Explainable Predictive Decision Mining for Operational Support

TL;DR

This paper enhances decision mining in process management by integrating advanced machine learning for prediction and SHAP values for explainability, enabling proactive operational support.

Contribution

It introduces a novel approach combining machine learning prediction with explainability via SHAP values for decision mining.

Findings

Improved decision prediction accuracy with advanced ML algorithms.

Effective explanations of decisions using SHAP values.

Web application implementation demonstrating practical utility.

Abstract

Several decision points exist in business processes (e.g., whether a purchase order needs a manager's approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than $500 needs a manager approval). Decision mining in process mining aims to describe/predict the routing of a process instance at a decision point of the process. By predicting the decision, one can take proactive actions to improve the process. For instance, when a bottleneck is developing in one of the possible decisions, one can predict the decision and bypass the bottleneck. However, despite its huge potential for such operational support, existing techniques for decision mining have focused largely on describing decisions but not on predicting them, deploying decision trees to produce logical expressions to explain the decision. In this work, we aim to enhance the predictive capability of decision mining to enable proactive operational support by deploying more advanced machine learning algorithms. Our proposed approach provides explanations of the predicted decisions using SHAP values to support the elicitation of proactive actions. We have implemented a Web application to support the proposed approach and evaluated the approach using the implementation.