The three principles of modern demand forecasting, and the five data types needed for a best-in-class forecast
No matter what types of products you make or which technologies you use, a best-in-class forecast is based on one thing: unconstrained demand, or the amount that you would have sold if the product were always available on the shelf (physical or virtual), when and where a consumer wanted to buy it.
AI and machine learning, causal or seasonal factors, and more certainly help get you closer to a more accurate forecast, but they’re aren’t the biggest difference makers.
In The No-Nonsense Guide to Forecasting, we’ll dive into the three most important factors in building a more accurate forecasting process, including:
New feature in Alloy.ai helps consumer brands stay in-stock by calculating and auto-generating store-specific orders via Walmart NOVA.
Visit siteCascades uses Alloy.ai to connect and bring in data from more than 20,000 retail stores to power their demand planning, supply chain decisions and more.
ViewBy integrating retailer sales and inventory data into SAP IBP, Alloy.ai and Westernacher help CPGs improve shipment forecast accuracy and achieve better inventory planning through...
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