Supply Chain Analytics Transformation for a Manufacturing Client

Client Overview

Client: A mid-sized manufacturing company producing consumer electronics.

Industry: Manufacturing

The Challenge

The supply chain team relied on manual Excel-based reports for managing procurement, production planning, inventory, and logistics, leading to:

• Fragmented Data: Data spread across multiple systems, such as ERP, inventory management, and shipping tools, creating inconsistencies.
• Inefficiencies in Reporting: Preparing reports was time-intensive, with frequent errors and delays.
• Limited Insights: Excel reports lacked advanced analytics capabilities for proactive decision- making.

The client required a modern, automated analytics solution to integrate data sources, improve reporting, and provide actionable insights.

The Solution

The project involved setting up a centralized data pipeline using data engineering practices, developing interactive Power BI dashboards, and automating reporting processes.

1. Data Engineering: Centralizing Supply Chain Data

Data Integration:

Connected multiple data sources, including:

ERP systems for procurement and production data.

Inventory management software.

Third-party logistics (3PL) systems for shipping and delivery tracking.

Used Azure Data Factory to extract, transform, and load (ETL) data into a unified SQL database.

Data Cleaning and Transformation:

Standardized key data points such as product codes, supplier IDs, and time stamps.

Resolved inconsistencies, such as duplicate records and missing values.

Data Modeling:

Designed a star schema optimized for analytics, with:

Fact Tables: Procurement orders, production metrics, inventory transactions, and shipping records.

Dimension Tables: Products, suppliers, warehouses, locations, and dates.

2. Power BI Dashboards: Interactive Supply Chain Reporting

Key Dashboards Developed:

Procurement Performance: Monitored supplier lead times, delivery accuracy, and cost trends.

Inventory Management: Tracked stock levels, turnover rates, and reorder points by product and warehouse.

Production Efficiency: Visualized machine utilization rates, production delays, and throughput trends.

Logistics and Delivery: Analyzed on-time delivery rates, shipping costs, and transit times.

Advanced Visualizations:

Heatmaps for identifying underperforming suppliers or high-cost delivery routes.

Line charts for inventory trends and forecasts.

Drill-through capabilities for detailed analysis at the SKU or shipment level.

Real-Time Data Refresh: Enabled dashboards to pull data automatically from the centralized database, providing up-to-date insights.

3. Process Automation

ETL Automation: Scheduled daily and weekly data refreshes using Azure Data Factory.

Report Distribution: Configured Power BI subscriptions to send tailored reports to stakeholders.

The Outcome

1. Enhanced Reporting Efficiency

Report preparation time reduced by 60%, saving the supply chain team 20+ hours per week.

2. Improved Decision-Making

Real-time inventory dashboards reduced stockouts by 30% and excess inventory by 20%.

Supplier performance insights helped renegotiate contracts with underperforming vendors, reducing procurement costs by 10%.

3. Optimized Operations

Production line bottlenecks were identified and resolved, improving throughput by 15%.

Logistics analysis reduced average delivery time by 20%, enhancing customer satisfaction.

4. Scalability

The Power BI system supported the addition of new metrics (e.g., carbon footprint tracking) and accommodated future business growth.