InPhroNeSys
Where Supply Chain Meets Data Science.
Real-world methods for procurement, inventory, and operations professionals who want to go beyond Excel — with reproducible code, realistic datasets, and techniques you can apply today.
Newest Entries
-
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…
-
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…
AI learning AI newsletters AI podcasts Anthropic Academy ARIMA bullwhip effect bupaR Clauders DDMRP demand-driven demand amplification demand forecasting economic order quantity EOQ ETS forecast accuracy Forecasting fpp3 free AI courses Google AI incremental cost inventory inventory cost inventory management machine-learning manufacturing MRP OpenAI Academy order quantity optimization process-mining procurement pull-system quantity discounts R replenishment R programming seasonality Shiny STL decomposition supply-chain supply chain AI supply chain analytics supply chain simulation time series analysis variance analysis
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.