So I need to make an XYZ analyse and After calculation ratio of variation of quantity of goods a very large spread was obtained. And now I don’t understand which scheme to XYZ need to do.
I’m working with an e-commerce dataset similar to the “Marketing Insights for E-Commerce Company” on Kaggle. I’m performing XYZ analysis, and after calculating the coefficient of variation (CoV) for product quantities, I’ve observed a very wide range of CoV values. This makes it challenging to determine appropriate thresholds for classifying products into X, Y, and Z categories. Could anyone share advice on how to handle this wide variation in CoV for effective XYZ analysis in Knime? Are there specific nodes or workflows that could be helpful? I’m particularly interested in strategies for setting dynamic or data-driven thresholds for the XYZ classification.
Hi @Vira_Maykova , as this is a totally new question, I’ve moved it to a new topic, where hopefully somebody will be able to assist you.
Please always create a new topic for a new question. This ensures that the posts within an existing topic remains “on topic” but also means that your new question will get wider visibility for people with the relevant expertise, because it has a title that is aligned to the question being asked. thanks.
I’m not an expert in XYZ Analysis. A number or sources pretty consistently use the following classification: 5. Classify the Product Assortment into Three Categories – X, Y, or Z:
X - Products with a coefficient of variation between 0-10% represent items with the most consistent demand.
Y - Products with a coefficient of variation between 10-25% denote items with variable sales volumes.
Z - Products with a coefficient of variation exceeding 25% are items with sporadic demand.