Tag: R
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Factory Physics: The Laws Your Factory Floor Already Obeys
Throughput, WIP, and cycle time aren’t three dials you can set independently. They’re bound by physics, and ignoring that costs you weeks of lead time.
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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.