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
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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…
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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…
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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…
ARIMA bottleneck-analysis buffer-management bullwhip effect bupaR centrality data management data quality DDMRP demand-driven demand amplification demand forecasting efficiency ERP ETS forecast accuracy Forecasting fpp3 graph-theory igraph inventory inventory management lean machine-learning manufacturing master data MRP network-analysis operations-management process-mining pull-system R replenishment resilience R programming seasonality Shiny STL decomposition supply-chain supply-chain-risk supply chain analytics supply chain simulation time series analysis variance analysis visualization
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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.
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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.
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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.