Overview
A fast-growing e-commerce and sports retail company faced recurring challenges in accurately predicting customer demand. Traditional forecasting models failed to account for seasonal variations, marketing campaigns, and live sports events.
The project implemented a cutting-edge predictive analytics platform leveraging AI and machine learning to enhance inventory planning and optimize supply chain efficiency.
Problems
The legacy demand forecasting model relied on historical sales data and static regression models, which resulted in underestimating demand spikes during special campaigns and major events.
Operational costs increased due to overstocking of slow-moving products and frequent stockouts of high-demand SKUs.
Inventory and warehouse operations were reactive rather than predictive, causing delayed fulfillment and lost revenue opportunities.
The manual forecasting process was time-intensive, required multiple teams, and lacked real-time insights.
Marketing and sales teams operated in silos, with no unified system to align demand signals with inventory strategies.
Solutions
A predictive analytics engine powered by machine learning models was designed, incorporating real-time sales signals, historical patterns, weather data, event schedules, and marketing campaigns.
The system uses a demand-sensing approach to adjust forecasts dynamically, enabling more accurate inventory planning and automated purchase orders.
An intuitive dashboard allows business teams to visualize live demand patterns, upcoming spikes, and potential bottlenecks.
The AI engine integrates directly with ERP and warehouse systems, enabling end-to-end demand planning automation.
This led to a 30% improvement in forecast accuracy, 45% fewer stockouts, and improved revenue during high-demand periods.
