoing executions of a process in order to predict future states and undesirable outcomes at runtime. These predictions can be used to trigger interventions in order to maximize a given reward function, for example by generating alerts or making recommendations to process workers. The talk will provide a taxonomy of the state of the art in this field, as well as open questions and possible research directions.

Robotic process mining seeks to analyze logs generated by user interactions in order to discover repetitive routines (e.g. clerical routines) that are fully deterministic and can therefore be automated via Robotic Process Automation (RPA) scripts. These scripts are executed by software bots, with minimal user supervision, thus relieving workers from tedious and error-prone work. The talk will present initial results in the field of robotic process mining and discuss challenges and opportunities.

Finally, the talk will introduce a gestating family of methods for search-based process optimization. These techniques rely on multi-objective optimization algorithms in conjunction with data-driven process simulation, in order to discover sets of changes to one or more business processes, which maximize one or more performance measures. The talk will present a framework for search-based process optimization and will sketch approaches that could be explored to realize the vision of a recommender system for process improvement.