Kategorie: Supply Chain Management
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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.
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Your Line Chart Is Hiding 8 Patterns: How to Find Them with fpp3
Four datasets. Identical statistics. Completely different shapes. If you’re not plotting your demand data before forecasting it, you’re flying blind — and fpp3 gives you the visual toolkit to see what your spreadsheet hides.
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Your First Forecast in 15 Minutes: A Supply Chain Pro’s Guide to R, RStudio & FPP3
You convinced your boss R is better than Excel. Now what? Install it, load fpp3, and produce your first real forecast — all before your coffee gets cold.
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Stop Forecasting in Excel: Why R Is the Only Serious Tool for Supply Chain Demand Planning
Excel can’t do seasonality, model comparison, or prediction intervals without heroic effort — R does all three in six lines of code. Here’s why April is the month you finally make the switch.
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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…
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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.
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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.
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Prophet: The Forecasting Tool That Actually Makes Sense to Non-Statisticians
Meta’s Prophet gives supply chain teams accurate demand forecasts without requiring a statistics degree — here’s how it works, where it shines, and where it doesn’t.
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When S&OP Fails: A Data-Driven Survival Guide for Production Planners
Only 15% of companies run S&OP successfully. For the other 85%, here’s a data-driven toolkit that lets production planners bypass the broken process.
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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.
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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.