Demand Forecasting for a Multi-Branch Retail Chain
Fixed stock-outs and overstock by deploying store-level demand intelligence.
Demand Forecasting for a Multi-Branch Retail Chain
Fixed stock-outs and overstock by deploying store-level demand intelligence.
Founder note: this is written to be practical. We focus on what we learned in delivery, what changed for the team, and what results held up after rollout.
The Real Problem
Inventory plans were based on monthly averages and manager intuition, which caused repeated stock-outs in fast-moving categories and dead stock in seasonal lines.
What We Actually Changed
Built a store-level demand forecasting model using 18 months of transaction data, weather signals, and promotional calendars — feeding automated replenishment recommendations.
TOOLS AND METHODS USED
Delivery Journey
Data Pipeline
Transaction ingestion, feature store
Model Development
Store-level forecasting models
Deployment
Replenishment dashboards, rollout
What Improved
Store managers finally trust the numbers. We stock what sells and stop bleeding margin on dead inventory.
FOUNDER BRIEF
DELIVERY STACK
WANT A SIMILAR OUTCOME?
We can map your opportunity and constraints before you commit budget.
Request free feasibility studyarrow_forward