Tag: R
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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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Six Times They Tried to Prove Sophistication Wins
Forty years of open forecasting competitions, and the winner’s margin over a dumb average stayed thin. Then M6 asked whether accuracy makes money. The correlation was 0.04.
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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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Your Supplier Isn’t Offering a Discount. They’re Offering You 29.8%
The price-break EOQ math says buy 600 units instead of 87 and save $6,566 a year. Then a carrying rate above 29.8% flips the answer. Here are the three numbers no supplier quote contains.
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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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I Let AI Agents Play a 5-Year Beer Game: Why Sharing Data Beat Buying a Smarter Model
I ran the classic Beer Game for 260 weeks with AI agents in the chairs. Giving the chain real customer demand cut in-game cost by up to 40 percent, far more than paying for a smarter model did.
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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.