Demand Forecasting & Inventory AI
RetailMax
94% SKU-level forecast accuracy — reducing stockouts by 55% and overstock by 30%.
Overview
Project Overview
RetailMax operates 380 stores and an e-commerce platform with 45,000+ SKUs. Their spreadsheet-based forecasting relied on historical averages that couldn't account for seasonality, promotions, local events, or market shifts — leading to $12M annually in excess inventory and lost sales.
AgilizTech built a predictive demand engine using time-series models that forecasts SKU-level demand with 94% accuracy across all locations — reducing stockouts by 55% and overstock by 30%, recovering millions in previously lost revenue.
Industry
Retail & E-commerce
Timeline
12 weeks
Tech Stack
The Challenge
What problems needed solving?
Inaccurate Demand Signals
Existing forecasts based on 12-week moving averages achieved only 61% accuracy at the SKU-store level, with systematic bias during promotional and seasonal periods.
Chronic Stockout Problem
Top-selling items experienced stockouts 18% of the time, resulting in an estimated $7M in lost sales annually and significant customer frustration.
Excess Inventory Burden
Slow-moving and over-ordered inventory tied up $5M+ in working capital and required frequent markdowns that eroded margins by 4 percentage points.
No Local Context
National-level forecasts ignored store-specific demand patterns driven by local demographics, weather, events, and competitive dynamics.
The Solution
How AgilizTech delivered results
Multi-Signal Demand Model
Built an ensemble of time-series models that incorporate sales history, pricing, promotions, weather, local events, and economic indicators for SKU-store-day level forecasts.
Automated Replenishment Engine
Created a downstream optimizer that translates demand forecasts into purchase orders, safety stock levels, and distribution plans automatically.
Promotion Impact Simulator
Developed a what-if engine that predicts demand uplift from planned promotions, enabling merchandisers to optimize discount depth and timing.
Real-Time Demand Sensing
Implemented a near-real-time adjustment layer that detects early demand signals from POS data and adjusts short-term forecasts within hours.
The Benefits
Measurable business impact
94%
Forecast Accuracy
SKU-store-week level accuracy, up from 61% — enabling precise inventory planning.
55%
Stockouts Reduced
Reduction in stockout incidents, recovering an estimated $3.9M in annual sales.
30%
Overstock Reduced
Reduction in excess inventory, freeing $1.5M in working capital.
+2.1%
Margin Improvement
Gross margin improvement driven by fewer markdowns and better sell-through rates.
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