POS Forecasts

Fill retail forecasting gaps with 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
  • 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.

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Frequently Asked Questions

Alloy.ai connects to retail data portals and imports POS data in real time, producing short- and long-term demand sensing forecasts at the SKU/store/channel/day level of granularity. This detailed forecasting enables brands to identify supply shortages and sales opportunities across various channels.
Alloy.ai provides four forecasting models: Historical Average- Calculates the average of recent sales over a specified historical period. Annual Growth- Projects future demand by following the previous year’s demand patterns at a specified growth rate. GAM (Generalized Additive Model)- A machine learning model that evaluates year-on-year trends, seasonality, holidays, and errors to forecast future patterns. Seasonal Historical Average- Considers historical sales data with a focus on seasonal variations to predict future demand.
Brands can compare their sell-in plans to actual sell-through, even if retailers don’t provide a POS forecast. Alloy.ai’s forecasts can be exported into planning systems to enhance shipment forecast accuracy. Additionally, the platform provides order recommendations and calculates weeks-of-supply based on future demand versus historical sales, aiding in proactive inventory management and reducing the risk of stockouts or overstock situations.