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Reference Implementation · Retail / Grocery · 8–12 week reference implementation

AI Demand Forecasting for High-SKU Grocery Retail

Reference implementation: how NDN Demand IQ addresses stockout reduction and forecast accuracy for mid-scale grocery retailers operating 100–200 locations

+32%
Forecast Accuracy
~45% drop
Stockout Rate
Significant
Waste Reduction
8–12 wks
Time to Value
Reference Implementation — This scenario is modeled against realistic industry constraints to demonstrate how the product architecture addresses this class of problem. It is not a historical client engagement.

Reference Scenario

Scenario: Reference Scenario — Southeastern US Grocery Retailer
Industry: Retail / Grocery
Scale: Scenario scale: 100–200 stores, 6,000–10,000 SKUs

The Challenge

A mid-scale grocery retailer operating 100–200 stores relies on legacy weekly aggregate forecasts with no SKU-level or store-level precision. Manual buyer overrides introduce inconsistency across regions, contributing to perishable write-offs and stockout rates on top-moving items. Category managers spend a disproportionate share of their week correcting forecasts rather than working on supplier strategy.

Our Solution

NDN Demand IQ is deployed in a phased engagement. Phase 1 consolidates POS data, promotional calendars, weather feeds, and competitor pricing into a BigQuery warehouse. Phase 2 trains gradient-boosted ensemble models per product category with weekly automated retraining. Phase 3 integrates AI-generated replenishment signals directly into the retailer's existing ERP system — no workflow changes required for store teams.

Modeled Outcomes

+32%
Forecast Accuracy
Modeled MAPE improvement vs. legacy weekly-aggregate baseline over a 90-day evaluation window
~45% drop
Stockout Rate
Modeled reduction in top-item stockouts based on improved replenishment signal precision
Significant
Waste Reduction
Perishable write-off savings scale with store count and SKU volume; modeled from industry benchmarks
8–12 wks
Time to Value
Typical phased rollout timeline from data onboarding to live replenishment signals

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