Schlagwort: R
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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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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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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.
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Don’t Push Your Suppliers — Pull Them!
How implementing Demand Driven MRP (DDMRP) transformed supplier lead times, reduced on-hand inventory, and brought lean pull principles to life in our supply chain.
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Advantages of R and Python over Excel
Twelve compelling reasons why R and Python outperform Excel for data analysis — and practical advice on making the transition from spreadsheets to code-based analytics.
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Data Quality Assessment for ERP Systems
Master data quality is the foundation of every ERP system. Learn how to systematically assess and visualize data gaps using R before they undermine your operations.
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Sales Data Visualization: Beyond Pie Charts
Move beyond pie charts to more effective visualizations for sales data — waffle charts for proportions, seasonal decomposition for patterns, and interactive dashboards for forecasting.
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Network Analysis for Supply Chain Risk and Resilience
Your supply chain is a network. Graph theory and R’s igraph package reveal which nodes are critical, where single points of failure hide, and how disruptions propagate — before they happen.
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bupaR: The Process Mining Toolkit That Shows You How Your Factory Actually Runs
Your factory has a designed process and an actual process. bupaR — the open-source process mining suite for R — shows you the difference, and that difference is where your efficiency gains are hiding.
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Process Mining a Mobile Phone Assembly Line with bupaR
Using R’s bupaR ecosystem to analyze a smartphone assembly process — from creating event logs to discovering bottlenecks, rework patterns, and resource utilization.
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Five Data Science Capabilities That Transform Supply Chain Operations
Five concrete data science capabilities — from demand forecasting to anomaly detection — that deliver measurable improvements in supply chain planning, procurement, and logistics.