An international drugstore chain updates their legacy systems to a cloud-based forecast management system

The Pain Point

The client is an international $105B drugstore chain with 331,000 employees in over 9,000 drugstores worldwide. With a huge inventory, the client was looking for a more accurate and efficient forecast management software that is cloud based and utilizes machine learning for just-in-time weekly inventory demands.

The Solution

Building upon an existing solution, we leveraged innovative data science and predictive analytics to forecast just-in-time weekly inventory demands. The Softensity team customized the software, enhancing it with new algorithms and performing quality assurance to meet the client’s needs. This new system leveraged two years’ worth of data to learn from it and forecasted demand and inventory intelligently. It also took into account the client’s promotion strategies, advertising models and calendar hierarchy. Our team optimized and enabled new data pipelines to enable machine learning and efficient forecasting. In this project, Softensity provided project leadership, software engineers as well as quality assurance resources for testing.

The Results

Softensity Case Study 1

Optimizing the data pipeline for weekly batched processes increased timely forecasting accuracy by 30% for millions of products with different attributes, calendar hierarchies and seasonal needs

Contact: Scott Kirby, Director of Consulting Services / 404.451.7110 /

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