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:
Here's everything we know about Walmart Scintilla, the rebranded data platform offering real-time insights, improved collaboration, and actionable analytics for suppliers.
Keep readingEdgewell used Alloy.ai to optimize inventory, resolve phantom stock issues, and drive $4M in new sales, transforming data into actionable supply chain insights.
ViewNew feature in Alloy.ai helps consumer brands stay in-stock by calculating and auto-generating store-specific orders via Walmart NOVA.
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