Demand forecasting for a multi-branch retail operation
How a mid-size Saudi retail chain could transform inventory management using machine learning.
What this produces
- Category-level forecasts that account for Ramadan and school-year seasonality
- Reorder points the buying team sets, sees and owns
- A cleaned POS and supplier lead-time dataset
- Alerts before a category runs short or long
The Challenge
A 12-branch retail chain in Riyadh was sitting on months of excess inventory in some categories while frequently stocking out in others. Manual forecasting was slow, seasonal patterns were being missed, and shrinkage from expired stock was eroding margins.
Our Approach
Data Audit & Cleaning
We audit 3 years of POS data, supplier lead times, and seasonal sales patterns. We identify and clean data quality issues before any model training begins.
Demand Forecasting Model
We build a category-level ML model that accounts for Ramadan demand spikes, school-year seasonality, and local events — factors that generic forecasting tools miss.
ERP Integration
We connect the model output directly to the existing ERP (SAP or similar) so reorder suggestions are generated automatically without manual intervention.
Team Training
We train the buying team to interpret the model's confidence scores and override logic — ensuring the AI augments, not replaces, their expertise.
Technologies & Services
Facing a similar challenge?
Let's talk about how we can apply this approach to your business.