A leading consumer goods companies with brands in hair care, skin care, edible oils, health foods, male grooming, and fabric care categories was achieving low accuracy in forecasting demand across its product portfolio.
Client had developed simple rules-based demand forecasting models with less than 70% accuracy on average resulting in:
❖ Frequent stock outs of some SKUs during high demand months
❖ Excess inventory of SKUs having low demand
Client wanted to build predictive models that forecast sales of different SKUs across all its brands and in top sales areas at a retailer level to optimize inventory.