Data Engineering Case Study

Supply Chain Data Platform

Azure-powered data engineering platform for a global CPG manufacturer, achieving 25% improvement in case fill rates and 10x faster insights.

Supply Chain Data Platform

The Challenge

A leading multinational consumer goods (CPG) company struggled with fragmented data across multiple ERP systems, warehouses, and logistics partners, leading to poor visibility into supply chain operations. This resulted in frequent stockouts, delayed shipments, and inefficient inventory management, costing the business millions in lost revenue and excess holding costs annually.

The primary objectives were to create a unified view of end-to-end supply chain KPIs, enable real-time decision-making, and reduce operational inefficiencies by 30% within the first year.

Manual reporting processes took days to compile, hindering proactive responses to demand fluctuations and supply disruptions. Legacy systems lacked scalability for growing data volumes from IoT sensors and e-commerce channels.

The Solution

We designed a modern data engineering platform using Microsoft Azure as the core tech stack, featuring Azure Data Lake for centralized storage, Azure Data Factory for automated ETL pipelines, and Azure Synapse Analytics for scalable warehousing to ingest and process data from 20+ disparate sources including ERPs, IoT devices, and third-party logistics APIs.

Key features included real-time data pipelines for supply chain KPIs (e.g., case fill rate, on-time delivery), data quality frameworks with automated validation, and a unified BI dashboard powered by Power BI for role-based access across teams.

The architecture employed a data mesh approach for domain-specific data products, ensuring governance and discoverability while supporting advanced analytics like predictive demand forecasting.

Tech Stack

Azure Data Lake
Azure Synapse Analytics
Azure Data Factory / dbt
Azure Event Hubs
Power BI
Azure Kubernetes Service (AKS)

Implementation

Our cross-functional team of 8-including 3 data engineers, 2 architects, PM, and domain experts-collaborated via Agile sprints with bi-weekly demos.

Technical highlights included migrating 5TB of historical data to Azure Synapse with zero downtime, implementing Azure Data Factory pipelines for 50+ daily workflows, and integrating Azure Event Hubs for real-time IoT streaming. Challenges like data schema mismatches were resolved through automated reconciliation rules, ensuring 99.5% pipeline uptime.

Results & Impact

25% Better Fill Rate

Reduced stockouts and improved customer satisfaction across 10+ warehouses

22% On-Time Delivery

Enabled proactive routing optimizations via real-time visibility

15% Less Excess Stock

Alongside 80% faster reporting

10x Faster Insights

Dashboards refreshed in minutes instead of days

Key Takeaways

Leveraging Microsoft Azure accelerates data platform delivery with integrated services like Data Factory and Synapse, ideal for enterprise-scale supply chains. Data mesh principles enhance ownership and agility. Prioritizing Microsoft ecosystem ensures seamless integration with existing tools for faster ROI.

Outcome

The client reported: "This Azure-powered platform turned our supply chain from reactive firefighting to predictive excellence, unlocking efficiencies we didn't know were possible." The partnership evolved into ongoing GenAI enhancements for demand forecasting.

Have a Similar Project in Mind?

Let's discuss how we can build solutions tailored to your needs.

Schedule a Consultation