Tag: supply chain analytics
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Your Demand Chart Hid a 132-Unit Problem. The FT’s Chart Rules Catch It
Four datasets can share the same summary statistics and still tell four different stories. So can your SKU history. Here is how to name the relationship in your data, pick the chart that fits it, and spot the trend break your eye keeps missing.
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What Shape Is Your Plant? In Two of the Four, Your Bottleneck Lies
Every factory game teaches you the slowest machine sets the pace. None teach you that in a V-plant, the trunk can run at 100% and convert every unit it should, while the customer still goes short.
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Four Kinds of Weird in Your Price File: EUR 43,017 of Negotiating Room
A supplier asked for 20.4%. The published indices supported 2.3%. Here is the R workbench that found the gap, and the three things it honestly could not find.
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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.
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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.
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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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Taking the Engine Apart: Time Series Decomposition for Supply Chain Forecasters
Every time series is a cocktail of trend, seasonality, and noise. Decomposition is how you separate the ingredients — and once you can see each one, choosing the right forecast model stops being a guessing game.