
Project
Adone WooCommerce Inventory Management Tool
A desktop application that automates WooCommerce product price and stock updates from Excel while validating product and variation matches and reporting discrepancies for faster follow-up.
Problem
After launching the online store with roughly 2,000 Al Amal Pharmacy products, price changes and branch stock depletion required frequent WooCommerce updates. Because the company ERP was not directly integrated with the store, customers could order unavailable products or see outdated prices, creating extra work to source alternatives, approve price differences, and manage operational losses and poor customer experiences.
Goals
Create a tool that receives product prices and quantities from Excel, transforms them into the structure required by WooCommerce, and applies accurate store updates while minimizing manual work and stock or pricing errors.
Solution
A C# desktop tool was developed to import Excel product, price, and quantity data and match every record to the correct WooCommerce product and variation before preparing the update file. It avoids uploading products that do not exist in the store and produces a discrepancy list to support replenishment requests and follow-up on records requiring attention.
Process
- 01
Research & analysis
Understand the problem, workflow, and actual user needs.
- 02
Planning
Map the solution logic and select suitable tools and technologies.
- 03
Development
Build the features, process data, and connect system elements.
- 04
Testing
Validate functionality and results, then address feedback.
- 05
Launch
Prepare and deliver the solution for operational use.
Challenges
The primary challenge was the large product catalog and the need to conform precisely to the WooCommerce import template. A matching or structural error could accidentally create new products or update the wrong prices and quantities. Validation therefore focused on product identity, variations, and excluding unmatched records before an update was prepared.
Results
The tool improved the speed and accuracy of store updates, reducing processing time to roughly 10% of the previous workflow and manual effort to 5% or less. It also improved stock and pricing reliability, reduced orders affected by unavailable items or price differences, and supported better customer satisfaction.

