Where SCM meets data science and AI.
Real-world methods for supply chain and operations management professionals who want to go beyond Excel — with reproducible code, realistic datasets, and techniques you can apply today.
Newest Entries
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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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S&OP: Everyone Signed Off on the Number. It Was Still Wrong by 8.2%.
A consensus forecast measures agreement, not truth. Here is what a textbook S&OP cycle…
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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…
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The $50,000 Prompt: How McKinsey Frameworks Turn AI Into Your Best Supply Chain Consultant
Most supply chain managers ask AI vague questions and get vague answers. McKinsey consultants have spent 100 years perfecting structured thinking frameworks — and those same frameworks transform AI prompts from mediocre to boardroom-ready. Here are 6 frameworks, 12 before/after examples, and the data to prove it.
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Stop the Madness: Why Your MRP Keeps Changing Its Mind (and How Time Fences Fix It)
Your MRP system reschedules 300 orders before lunch. Your shop floor ignores half of them. Your suppliers stopped trusting your forecasts two quarters ago. Time fences are the fix — and the math shows exactly where to set them.
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The $2,700 Post-It Note: How a 1913 Formula Still Beats Your ERP
A procurement manager discovers she’s been wasting $2,700 per year on a single component — and the fix fits on a Post-it note. We use R to show why a 1913 formula still outperforms gut-feel ordering, when it breaks, and what to do about it.
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Quantity Discount Analysis: The Hidden Trap in Supplier Pricing That Most Buyers Miss
A supplier offers lower prices for larger orders — sounds great, right? Quantity Discount Analysis reveals that many discount schedules actually charge you more per incremental unit as volumes rise. Learn to spot this hidden trap with R.
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The Bullwhip Effect: Why a 10% Demand Blip Becomes a 400% Supply Chain Earthquake
A small wobble in customer demand can snowball into chaos upstream. We quantify the bullwhip effect with R, simulate a 4-tier supply chain, and show why cutting lead time is the single most powerful lever you have.
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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.
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How Dynamic Buffer Management Works in DDMRP
DDMRP replaces static safety stock with dynamic, color-coded buffers that expand and contract with actual demand. This post explains the math, the logic, and the R code behind buffer sizing, net flow position, and demand-driven replenishment.