Tag: supply chain analytics
-
I Scored 10 Fortune 500 Giants as Suppliers: Wall Street Would Not Agree
I applied a multi-dimensional supplier risk framework to 10 of the largest Fortune 500 companies — using real public data. The company with the best credit rating scored as the riskiest supplier. Your neighborhood pharmacy landed at number two. Here’s every score, every data source, and the R code to run it on your own…
-
Where Should Your Warehouse Be? 12 Lines of R Code Have the Answer
Most companies pick warehouse locations based on gut feel, real estate deals, or where the CEO lives. The optimal location — the one that minimizes total weighted transport cost — can be found with 12 lines of R code. The answer is rarely where you think.
-
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.
-
The Experience Curve: The Most Powerful Cost Model You’re Probably Not Using
Every time cumulative production doubles, costs fall 20-30%. Here’s how to fit experience curves with R and use them for supplier negotiations, cost forecasting, and strategic sourcing.
-
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.
-
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.
-
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.