AI-Driven Demand Forecasting for Retail Expansion
- Machine Learning
- Demand Forecasting
- Retail
- Predictive Analytics
- Data Science
Objective
Replace manual, intuition-based volume planning for shelf-rental and convenience stores across Indonesia with machine-learning-driven predictions.
Features Highlight
Machine-Learning Forecasts
Predicts supply quantities from historical sales data, improving accuracy across roughly 500 stores previously planned in Excel.
Scales With Expansion
Supports business growth amid rapid store and regional expansion without adding planning headcount.
Less Manual Effort
Reduces dependency on individual intuition and frees teams from repetitive spreadsheet work.
About
Explore how data-driven forecasting turns guesswork into reliable, repeatable supply planning.
From Excel to ML
Consolidated fragmented spreadsheet planning into a single forecasting system across the store network.
Higher Precision
Improved forecast accuracy beyond what on-site experience alone could achieve.
Built to Scale
Designed to keep pace as the store network and regional coverage grow.
Operational Resilience
Reduces stockouts and overstock by grounding planning decisions in data.
