Achieving 50% Reduction in Manual Inventory Tracking For A Footwear Manufacturer With eCommerce Data Management & Visualization

Client Overview

UK-Based Footwear Manufacturer Operating Across Retail, Wholesale, and Online Channels

The client was one of the oldest and largest footwear manufacturers in the UK. Known for quality footwear, they sold their products through third-party retail showrooms, online marketplaces & platforms, wholesale channels, and branded stores.

Business Requirements

Centralizing Orders and Inventory Across Online and Offline Channels For Operational Visibility

As the client expanded into online marketplaces, they needed a cohesive system to track orders, inventory levels, and pricing across both digital and physical sales channels. The client required Data4eCom to:

  • Consolidate order, stock, pricing, and sales data from Amazon, eBay, and Etsy alongside their offline retail and wholesale channels into a single centralized repository.
  • Automate the tracking of inventory movements, order status updates, and price changes across all active platforms to reduce dependence on manual monitoring.
  • Develop visual dashboards to showcase sales trends, channel-by-channel order volumes, inventory turnover, and product performance for executive and operational review.
  • Provide end-to-end inventory analysis support for proactive stock level monitoring, demand forecasting, and turnover optimization.

Key Challenges

Addressing Disconnected Systems That Limited Pricing, Inventory, and ROI Visibility

Data spread across four channels: Order, inventory, pricing, and sales data were stored separately across Amazon, eBay, Etsy, and offline systems, with no standardization. These factors made it difficult to build a single, accurate view of business performance without extensive manual work.

Inconsistent pricing reduced margins: Without coordinated price updates across platforms, the same product was often listed at different prices at the same time. This led to sales at very low or zero margin, and the issue was often discovered only after the sale.

ROI reporting was difficult and unreliable: Measuring returns by channel or product category required manual extraction and cross-checking of data from disconnected systems. The process took time and often produced inconsistent results.

Manual work increased with online growth: As online order volumes grew, so did the effort required to update inventory and reconcile orders across platforms. This placed growing pressure on the internal operations team.

Our Solution

Centralizing Commerce Data for Real-Time Monitoring and Analysis

Data Consolidation Across Online and Offline Sales Sources

We started by reviewing the client’s sales and inventory data from both marketplace and offline channels to determine which fields were relevant for reporting and where the existing records lacked consistency. This included order data, stock information, pricing, and sales figures. Before bringing the data together, we used Python scripts to clean and prepare the records for standardization.

Once the source data was validated, AWS Glue was used to extract, transform, and align the records from each channel into a consistent structure. The processed data was then loaded into Amazon Redshift, creating a centralized environment for storage and analysis.

Automated Monitoring of Orders, Stock, Sales, and Pricing

To reduce reliance on manual tracking, we set up AWS Lambda to automate the monitoring of changes across core commerce data points, including orders, inventory levels, sales activity, and price updates. This gave the client a near real-time view of changes taking place across channels.

Because the data volume was substantial, Apache Spark was used to support large-scale processing efficiently. Amazon Redshift also enabled faster querying of the consolidated data, making it easier to retrieve and analyze updated records.

Power BI Dashboards Built Around Business KPIs

After centralizing the data, we connected the repository to Power BI and built dashboards to give the client a clearer view of performance across channels. These dashboards covered sales trends, order comparisons, product-level performance, and inventory movement.

We also defined the key metrics to be tracked, including sales trends, inventory turnover, and channel-wise order performance. Based on those metrics, we created custom dashboard templates for different business users. Interactive elements such as filters, slicers, and dynamic charts were included to support deeper analysis.

Inventory Analysis Support for Demand Visibility

We also supported the client with inventory analysis to improve stock monitoring, demand planning, and turnover management. Using AWS Glue for ETL, Amazon Redshift for integrated inventory data, and machine learning services such as SageMaker, we analyzed product movement and related pricing patterns.

This helped identify slow- and fast-moving products clearly. Based on those findings, we developed dedicated dashboards that showed current stock positions, holding costs, and expected demand.

Technology Stack

Apache Spark
Apache Spark
AWS Lambda
AWS Lambda
Power BI
Power BI.
AWS Glue
AWS Glue
Amazon Redshift
Amazon Redshift
Amazon SageMaker
Amazon SageMaker
Project Outcome

Improvements in Tracking, Forecasting, and Product Visibility

50%

Reduction in manual tracking and updating of inventory and orders.

35%

Improvement in demand prediction accuracy through ML-supported analysis.

30%

Increase in actionable insights through executive dashboards.

25%

Reduction in low-margin obsolete products through better identification of slow- and fast-moving items.

Get In Touch

Looking for End-to-End Product Data Management Services?

Our team helps businesses organize product, pricing, inventory, and sales data effectively with end-to-end product data management and leverage it to design clear dashboards through data visualization services.

Write to us at info@data4ecom.com to schedule a free service demo.

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