RightChain.ai Plotting

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3.2 Clustering and Classification Clustering parameters can be used to create clustered groups using the “Bases” and “# of Clusters” provided. The resulting k-means clusters can be visually assessed by changing the “Color Basis” plot control to be “Cluster”. Some example uses of clustering include creating ABCD SKUs based upon their picking frequency and volume, creating “Very Heavy”, “Heavy”, “Medium”, and “Light” transportation route days based upon the number of stops, time per stop, and distance between stops; or identifying SKUs with “Very Problematic Inventory”, “Problematic Inventory”, and “Non Problematic Inventory” based upon a combination of $s, days, and cube on hand.

3.3 Plot Properties (such as Marker Color, Shape, and Size) Multiple properties of how the plots looks are customizable and include everything from the type of marker for each data point to the size and color scheme of those markers. Colors can be based on specific metrics or dimensions as well as the cluster groups. 3.4 Labels and Statistics Plot markers can also optionally display with text labels of their cluster number or other column data. Plot statistics set in the sidebar determine what stats will be shown in the statistics table or drawn on the plot if it is an XY plot. For XY plots where both axes are metrics, stat lines are drawn for just the data in view and reported below the plot just for that filtered subset, and they’re updated as you pan around the plot or zoom in on plotted data.

RightChain™ Plots

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