[ RETAIL & FMCG ]
Demand Forecasting and Inventory Optimiser
Procurement teams across thousands of SKUs cannot forecast each one by gut. Single-model approaches break on the long tail. We built a hierarchical forecasting system that picks the best model per SKU-store combination automatically, runs 12-month rolling forecasts on a scheduled pipeline, and surfaces exception alerts to buyers when reality diverges from the procurement plan.
Client
A regional FMCG retail group
Timeline
Multi-month delivery
Role
Hierarchical demand forecasting
Team
Pod: ML + data engineering
Year
2024
Industry
Retail and FMCG
01. The Challenge
Demand patterns vary wildly across thousands of SKUs. Seasonal products, promotional spikes, new launches, and regional variation defeat a single forecasting approach.
Procurement decisions are made weeks in advance with limited visibility. The result is persistent stockouts on top movers and overstocking on slow sellers.
02. The Solution
Built a hierarchical forecasting system with a LightGBM and Prophet ensemble. Each SKU-store combination trains independently and the framework picks the better-performing model on rolling backtests.
Orchestrated 12-month rolling forecasts via Apache Airflow into Snowflake, with a Tableau exception dashboard that surfaces the SKUs where actual demand has diverged enough from the procurement plan to warrant attention.
03. The Outcome
A working monthly procurement cycle grounded in forecast outputs, not gut feel.
A defensible, auditable forecast for every SKU in the range, with model selection rationale traceable per item.
An exception-driven workflow that focuses buyer attention on the SKUs that need it.
01 · Context
02 · Architecture03 · Outcome[ Impact ]
Per-SKU
Model selection across the catalogue
12 months
Forward forecast horizon
Exception-driven
Buyers alerted only on divergence
Hierarchical
Roll-up by store, region, and category
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