Migrating the Supply Chain Analytics Platform That Moved a Fortune 500 Semiconductor Manufacturer off SAP HANA and into AI-Ready Infrastructure

The data layer migration: moving supply chain analytics off SAP HANA without touching live ERP performance

The first architectural decision was the hardest constraint: any analytics migration had to run in parallel with the live SAP HANA ERP, not as a replacement of it.

The transactional system had to keep running without interruption while the analytical layer was being built alongside it. That required a deep working knowledge of the CIBR data models inside SAP HANA, most of which had no current documentation and whose subject matter experts were no longer available.

The team worked backwards, analysing the data available in the development environment, generating synthetic datasets to enable testing without production data access, and using Python libraries to navigate the complex access permission structure that had been blocking migration progress. The result was a Snowflake environment with replicated CIBR views, a Python Flask API microservice layer for ingestion, and a data foundation that made ERP analytical queries a Snowflake problem rather than a SAP HANA problem.

This Phase Produced

• SAP HANA → Snowflake migration pipeline (Schema and data model replication)
• CIBR data model replication in Snowflake (Backward-engineered from undocumented source)
• Synthetic data generation for dev/test (Enabled testing without production data access)
• Python Flask API microservice ingestion layer (Source-to-Snowflake data movement)
• Snowflake Connector integration (Live query and ingestion connectivity)
• Access permissions resolution framework (Python-based mitigation for complex permission structures)

AI models in production: yield forecasting, PO delay prediction, and cherry-picking optimisation on a ready data foundation

Once the data layer was stable, the question was which AI use cases were now tractable that had been impossible before.

Three stood out as highest operational value:

1. predicting production yield and lead times so procurement could plan ahead of supply compression; identifying purchase order delays before they propagated downstream into manufacturing schedules; and optimising die-level cherry-picking, the process of selecting the highest-performing dies from a production wafer to maximise output quality.
2. Each model was built on SKLearn and deployed through Azure ML into an AKS Kubernetes environment, with a React and D3.js dashboard giving supply chain planners direct access to model outputs without requiring data science intermediaries.
3. The insight-driven dashboard combined live Snowflake queries with ML model inference, making predictive supply chain analytics accessible at the operations level rather than inside a data science team's tooling.

This Phase Produced

• Production Yield and Lead Time Forecast model (SKLearn model trained on historical supply chain data)
• Die-Level Cherry-Picking (DLCP) ML model (Wafer output quality optimisation)
• Purchase Order Delay prediction model (At-risk PO detection before downstream impact)
• Insight-driven supply chain dashboard (React + D3.js, live Snowflake queries + ML inference)
• Azure ML model deployment pipeline (Model training, deployment, and monitoring)
• MLOps pipeline on Azure AKS (Containerised model serving and retraining)