Periodic Reporting for period 2 - PIX (The Process Improvement Explorer: Automated Discovery and Assessment of Business Process Improvement Opportunities)
Reporting period: 2021-03-01 to 2022-08-31
Every mid-to-large organization needs to continuously improve its business processes to remain efficient, to deliver excellent customer service, to comply with regulations, and to adapt to changes in customer expectations. Business Process Management (BPM) is the art and science of how to organize, coordinate, and continuously improve business processes by making optimal use of human and technology assets in an organization.
One of the most recurring questions in the field of BPM is: How to discover and evaluate opportunities for improving one or more business processes in an organization?
Current approaches to discover business process improvement opportunities are expert-driven. In these approaches, managers identify improvement opportunities based on their intuition and experience. When an improvement opportunity is identified, a team of business analysts are tasked with validating and quantifying the improvement opportunity using data extracted from enterprise IT systems. In other words, the ideas for improvement are generated using expert intuition, and then these ideas are evaluated manually using historical execution data.
This approach is sub-optimal: many ideas for improvement are left unexplored, because it would be too time-consuming and costly to consider them all.
The PIX project is building the foundations of a new generation of business process improvement methods, which will not rely primarily on human intuition to discover improvement opportunities, and on manual data analysis to evaluate these opportunities. Instead, in the future-generation methods envisaged in the project, the improvement opportunities are derived from a systematic exploration of a space of possible changes, and each opportunity is evaluated automatically based on historical execution data.
In other words, the objective of the PIX project is to develop algorithms to analyze historical execution data to uncover changes to a process that are likely to improve its performance. These include changes in the control-flow dependencies between activities, partial automation of activities (e.g. using software bots), changes in resource allocation rules, or changes in decision rules.
One of the most recurring questions in the field of BPM is: How to discover and evaluate opportunities for improving one or more business processes in an organization?
Current approaches to discover business process improvement opportunities are expert-driven. In these approaches, managers identify improvement opportunities based on their intuition and experience. When an improvement opportunity is identified, a team of business analysts are tasked with validating and quantifying the improvement opportunity using data extracted from enterprise IT systems. In other words, the ideas for improvement are generated using expert intuition, and then these ideas are evaluated manually using historical execution data.
This approach is sub-optimal: many ideas for improvement are left unexplored, because it would be too time-consuming and costly to consider them all.
The PIX project is building the foundations of a new generation of business process improvement methods, which will not rely primarily on human intuition to discover improvement opportunities, and on manual data analysis to evaluate these opportunities. Instead, in the future-generation methods envisaged in the project, the improvement opportunities are derived from a systematic exploration of a space of possible changes, and each opportunity is evaluated automatically based on historical execution data.
In other words, the objective of the PIX project is to develop algorithms to analyze historical execution data to uncover changes to a process that are likely to improve its performance. These include changes in the control-flow dependencies between activities, partial automation of activities (e.g. using software bots), changes in resource allocation rules, or changes in decision rules.
We have made substantial progress in the development of three key pillars to achieve the vision of the PIX project.
The first pillar addresses 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 are developing a tool, 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, such as the use of machine learning techniques to optimize the fidelity of a simulation model, as well as algorithms to automatically discover the working timetables of workers in a business process, as well as work habits and patterns such as multi-tasking, batching, and prioritization. Discovering and accounting for specific work habits in a business process simulation model is essential in order to ensure that the simulation model closely reflects the reality.
The second pillar is an optimization engine 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 optimal set of improvement opportunities.
A third pillar 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 have 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 first pillar addresses 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 are developing a tool, 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, such as the use of machine learning techniques to optimize the fidelity of a simulation model, as well as algorithms to automatically discover the working timetables of workers in a business process, as well as work habits and patterns such as multi-tasking, batching, and prioritization. Discovering and accounting for specific work habits in a business process simulation model is essential in order to ensure that the simulation model closely reflects the reality.
The second pillar is an optimization engine 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 optimal set of improvement opportunities.
A third pillar 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 have 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.
We still have breakthroughs to achieve to make Simod applicable in a wider range of real scenarios. For example, we have found that in many real-life processes, people do not work in the process at fixed times of the days. Instead, they work at different times of the day with different probabilities (e.g. someone is likely to work on their tasks at 5pm with a small probability, and at 6pm with a smaller probability). Also, the performance of a worker differs at different times of the day or of the week, and workers suffer from fatigue effects and work differently under high workload and stress. Besides, some forms of waiting times (e.g. waiting for a phone call from a customer) cannot be directly discovered from historical execution data, since events like “receiving a phone call” are often not recorded. We are designing techniques to detect and account for such events that are “invisible” in the data.
The process optimization engine we have developed is still in its infancy. It only takes into account changes in the allocation of workers to tasks. We will continue extending it to take into account a wider range of changes, such as changes in the order of tasks in a process, or changes in the decision rules used in a process.
The techniques we have proposed have been validated mostly in vitro, via computational experiments. One of the main activities in the second half of the project is to confront the proposed techniques to end users, in real or realistic environments, and to collect feedback and refine the techniques for practical use.
The process optimization engine we have developed is still in its infancy. It only takes into account changes in the allocation of workers to tasks. We will continue extending it to take into account a wider range of changes, such as changes in the order of tasks in a process, or changes in the decision rules used in a process.
The techniques we have proposed have been validated mostly in vitro, via computational experiments. One of the main activities in the second half of the project is to confront the proposed techniques to end users, in real or realistic environments, and to collect feedback and refine the techniques for practical use.