Overview
A mid-tier manufacturer needed to integrate data from ERP, manufacturing execution systems (MES), and IoT sensors to improve supply chain visibility and forecasting.
The Challenge
Data from SAP, Oracle MES, and IoT sensors was in different formats and systems. Analysts spent 80% of their time on data wrangling. Forecasting was manual and inaccurate, leading to overstock and stockouts.
The Approach
We built a Databricks-based lakehouse using Delta Live Tables for automated pipelines. IoT data streams were ingested via Azure IoT Hub; ERP and MES data were pulled via scheduled jobs.
Implementation
Key components included:
- Bronze layer: Raw ingestion from ERP, MES, and IoT with schema inference
- Silver layer: Cleansed and validated data with DLT expectations
- Gold layer: Demand forecast inputs, inventory KPIs, production bottleneck analytics
- Power BI: Supply chain dashboards with drill-through to detail
Results
- 35% improvement in demand forecast accuracy (MAPE reduction)
- Automated replenishment alerts based on safety stock and lead times
- Production bottleneck visibility with real-time line utilization metrics
- Reduced manual data work by 70% for the analytics team
Key Lessons
- IoT data volume can grow fast—design for incremental processing
- DLT expectations caught data quality issues before they reached reports
- Work closely with supply chain planners to define KPIs
- Start with one plant or product line before scaling