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Manufacturing Digital Supply Chain Reference Architecture

This architecture helps you understand integrations with common industry sources and sinks for digital supply chain use cases for Manufacturing. It outlines the best practice design patterns across the lakehouse architecture.

Reference architecture for digital supply chain on Databricks with product elements overlaid on top of industry data sources and sinks.

Establish your digital supply chain hub for intelligent resupply and analytics

Data and platform flows:

  1. ERP, WMS, TMS, news, email and market data collected from on-premises/hybrid data systems using Lakehouse Federation or Lakeflow Connect.
  2. On-premises data and cloud/lakehouse–native data (Databricks Marketplace, Delta Sharing, web APIs) are ingested in the Bronze layer alongside metadata, eliminating the need for additional ETL to recover source system semantics.
  3. Clean and enrich data using DLT for both batch and streaming data pipelines into Silver tables (sales, supply, production, transportation). Silver tables are used in AI models to predict part-level demand, supplier delivery risk and inventory optimization, thereby accelerating supply chain planning cycles across millions of SKUs.
  4. Data is aggregated hourly, daily, monthly or quarterly to support real-time analysis of supply chain performance, compliance, supplier exposure and inventory position and to understand disruptions in natural language with Databricks SQL and AI/BI.
  5. Multimodal forecasting and autonomous supply chain countermeasures (automated order, inventory reallocation, supplier sourcing) orchestrated through Mosaic AI and the Mosaic AI Agent Framework.

 

Industry Notes
Manufacturing and Automotive Data Intelligence Outcome Map - Digital Supply Chain

Sources
Partner/Builton

Marketplace

Other Data

Business Apps