ML Workbench Examples / GitHub Repositories

Question

I’m currently exploring the Celonis Machine Learning Workbench and looking for some practical examples to learn from.

I found the official PyCelonis Examples repository on GitHub, which is very helpful for understanding PyCelonis and interacting with Celonis resources. However, I was wondering whether there are also repositories, sample notebooks, or templates focused more on end-to-end ML Workbench use cases.

I’m particularly interested in Supply Chain examples such as:

Late/on-time delivery prediction
Supplier or procurement risk prediction
Inventory-related predictions
Lead time prediction
Bottleneck or process outcome prediction

I also came across the older O2C – On-Time Delivery Prediction example, but it seems to rely on the legacy celonis_ml package.

Does anyone know of any up-to-date PyCelonis 2.x / ML Workbench GitHub repositories, Jupyter notebooks, or sample projects that could be used as a starting point?

And if you have built something similar yourself and can share the general approach or architecture, I’d love to learn from it. 😊

Thanks in advance!

abhishek.chatu14 · Answer

This is the approach that I would follow for the same

Define prediction point — only use information available at that point in time.
Feature engineering — supplier performance, lead time, PO changes, quantity, material, plant, etc.
Export data to ML Workbench using PyCelonis
- PyCelonis Documentation
Train and validate ML model — start with Logistic Regression / Random Forest / XGBoost.
Generate predictions for current/open POs.
Bring predictions back into Celonis as risk scores/predicted outcomes.
Use predictions operationally — dashboards, Action Flows, alerts/interventions.
Measure impact — compare predicted-risk cases, interventions, and actual outcomes.
Supply-chain ML examples to use as modelling references:
Supply Chain Analytics — Late Delivery / Shipment Duration Prediction
Supply Chain Late Delivery Prediction
Late Delivery Prediction — ML/API Example

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