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Retail specific Machine Learning produces significantly more accurate Store/SKU/Day level forecasts (reducing excess inventory, reducing stock-outs) and touchless forecasting
About
Using Retail specific ML, the Rubikloud solution generates significantly more accurate Store/SKU/Day level forecasts (reducing excess inventory while at the same time also reducing stock-outs). Additionally, Rubikloud’s ML allows for forecast automation (using confidence indicators) reduces merchandiser/buyer planning effort by 50%+ (some Rubikloud clients now automate 90% of their entire SKU forecast – meaning merchandiser/buyer only adjust 10% of SKU forecasts, the rest goes straight into replenishment without human interaction). Rubikooud also uses ML to address brand NEW SKU's using advanced methods (Ontology, etc.) to forecast for new SKU's.
Key Benefits
Increase forecasting accuracy (WMAPE) Reduce Excess Inventory between 10-30% Reduce stock-outs by 30%+ Reduce Planner effort by 50% Automate the forecasting process by touchless forecasts Reduce Waste (Fresh food and other categories)
Applications
Our solution has been successfully implemented at numerous Groceries Retailers and Health & Beauty Retailers.