Optimizing Inventory Management in Fashion Retail

The primary challenge was to reduce the amount of inventory that remained unsold in the outlet section after each season using predictive analytics and AI. By leveraging transactional data from each store and their geographical location, the goal was to accurately predict which products would sell in each season.

Adidas, a global leader in sports apparel and footwear, operates an extensive network of retail outlets and franchises worldwide. Faced with challenges related to overstock and seasonal inventory clearance, Adidas sought to leverage advanced machine learning technologies to enhance its inventory management practices. The goal was to minimize clearance sales and associated costs by better predicting product demand at each store, thereby improving efficiency and reducing excess inventory.

PulseUp founder and a team of highly skilled Data scientists led this project in his previous company EPICA, in collaboration with the Adidas team. The team implemented a solution involving real-time processing and analysis of inventory units sold to determine future inventory orders for the franchise owner in Colombia. 

This approach included several key actions:

1. Activation of real-time sales reporting by location and category.
2. Execution of purchase orders for suggested inventory.
3. Review of proposed KPIs.

Adidas, through its franchises, noticed a high number of garments moving to clearance and subsequently being sold at outlets, which incurred additional costs. Data analytics from their point-of-sale systems became the primary resource for forecasting future product demand more accurately, aiming to enhance efficiency in the inventory presented in each store.

The project analyzed over 42,000 SKUs and achieved a significant reduction in seasonal inventory by 10.3% within the first eight months. This reduction not only optimized inventory levels but also decreased the financial burden associated with unsold stock moving to outlets.

This implementation showcases how advanced machine learning algorithms can transform inventory management by predicting future sales based on detailed, real-time data analysis. PulseUp’s solution capitalized on historical sales data and integrated geographical insights to tailor inventory orders more precisely, reducing overstock and minimizing clearance sales.

The integration of AI and machine learning into Adidas's inventory management process allowed for a sophisticated, data-driven approach to order fulfillment. By aligning inventory orders more closely with predicted sales, Adidas was able to streamline operations, reduce excess stock, and lower overall costs. This case study exemplifies the power of predictive analytics in enhancing operational efficiency and responsiveness in the retail sector.