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.

1. Profile Print and
Explorative Data Mining
Fritz Ulrich (Duni)
Frank Köhne (viadee)

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Our process and how it
should run
Return order New order

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How does it look for Users?

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Are we following the happy
path?

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Now we can have a look at
reports…

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…but what about
the future?

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Objectives
• Predicting the duration of an individual process instance
• Optimized scheduling
• Optimized production planning
• Explain the factors that influence process duration
• Explore what is possible

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DATA ANALYSIS
• Mean duration:
• New order  production ready
• Including both design, QC and
customer approval!
Duration bias!
- Early in the process no
long running process
ended
- Long running processes
may be running still
- Short ones are
overrepresented

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Training of the
model
Validation of
the model
Create
machine learning
model
75%
Prediction of all
cases
986 Instances
25%
3.389 process
instances
(partly running)

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Prediction method
• Gradient Boosting
• State of the Art - Procedure for regression analyses
• Compared to other possible models, fast, simple and high accuracy
• How does the prediction work?
1. A simple model is trained.
2. Prediction of the model is compared with the real data.
3. A new model is trained to learn the errors of the old model and to
correct them.
4. The two models are combined and step 2 is continued until the desired
accuracy or maximum complexity is reached.

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AI Explanation models
1. Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Why should i trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, pages 1135–1144. ACM, 2016.
2. Ribeiro, M. T., Singh, S., & Guestrin, C. (2018). Anchors: High-Precision Model-Agnostic Explanations. Aaai.
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ORDER COMMENT LENGTH

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MACHINE LEARNING PIPELINE
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HistoryEventListener
- Catch Events like a history
table would.
- Asynchronous and
lightweight.
Event Store / Stream
- Store Events persistently,
scaleable
- Unchanged single point
of truth
Preprocessing (1/2)
- Filter and flatten data
- Cleansing
- Aggregation to a data
mining table for learning
- Joins, anonymisation
Learning
- Data Mining
- Visualisation
- Explanation
- Provide executable
model (java)
Reusable / Configurable Code Specific Code

18. INTEGRATION SCENARIOS
HistoryEventListener
Preprocessing Machine Learning
B) Prediction
Service
Interface
(Pull)
A) Predict-on-
Event
(Push)
C) Batch-
Prediction
(Push)

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LESSONS LEARNED
• „Small Data“ – Meaningful analysis on limited data at process
instance level
• you may not need activity instance data at all
• Camunda is useful both as a datasource, AI integration style and as
a means to organize the lifecycle of prediction models.
• Need for a governance process (new variables, renamed variables,
new categorial values).
• Discussion needed?
• There may be considerable value in understanding the prediction
model – even if it is not (yet) integrated in an automated process.

20. Thank you for your attention