
Explore ABC segmentation in supply chain analytics, classifying SKUs into A (80%), B (15%), and C (5%) to optimize inventory and service levels, with noted limitations.
Apply abc segmentation analysis in Python on 2015 sales value using pandas, numpy, and plotly to rank items and summarize results by 80/15/5 thresholds.
Combine ABC and XYZ analyses to segment SKUs by sales value and demand fluctuation using the coefficient of deviation, forming nine meaningful categories for strategic planning.
Learn to prepare data for XYZ analysis by splitting by store, creating weekly time-series, and computing mean, std dev, and coefficient of variation, then combine with ABC analysis.
Perform xyz analysis by computing mean, standard deviation, and coefficient of deviation, categorize items into X, Y, Z by CV, and visualize with a mean-std scatter colored by category.
Integrate abc and xyz analyses in python by building a combined abc-xyz function that processes data by store and returns a segmented abc-xyz output, with transformation and export to excel.
Visualize supply chain segmentation results with bar and scatter plots using Plotly Express, comparing categories A, B, and C and exploring demand patterns with sample items and mean lines.
Supply chain problems are complex due to large number of SKU's, Customers and Suppliers, as a first step in any supply chain analytics project we need to reduce the complexity of the problem. Supply chain segmentation is a fundamental first step that enables us to reduce the supply chain complexity. Once we perform the supply chain segmentation, we can easily start choosing the proper demand forecasting model, can optimize the inventory, or can implement any kind of strategy based on the segments.
In this course we learn the most commonly used ABC and XYZ segmentation analysis, as well as we combine the both. The key advantage of learning this course is at the end of the lesson you will have a complete code to perform ABC, XYZ analysis on real life use cases with multiple stores and more than 3000 skus.
In addition to building the segmentation algorithms we will implement interactive visualizations in python, so you can present and explain your analysis in a user friendly manner to your manager or to your client.
This course will serve as a first building block for your supply chain analytics project. If you are a supply chain professional, after learning in this course you can use your company data to implement the ABC, XYZ segmentation analysis to derive insights for your organization. If you are a supply chain student you can use this knowledge to understand much deeper topics as well as can build your portfolio of supply chain analytics projects.