POS Forecasts
Fill retail forecasting gaps with Alloy.ai's accurate sell-through forecasts
POS Forecasts in Alloy.ai
No retailer sell-through forecast? No problem. Alloy.ai offers demand planners and sales teams POS forecasts calculated down the the SKU/store level — updated weekly. Alloy.ai connects to your retail data portals and imports POS data in real time, then lets you easily toggle between different POS forecasting models in to identify supply shortages and sales opportunities
Benefits of Alloy.ai POS Forecasts for Consumer Brands
- Detailed, cross-channel POS forecasts - Alloy.ai produces short- and long-term demand sensing POS forecasts by connecting to your POS data, then incorporating SKU-level trends and seasonality at the SKU/store/channel/day level of granularity.
- Sell-in vs sell-thru forecasts - Compare your sell-in plans to actual sell-through, even if your retailers don't provide a POS forecast.
- Export POS forecasts - Alloy.ai POS forecasts can be exported into planning systems to improve the accuracy of your shipment forecast.
- Order recommendations and weeks-of-supply - Alloy.ai leverages the POS forecast for recommending incremental orders to your retail partners as well as calculating weeks-of-supply based on future demand vs historical sales.
- As accurate as top retailers - We've benchmarked our POS forecasts against those from top retailers and found that Alloy.ai forecasts perform nearly as accurately or better.
- Four forecasting models available - Historical Average, Annual Growth, GAM and Seasonal Historical Average models are available to give you greater accuracy.
- Annual Growth – A forecast that follows last year’s demand patterns at a specified growth rate.
- Historical Average – The average of recent sales, for a specified historical period.
- GAM – A machine learning model that simultaneously evaluates year-on-year trends, seasonality, holidays and error to forecast future patterns.
- Seasonal Historical Average – An Alloy.ai proprietary model built specifically for products with high seasonality. The model accounts for recent sales volume and seasonal patterns over multiple years.
35% YoY Forecast Accuracy Improvement at Ember with Alloy.ai
Alloy.ai’s software also helps Ember drive inventory efficiency, scale its access to retail point-of-sale data, and align internal functions around a single source of truth for sales, inventory and demand planning.
Purpose-built for consumer goods brands
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