CART® Regression for Predict Stock Rotation

Use CART® Regression to use complex relationships with multiple predictors to predict stock rotation time.

This example applies to the Supply Chain Module. For more information, go to www.minitab.com/supply-chain-module.

Example

Stock rotation is the number of days until the inventory stock is depleted. To calculate stock rotation, first divide the average inventory value by the total sales for the same time period. Then multiply this ratio by the number of days in the time period.

In this worksheet, Turnover Days is the response. Sales Promotions, % Price Reduction, and % Customer Returns are the continuous variables. Item, Collection, Department, and Size are the categorical variables. The predictors may explain differences in stock rotation time.

C1	C2	C2	C2	C5-T	C6-T	C7-T	C8-T
`Turnover Days`	`Sales Promotions`	`% Price Reduction`	`% Customer Returns`	`Item`	`Collection`	`Department`	`Size`
43.8	4	15	25.0	Jeans	Classic	Petite	Extra Small
41.5	1	15	22.5	Pants	Modern	Plus	Medium
63.1	6	10	17.6	Shorts	Athletic	Plus	Extra Large
29.9	6	20	12.9	Skirts	Vintage	Tall	Small

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Inventory, select Stock rotation.
Select Predict stock rotation, then click OK.
Select CART® Regression, then click OK.
In Responses, enter the column that contains the stock rotation data. The response is also called the Y variable.
In Continuous predictors, enter the columns of numeric data that may explain or predict changes in stock rotation time. The predictors are also called X variables.
In Categorical predictors, enter the categorical classifications or group assignments that may explain or predict changes in stock rotation time. The predictors are also called X variables.
Click OK.

Tip

For more information about this analysis, click Help in the main dialog box.