Tag: R
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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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Sole Source: The $900k Median Problem Your Dual-Source Checkbox Won’t Fix
The dual-source flag teams buy to feel safe moves the median cost of a disruption about $44k. In the wrong direction. The dependency nobody flags moves it $474k.
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The Resilience Ladder: Why the Things You Buy to Feel Safe Don’t Save You
I pulled 3,000 disruptions to find what separates firms that survive a shock from firms that bleed. The dual-source checkbox wasn’t 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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Why VAR Beat Google’s TimesFM — and How to Build One in R
A peer-reviewed 2025 study put Google’s TimesFM foundation model head-to-head with vector autoregression on real hospital data. Spoiler: the 1980s econometric model won. Here’s what VAR is, why it works for supply chain, and how to build one in R.
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Global Forecasting with XGBoost in R: A Walmart Weekly Walkthrough
A hands-on walkthrough of global XGBoost forecasting in R with tidymodels and modeltime, applied to the Walmart weekly sales dataset. What the feature importance reveals, when ML earns its complexity, and when ETS or SNAIVE quietly wins.
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I Ran 6 Models on Real Demand Data — Here’s How I Picked the Winner
Six forecasting models, one real demand series, one honest horse race. Here’s the model that won — and the metric that made the choice unambiguous.
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Is Your Forecast Any Good? The Forecaster’s Toolbox
Four acronyms decide whether you trust a forecast: MAE, MAPE, RMSE, MASE. Here is when each one lies to you — and the one benchmark that catches them all.