This document discusses using machine learning to analyze process duration data from a process management system. It summarizes the following:
- They analyzed process duration data from new orders to production ready to predict individual process instance durations and optimize scheduling and production planning.
- Their machine learning model used gradient boosting and was trained on 75% of process instance data and validated on 25% to predict durations of all cases.
- They explored using explanation models to understand what factors influence predicted durations and ensure the model can be trusted.
- Integration scenarios were discussed to push predictions to services or batch predict, with the goal of a reusable and configurable pipeline.