# Pattern-based action engine: Generating process management actions using temporal patterns of process-centric problems

- Type: Journal article
- URL: https://www.gyunam.com/publication/2023-comind-actionengine/
- Date: 2023-01-01
- Authors: Gyunam Park, Daniel Schuster, Wil M. P. van der Aalst
- Venue: Comput. Ind.
- DOI: https://doi.org/10.1016/J.COMPIND.2023.104020
- PDF: https://www.gyunam.com/publication/2023-comind-actionengine/paper.pdf
- Research lines: [Prescriptive process analytics and digital twins](https://www.gyunam.com/research-line/prescriptive-process-analytics/index.md)

## Abstract

As business environments become more competitive, organizations strive to improve their business processes to reduce costs and increase quality and productivity. As process improvement traditionally embraces manual creative tasks that are time-consuming and labor-intensive, the need for automating it arises. Action-Oriented Process Mining (AOPM) aims to support automated process improvement by leveraging various process mining techniques. To that end, AOPM first monitors the presence of operational constraints, i.e., operational problems, in business processes, e.g., a high waiting time for patients to register. Next, it produces interim management actions designed to address these transient problems by analyzing the monitoring results. For instance, if an excessive waiting time persists for more than a week, the system might recommend dispatching additional resources for the upcoming week. Contrary to the mature process mining support for monitoring operational constraints, the action part is typically missing in today’s process mining tools. In this work, we propose an action engine to support the automatic generation of actions. It analyzes temporal patterns of monitoring results and produces action plans that describe the execution of management actions. We have demonstrated a use case using the data of a Dutch financial institute to evaluate the feasibility of the proposed action engine and conducted experiments to evaluate its effectiveness.

## Practical problem

Organizations monitor their processes for operational problems such as long waiting times or a high share of cancelled orders, and process mining tools are good at flagging these problems. Turning the flags into concrete management actions, however, is still done by hand, which is slow and expensive. The few existing action engines treat each problem as an isolated point event and fire actions on simple rules such as 'if the problem is present, trigger the action'. In practice problems stretch over time intervals and follow one another in patterns, and some remedial actions cannot run at the same time, so rule-based engines produce incomplete or conflicting action plans.

## Research questions

1. How can temporal patterns of interval-based operational problems be specified and detected so that management actions are generated automatically and without conflicts?
2. How do the number of actions and the number of conflicts between them affect the computation time and the scheduling performance of the action planner?
3. How do different conflict structures of the same size affect the computation time and scheduling performance of the action planner?

## Contributions

- A pattern-based action engine with three phases: domain experts design action graphs, an action generator derives action requirements from monitoring results, and an action planner schedules conflict-free action plans.
- Formalizes constraint instances as interval-based data, temporal pattern trees built on Allen's 13 temporal relations, and action graphs that connect patterns to actions with required durations.
- An action conflict model (a directed acyclic graph of precedence constraints) and a makespan-minimizing planner cast as parallel machine scheduling with precedence constraints and unlimited machines.
- A use case on the BPI Challenge 2017 loan application process in which frequent temporal patterns are discovered from weekly monitoring results with the process mining tool `Cortado`.
- An open-source implementation and experiment scripts on GitHub (aopm/pattern-based-action-engine).

## Key findings

- In the loan application use case, an action graph with six temporal patterns and seven actions applied weekly over 12 weeks (October to December 2016) yields a conflict-free plan of minimal makespan every week; plan 2 has makespan 4 days, total waiting time 2 and total flow time 14.
- Plans differ by week: plan 9 contains all actions with total waiting time 11, plan 10 contains only two actions with no waiting; waiting arises when an action is planned together with a conflicting predecessor (one action waits in 8 of 9 weeks).
- Planner runtime grows quadratically with the number of actions (100 to 1,000) and proportionally with the conflict ratio; with 1,000 actions and 90 percent conflicting pairs the runtime is about 250 seconds, and below 200 actions the conflict ratio hardly matters.
- Makespan grows linearly with the number of actions and rises with the conflict ratio, with the largest jump between 40 and 50 percent conflicts; for 500 actions, makespan variance across conflict structures of equal size peaks around 50 percent.
- Limitation: the use case was not executed inside the organization, so the effect of the actions is unmeasured; the planner ignores action instances already running and plans fixed weekly requirements, which can delay a response by up to a week.