StockSense: Intelligent Inventory Optimization
Demand forecasting and automated inventory management powered by time-series AI, reducing stockouts and excess inventory across 500+ retail locations.
Project Overview
StockSense is an intelligent inventory optimization engine that combines proprietary time-series forecasting models with external data signals including weather, local events, economic indicators, and competitor pricing to predict demand at the store-SKU level. The platform automates replenishment decisions, optimizes safety stock levels, and provides seasonal planning tools that have eliminated the guesswork from inventory management.
The Challenge
The retailer was hemorrhaging margin due to chronic inventory imbalances. Stockout rates averaged 8.3% across their network, directly causing an estimated $94 million in lost annual revenue. Simultaneously, excess inventory in slow-moving categories was tying up $67 million in working capital and driving markdowns that eroded gross margins by 3.2 percentage points.
Their existing replenishment system relied on simple moving averages and static reorder points set manually by category managers. These rules could not account for the complex interplay of seasonality, local market dynamics, promotional cannibalization, and supply chain variability. During peak periods such as back-to-school and holiday seasons, demand planning accuracy dropped below 55%, leading to both surplus and shortage crises.
Supply chain visibility was fragmented across three distribution centers, 14 regional warehouses, and 520 stores, each with different inventory management systems. The lack of a unified demand signal made it impossible to optimize allocation decisions or implement effective store-to-store transfers. Category managers spent over 60% of their time on manual inventory adjustments rather than strategic merchandising decisions.
Our Solution
We developed StockSense around a hierarchical time-series forecasting engine that generates demand predictions at the store-SKU-day level with a 28-day forecast horizon. The core model is a temporal fusion transformer that processes over 200 features per prediction, including historical sales, price elasticity curves, promotional calendars, weather forecasts, local event schedules, and macroeconomic indicators.
The system incorporates an automated feature engineering pipeline that discovers and ranks demand signals without manual intervention. For example, the model learned that ice cream sales at coastal stores spike 48 hours before a forecasted heat wave, while specific apparel categories see demand shifts correlated with social media trend velocity. These non-obvious signals improved forecast accuracy by 18% over baseline.
An optimization layer translates demand forecasts into actionable replenishment orders, dynamically adjusting safety stock levels based on supplier lead time variability, forecast confidence intervals, and product margin profiles. The system also identifies opportunities for lateral transfers between stores, routing excess inventory from overstocked locations to stores facing potential stockouts.
We built a unified analytics platform that gives category managers, store managers, and supply chain leaders a single source of truth for inventory health. Interactive dashboards surface key performance indicators, highlight at-risk SKUs, and enable scenario planning for promotional events and seasonal transitions. Automated alerts notify stakeholders of emerging issues before they impact the shelf.
Tech Stack
Key Results
Reduction in stockout rate from 8.3% to 6.2%, recovering an estimated $23 million in previously lost revenue
Improvement in inventory turnover ratio, freeing $28 million in working capital
Store-SKU level demand forecast accuracy at 14-day horizon, up from 61% with the prior system
Decrease in forced markdowns due to better alignment of inventory levels with actual demand patterns
“StockSense fundamentally changed how we think about inventory. Our category managers went from spending their days fighting fires to actually planning strategically. The demand forecasting accuracy is remarkable, and the automated replenishment has given us confidence to reduce safety stock without risking availability. The ROI was evident within the first quarter.”
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