[ RETAIL & FMCG ]
Demand Forecasting and Inventory Optimiser
Hierarchical SKU-and-store forecasting with automatic model selection. Procurement teams get a 12-month forward view that survives the long tail.
Domain
RETAIL & FMCG
Status
Delivered across multiple FMCG engagements
Use when
Retailers or FMCG groups with thousands of SKUs need to retire gut-feel procurement.
Reference
A regional FMCG retail group
01. The problem
Demand patterns vary wildly across thousands of SKUs. Seasonal products, promotional spikes, new launches, and regional variation defeat a single forecasting approach.
02. The approach
Hierarchical forecasting with a LightGBM and Prophet ensemble. Each SKU-store combination trains independently. The framework picks the better-performing model on rolling backtests.
03. The architecture
Python pipelines orchestrated by Apache Airflow into Snowflake. Model selection, backtest, and forecast generation run on the same monthly cadence. A Tableau exception dashboard surfaces SKUs whose actuals diverge from the procurement plan.
04. The outcome
A defensible, auditable forecast for every SKU in the range, with model selection rationale traceable per item. Buyers stop forecasting; they react to exceptions.
[ ATI SHAPE ]
Predictive intelligence
LightGBM + Prophet ensemble per SKU-store
Agentic execution
Exception alerter that escalates only divergent SKUs
Secure data
Pipelines live inside the customer warehouse
Actionable outcomes
Monthly procurement decisions backed by per-item forecasts
[ IMPACT ]
Per-SKU
Model selection across the catalogue
12 months
Forward forecast horizon
Exception-driven
Buyers focus only on divergent SKUs
[ NEXT STEP ]
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