AI Retail Demand Forecasting Platform
From guesswork to data-driven inventory and sales planning for a national retail chain.
Impact
What changed.
Stockouts reduced by 40%
AI-driven forecasts gave buyers the data to pre-position stock before demand spikes — eliminating the empty shelf moments that previously cost the business revenue and customer trust.
Inventory costs down
Overstocking dropped significantly as purchasing aligned with actual projected demand. Dead stock markdowns fell and working capital improved across categories.
Buyers work smarter
Category managers shifted from reactive firefighting to proactive planning — reviewing recommendations, adjusting for local factors, and focusing on strategy instead of spreadsheets.
The challenge
Before
The client struggled with frequent stockouts and overstock due to manual, intuition-based inventory planning across 50+ stores. Buyers were making purchasing decisions based on gut feel with no historical data analysis, seasonal trending, or promotion lift modeling — leading to empty shelves during peak periods and excess dead stock eating into working capital.
- Manual sales tracking in Excel with no trend analysis
- No predictive analytics — purchasing decisions based on buyer intuition
- Frequent stockouts during peak seasons — lost revenue and customer dissatisfaction
- Excess overstock tying up capital and driving markdown losses
- No centralized dashboard — buyers operated in silos across product categories
- Promotions planned without demand lift modeling
The solution
What we built
We built a machine learning demand forecasting platform that ingests historical sales data, seasonality patterns, promotional calendars, and external signals to generate SKU-level forecasts across all 50+ stores. Buyers receive automated replenishment recommendations with confidence intervals. A centralized analytics dashboard gives category managers visibility on forecast accuracy, stockout risk, and overstock exposure — updated daily.
Core workflow connections
How the system flows.
- Sales History + Seasonality DataML Model TrainingSKU-level Demand Forecast
- ForecastReplenishment RecommendationBuyer ReviewPurchase Order
- Promotion CalendarLift ModelingAdjusted ForecastStock Pre-positioning
- DashboardStockout Risk AlertOverstock FlagCategory Manager Action
Process
How we built it.
Sales History + Seasonality Data → ML Model Training → SKU-level Demand Forecast
Forecast → Replenishment Recommendation → Buyer Review → Purchase Order
Promotion Calendar → Lift Modeling → Adjusted Forecast → Stock Pre-positioning
Dashboard → Stockout Risk Alert → Overstock Flag → Category Manager Action
Start a project
Want AI-driven forecasting for your retail or distribution business?
We build custom demand forecasting and inventory planning tools for retailers, distributors, and supply chain teams — trained on your data, integrated into your buying workflow.
No retainer lock-in · Month-to-month · Full transparency
