Machine-learning demand forecasting for a retailer
How a retailer could combine sales, promotions and seasonality data to forecast demand by store and SKU.
The situation
A regional retailer forecasts demand in spreadsheets using last year’s sales. Popular items run out during promotions while slow movers tie up cash and shelf space.
Our approach
Unify the data
Bring point-of-sale, inventory, promotions, pricing and calendar data into a cloud data platform.
Build forecasting models
Train models that account for seasonality, promotions, holidays and store differences.
Integrate with replenishment
Feed forecasts into ordering, with planners able to review and override.
Monitor and retrain
Track forecast accuracy and retrain automatically as patterns change.
Expand
Extend to markdown optimisation and new-store planning once accuracy is proven.
Typical technology
What success looks like
Metrics we would agree with you up front and track throughout:
- Forecast accuracy by category
- Stock-out rate on promoted items
- Inventory days on hand
- Waste and markdowns
- Planner time per week
This is an illustrative scenario showing how Inspired Infotech approaches this type of problem. It does not describe a specific client, and actual approach and outcomes depend on each organisation’s systems, data and goals.
Services behind this scenario
AI & Machine Learning
Predictive models, computer vision and NLP built on your data and taken all the way to production.
Learn moreGoogle Cloud
Data, analytics, Kubernetes and Gemini-powered AI on Google Cloud Platform.
Learn moreAWS Cloud Services
Migration, modernization, serverless, data and GenAI on Amazon Web Services.
Learn moreFacing a similar challenge?
Let’s talk about your situation and what a realistic plan looks like.