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
-
AI Model Distillation, Explained: The Compression Trick Behind the $589 Billion Monday
A single Chinese app knocked $589 billion off Nvidia in one day. The trick…
-
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…
-
Friction Is the Strategy: What Clausewitz Knew About Execution
Clausewitz’s 1832 concept of Reibung — friction — is not a complaint about execution. It is the structural diagnosis that strategy decks still refuse to internalise two centuries later. Any strategy that does not budget for friction is not a strategy; it is a wish.
-
Win Without Fighting: The Supreme Art in Business Strategy
Sun Tzu’s most cited principle is also his most misread. Winning without fighting is not pacifism — it is raising the cost of opposition so high that conflict becomes irrational for the adversary.
-
Shaping the Battlefield: Sun Tzu on Positioning Before Competition
Sun Tzu’s most underappreciated concept is shih (势) — the potential energy of a chosen position. The decisive act in strategy isn’t the fight, it’s the positioning that makes the fight unwinnable for the other side.
-
Know Before You Move: Sun Tzu’s Intelligence Doctrine
Sun Tzu’s Chapter XIII is a 2,500-year-old empirical claim: foreknowledge cannot be deduced from inside the room. Most companies still get this wrong — and the failure rates are measurable.
-
30 Books Every Forecaster, Demand Planner, and S&OP Lead Should Read — The Inphronesys Bookshelf
An interactive 30-book bookshelf for forecasters, demand planners, S&OP leads, supply chain strategists, and the data scientists who keep them honest. Filter by category and level, build a reading list for your role.
-
The Folly of Forecasting
From the Pythia at Delphi to Google’s GraphCast — humans have always demanded the future. Why some forecasts have gotten dramatically better, why others haven’t, and why AI will not deliver the silver bullet.
-
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.
-
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.