SAP HANA to Snowflake Migration for Supply Chain Analytics: Building Predictive Manufacturing at Scale

From ERP Bottlenecks to AI-Ready Analytics: A Case Study in Supply Chain Modernization

One-liner summary:

A global semiconductor leader partnered with Ideas2IT to migrate critical supply chain workloads from SAP HANA to Snowflake, unlocking predictive analytics, cost savings, and real-time operational intelligence across global manufacturing.

The Problem with the Status Quo

One of the most complex global supply chains in the semiconductor industry faced new pressure from pandemic disruptions, geopolitical shifts, and raw material shortages.

Forecasting, order visibility, and production yield optimization were constrained by tightly coupled SAP HANA environments that were expensive, rigid, and not designed for ML-scale workloads.

To stay agile in a volatile environment, the enterprise needed a modern data foundation and AI-enabled supply chain capabilities.

Where the Gaps Were

Key issues faced:

Static dashboards and batch processing delayed time-to-decision
No elasticity in SAP HANA for large-scale analytics or ML training
Lack of granular visibility into yield prediction and order risk
High operational costs and limited scalability across regions
Tooling mismatch for AI/ML workflows slowed experimentation

They needed to leap from traditional ERP-bound analytics to a composable, cloud-native data environment.

What We Delivered

Ideas2IT architected and executed a high-performance migration from SAP HANA to Snowflake, coupled with an AI-driven supply chain analytics stack.

Core Elements:

Data model translation + restructuring of key SAP HANA supply chain tables
Snowflake-native schema design optimized for query performance and ML readiness
AI/ML models built to forecast production yield, predict PO delays, and model die-level cherry-picking (DLCP)
Custom dashboards + business logic for real-time operations teams
Kubernetes-based MLOps stack deployed on Azure for model training, deployment, and drift monitoring
End-to-end monitoring using Azure ML, Azure Functions, and Snowflake connectors

The result was a complete transformation of the data ecosystem.

Outcomes We Achieved

KPI	Outcome
Production yield	↑ 15%
Supply chain efficiency	↑ 20%
Delivery time	↓ 10%
Sales enablement	↑ 3%
Operational cost	↓ 10–20%

The enterprise now simulates risk, forecasts constraints, and reallocates capacity dynamically on a modern, ML-ready data platform.

Key Takeaways

AI doesn’t scale on legacy platforms. Your ERP might store your data, but it won’t unlock insights without a modern data layer.
Snowflake is a foundation for real-time, analytics-driven operations.
ML must be operationalized end-to-end. From models, to deployment, retraining, and explainability.

This was about future-proofing one of the most critical manufacturing ecosystems.

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