Grocery Promotion Planning with AI Demand Forecasting

Case studies

SCP

Grocery Promotion Planning with AI Demand Forecasting

Case study showing how ML-driven promotion planning and forecasting improved revenue, reduced planning time, and lowered excess inventory for a grocery retailer.

Published

June 4, 2026

Read time

3 min read

Source

Kinaxis

This case study details the deployment of Rubikloud's AI demand forecasting and constraint-based optimization engines within a grocery retailer's breakfast foods category. The solution ingests internal and external data, generates candidate promotion calendars, and evaluates millions of forecast possibilities against business rules. Results include 3-5% revenue uplift, 50% reduction in planning time, and 30% reduction in stockouts and excess inventory costs.

Key takeaways

ML models evaluate promotion timing, mechanics, and pricing to maximize incremental sales and margin.

Constraint-based optimization incorporates 15+ business rules and evaluates 4,200 unique scenarios.

Automation of weekly promotion planning reduced manual effort by 50%.

Promotion performance improved, delivering 3-5% revenue increase and 30% reduction in stockouts.

SKU rationalization lowered the number of promoted items while increasing incremental sales per item.

Market overview

SCR methodology note

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Important consideration

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