The Challenge
This automotive parts manufacturer operated 12 production facilities across North America, Europe, and Asia, producing 50,000+ SKUs for major OEMs. Supply chain visibility was fragmented across three different ERP systems (SAP in Europe, Oracle in North America, and a custom system in Asia). Plant managers had no real-time view of inventory levels at other facilities, leading to excess stock at some plants and stockouts at others. Equipment failures caused $8M+ in unplanned downtime annually. Quality data was collected on paper forms and entered into spreadsheets days after production runs.
Our Solution
Built a unified supply chain data model in Microsoft Fabric that consolidates data from SAP, Oracle, and the custom Asian ERP into a common schema. Automated data pipelines run every 30 minutes for inventory and every 5 minutes for production data.
Deployed IoT sensors on 200+ critical production machines connected via Azure IoT Hub to Fabric Real-Time Intelligence. Eventstreams process vibration, temperature, and pressure data for anomaly detection using KQL-based rules.
Created predictive maintenance models using Fabric Data Science notebooks that analyze equipment sensor patterns to predict failures 48-72 hours in advance, enabling planned maintenance windows instead of emergency shutdowns.
Built real-time production dashboards showing OEE (Overall Equipment Effectiveness) by plant, line, shift, and machine. Quality metrics feed from automated inspection stations to Power BI with SPC (Statistical Process Control) charting.
Implemented global inventory optimization dashboard showing stock levels, in-transit inventory, reorder points, and demand forecasts across all 12 facilities with drill-through to plant-level detail.
Results
Inventory optimization and reduced emergency shipping eliminated $12M in annual supply chain waste.
All 12 facilities have real-time visibility into global inventory levels across 50,000+ SKUs.
Predictive maintenance models forecast equipment failures 48-72 hours in advance.
Proactive maintenance scheduling reduced unplanned downtime from $8M to $2.8M annually.
IoT sensors on critical equipment feed real-time data to anomaly detection algorithms.
All facilities across 4 continents share a single analytical platform.
Implementation Methodology
Phase 1 (Weeks 1-5): Data integration and IoT deployment. Unified ERP data into Fabric lakehouse, deployed IoT sensors on priority equipment, and established real-time data pipelines.
Phase 2 (Weeks 6-10): Analytics development. Built OEE dashboards, supply chain visibility reports, quality SPC charts, and inventory optimization models.
Phase 3 (Weeks 11-14): Predictive analytics and rollout. Deployed predictive maintenance models, trained plant managers and operations teams, and established monitoring protocols.