Tag: demand planning
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I Raced Amazon’s Forecasting AI Against a 66-Year-Old Formula. The Formula Won.
I put Amazon’s zero-shot forecasting AI against five classical models on 148 real retail series. A formula from 1960 beat a 205-million-parameter model by 14 to 16%.
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Your Exception Report Is Lying to You: How to Make ‘1% False Alarms’ Actually Mean 1%
A static ±3σ band promised 0.27% false alarms and delivered 3.80%. Here is how to turn your exception threshold from a statistics setting into an honest attention budget.
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S&OP: Everyone Signed Off on the Number. It Was Still Wrong by 8.2%.
A consensus forecast measures agreement, not truth. Here is what a textbook S&OP cycle looks like, why the signed-off number still ran 8.2% high, and the four moves that fix it without a full process overhaul.
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Does Your Forecast Beat a Sticky Note? The Placebo Test for Demand Planning
Your forecast has exactly one job: beat a sticky note that says ‘same as last year.’ Most don’t. Here’s the placebo test that proves it.
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I Gave an AI Agent the Reorder Button: It Rebuilt the Bullwhip in 250 Days
An AI agent with the reorder button hit ~100% fill rate and looked like a star. Then I measured what it dumped on its suppliers.
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30 Books Every Forecaster, Demand Planner, and S&OP Lead Should Read — The Inphronesys Bookshelf
An interactive 30-book bookshelf for forecasters, demand planners, S&OP leads, supply chain strategists, and the data scientists who keep them honest. Filter by category and level, build a reading list for your role.
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The 20 Most Influential People in Forecasting (And What to Learn From Each)
A TIME 100-style guide to the academics, ML engineers, and supply chain voices who shaped modern forecasting — and the one resource to start with for each.
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When the Algorithm Is Wrong and the Expert Is Right
Statistical models don’t know about your supplier’s factory fire, your competitor’s clearance sale, or the regulation that just changed. Here’s when expert judgment beats the algorithm — and the biases that make it worse.