Schlagwort: ETS
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The M5 Lesson: Why Simple Still Beats Fancy in Supply Chain Forecasting
The 2020 M5 competition taught a lesson the forecasting world keeps forgetting: on real supply chain data, simple models win more often than you’d think. Here’s what the Walmart SKU benchmark actually showed — and why it matters for today’s Time Series Foundation Model hype.
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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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Three Equations From the Navy in 1957: Why Holt-Winters Still Runs Your Forecast Engine
Holt-Winters wasn’t born in a statistics lecture — it was written for the U.S. Navy in 1957 to solve an inventory problem. Nearly seven decades later, three recursive equations still beat most of the software sitting on top of your ERP.
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FPP3: Stop Guessing Which Forecast Model Works — Measure It
Rob Hyndman’s fpp3 ecosystem lets you fit, compare, and evaluate multiple forecasting models in three lines of R code — here’s why supply chain teams should stop fighting Excel and start using a real framework.
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When S&OP Fails: A Data-Driven Survival Guide for Production Planners
Only 15% of companies run S&OP successfully. For the other 85%, here’s a data-driven toolkit that lets production planners bypass the broken process.
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Time Series Analysis for Supply Chain Management: Reading the Rhythm of Demand
Your demand data is trying to tell you something. We use STL decomposition, seasonal diagnostics, and ETS/ARIMA models to extract trend, seasonality, and noise from ice cream sales data — then honestly discuss where these methods break down.