Kategorie: Data Science
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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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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.