Data Integration from ERP & MES
Automatically merge ERP and MES data instead of exporting and reconciling manually.
Production and order data often sit in separate systems with different keys and formats. Standardized integrations via Azure Data Factory or similar tools bring ERP and MES data together in a shared model, creating end-to-end metrics from order to shop floor.
- A unified view from order to completion.
- Less manual coordination between departments.
- Faster reaction to production deviations.
manual reconciliation
faster reporting
to steady state
data errors
Calculated from time spent on manual Excel reconciliation before the integration was introduced.
- 01
Interface analysis
We review available interfaces and data models in ERP and MES.
- 02
Mapping design
We define shared keys and transformation rules.
- 03
Pipeline build
We build automated, monitored integration pipelines.
- 04
Validation
We reconcile results against existing reports.
- 05
Rollout & operations
We put the integration into production and hand over operations.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Order data
Customer orders and bills of material from the ERP.
Production data
Feedback and cycle times from the MES.
Material movements
Inventory and warehouse movements along production.
Quality data
Test results and scrap rates from quality assurance.
Master data
Article, customer and supplier master data.
Machine logs
Event and downtime logs from equipment.
Production management
End-to-end view of order status and capacity.
Sales
Reliable delivery dates based on real production data.
Controlling
Consistent metrics without manual rework.
IT
Maintainable, documented interfaces instead of one-off fixes.
A unified view from order to completion.
Less manual coordination between departments.
Faster reaction to production deviations.
Reliable metrics for sales and planning.
A base for predictive planning and analytics.
With a solid data foundation this use case gets faster, cheaper and far more stable.
An existing data platform offers ready-made connection patterns.
Central master data reduces mapping effort.
Monitoring catches integration errors early.
Reusable pipelines speed up further integrations.
Lakehouse architecture
Integrated ERP/MES data flows directly into the lakehouse.
Data quality & observability
Automated quality checks safeguard integration results.
Business intelligence
Reports draw on consolidated order and production data.
Lakehouse Architecture
A central lakehouse replaces the patchwork of data warehouse, data lake and Excel exports.
Streaming & Real-Time Data
Instead of daily batch runs, relevant metrics are available within seconds.
Data Modeling & Semantic Layer
A shared semantic layer ensures 'revenue' means the same thing everywhere.