Supply Chain Consumer Goods Distributor
Supply Chain: AI Demand Forecasting Cuts Overstock by 35%
A consumer goods distributor reduced overstock and stockouts by 35% using ML-based demand forecasting.
The Problem
A distributor of consumer goods struggled with inventory planning. Historical spreadsheets and gut feel led to frequent overstock (tying up capital) and stockouts (lost sales). Seasonal spikes and promotional events made planning even harder. The team spent 20+ hours per week on manual forecasts.
The Solution
We implemented an ML-based demand forecasting system that:
- Ingests historical sales, promotions, and external signals (weather, holidays, events)
- Trains per-SKU models that adapt to each product’s demand pattern
- Produces weekly forecasts with confidence intervals for inventory planning
- Integrates with the ERP to auto-adjust reorder points and safety stock
The system uses a combination of time-series models and gradient boosting, with human override for known one-off events (e.g., a major new customer launch).
The Outcome
- 35% reduction in overstock and stockout incidents
- 20 hours/week saved on manual forecasting—reallocated to supplier relationships and strategy
- Improved fill rates during peak seasons thanks to earlier, data-driven ordering
- Payback in 6 months from reduced carrying costs and fewer lost sales
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