A multicategory retail brand selling both direct to consumer through its own websites and via major marketplace retailers like Amazon sought to improve the accuracy and reliability of its demand forecasting process.
With a wide and dynamic product portfolio spanning multiple categories and distribution centers, forecasting demand accurately at the SKU and warehouse level was critical for maintaining optimal inventory, meeting customer demand, and avoiding costly stockouts.
The client's demand planning process relied heavily on manual, rule-based logic, which led to inconsistent accuracy - especially across fast-moving or promotional products.
Frequent stockouts disrupted not only sales but also the integrity of historical sales data, making trend estimation difficult.
Thousands of SKUs across multiple warehouses made manual forecasting infeasible.
Lack of higher-level visibility across product categories and regions made it hard to identify overarching demand trends.
New products had no historical data, requiring separate treatment from existing products.
The objective was to build an AI-driven demand forecasting system that could:
We developed a scalable demand forecasting engine powered by advanced time-series and causal modeling techniques. The model generates SKU-level and SKU-warehouse-level forecasts, combining statistical patterns with business context (price, promotions, and holidays).
Unified and cleaned historical sales data from multiple systems.
Identified and adjusted for stockout-affected periods to correct underreported demand.
Enriched data with key business signals including pricing, promotional flags, and holidays.
Established consistent hierarchies (SKU → category → warehouse → total) to enable roll-ups and drill-downs.
The forecasting framework automatically selects appropriate time-series configurations per SKU and warehouse.
The model generates forecasts weekly, projecting demand for the next 40 weeks - giving demand planners sufficient forward visibility for purchase and replenishment planning.
The AI-driven forecasting solution delivered both quantitative and qualitative impact for the retailer's planning and supply chain teams.
Early detection of potential stockouts and overstock situations led to more stable fulfillment cycles. Even without formal accuracy benchmarks, the planning team observed fewer last-minute corrections, more stable purchase cycles, and faster decision-making.
The AI-driven demand forecasting system transformed the client's supply chain planning from a reactive, manual process into a proactive, data-driven operation. By combining advanced time-series modeling with business context, the solution provided accurate, scalable forecasts that empowered the planning team to make better decisions with greater confidence.
The result is improved inventory management, reduced stockouts, more efficient operations, and a forecasting system that scales effortlessly with business growth.