/case-studies / retail-demand-sensing
DATA FOUNDATIONS FOR AI

Demand sensing cuts stock-outs and waste for a retailer

client
Grocery Retailer
industry
Retail
services
Data Foundations · AI / ML
duration
7 months
fig.80// skillikzmodeltraininfervectorAImodel.evalrollout86%98%accuracyusage80coveragelive
// OVERVIEW

Grocery Retailer — a retail organisation — engaged Skillikz on ai demand sensing: Forecasting that balances availability against waste. This case study sets out the business challenge, the AI-led approach we took, the technologies involved and the measurable outcomes delivered over 7 months.

// TECHNOLOGIES
DatabricksSnowflakedbtApache SparkKafkaVector DB
-20%
stock-outs
-15%
waste
store-level
forecasts
daily
refresh
01 // THE CHALLENGE

Coarse forecasts caused both empty shelves and spoilage, hurting sales, sustainability and margin at once.

02 // OUR APPROACH

We built store- and SKU-level demand sensing on a refreshed data platform, refreshed daily and integrated into replenishment.

AI-ready data pipelines and feature store
Store/SKU demand-sensing models
Integration into replenishment
Monitoring and continuous improvement
03 // THE RESULTS

Stock-outs fell 20% and waste 15% as forecasts got sharper and refreshed daily at store level.

Fuller shelves and less waste at the same time — that used to feel impossible.

Supply Chain Director · Grocery Retailer
// HOW WE'D DELIVER THIS TODAY

The AI services behind this outcome

A project like this draws on a focused set of Skillikz services — from first assessment to a working pilot and a clear path to scale.

// MORE WORK
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