The PIX project led to three major advances to the state of the art in the field of data-driven business process optimization.
The first advancement relates to the question of “How to automatically evaluate a business process improvement idea?” In other words, how to find out if and by how much a change to a business process will improve its performance? To address this question, we developed a method, called Simod, that automatically discovers a business process simulation model from a dataset containing historical traces of a business process. Simod incorporates several novel ideas, chiefly: (1) the use of machine learning techniques to optimize the fidelity of a simulation model; (2) algorithms to automatically discover “availability timetables” and “work habits” of workers in a business process entirely from historical data, including work habits such as multi-tasking, batching, and prioritization.
The second major advancement of the project is an optimization method, namely Optimos, that takes as input a historical execution dataset and generates all sorts of “candidate changes” that are likely to improve a process with respect to cost and time. This optimization engine relies on Simod to determine if and to what extent a given change improves (or degrades) the performance of the process. The optimization engine then builds a set of Pareto-optimal set of improvement opportunities.
A third major contribution of the project is a tool called Robidium, which analyzes interactions between office workers and software applications to detect repetitive data transfer routines that can be partially or fully automated. Robidium surgically separates tasks that can be automated and tasks that cannot be automated because they involve a human decision or human knowledge not present in the input data. Robidium then generates a script that allows a software bot to automatically replicate the repetitive tasks performed by humans. Each of the repetitive tasks identified by Robidium represents a potential improvement opportunity.
We have also made inroads in the direction of optimization of business decisions. Along this front, we developed a semi-automated method to discover condition-action rules to reduce the defect rate in a business process with respect to a given notion of “defect” (e.g. customer complaints, deadline violations, etc.). For example, we can automatically discover rules of the form “when a customer is from Southeast-Asia, then assigning activity A to worker X or executing activity A before activity B (rather than the other way around) increases the probability that this customer will be satisfied by 10%.” Such cause-effect relations help process managers to identify business process improvement opportunities.
The SIMOD method was transferred via a separate R&D project into a commercial tool for process optimization, namely Apromore. The methods are now part of the Apromore commercial platform. The methods are used to optimize business processes in several dozen large organizations across Europe, Australia, and USA.
The project has also made available an open-source implementation of the SIMOD, Optimos, and Robidium toolsets. The open-source implementation of SIMOD has attracted interest from other research groups and commercial vendors as evidenced by a dozen forks of the corresponding open-source code repositories.
