CART® Regression for Predict Total Supply Chain Management Cost As A Proportion Of Sales

Use CART® Regression to use complex relationships with multiple predictors to predict total supply chain management cost as a proportion of sales.

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

Example

Total supply chain management cost as a proportion of sales is the total supply chain costs for a specific time period divided by the total sales.

In this worksheet, Total Cost Proportion is the response. Number of Suppliers, Invoice Accuracy, and Number of Obsolete Items are the continuous variables. Product, Auto Purchase Order, Shipping, and Shipping Contract are the categorical variables. The predictors may explain differences in total cost as proportion of sales.

C1	C2	C3	C4	C5-T	C6-T	C7-T	C8-T
`Total Cost Proportion`	`Number of Suppliers`	`Invoice Accuracy`	`Number of Obsolete Items`	`Product`	`Auto Purchase Order`	`Shipping`	`Shipping Contract`
0.154	6	0.84	1500	Jigsaw	No	Partial Truck	Yes
0.143	4	0.89	675	Drill	Yes	Full Truck	Yes
0.167	8	0.93	2075	Jigsaw	No	Partial Truck	No
0.139	6	0.89	438	Drill	No	Parcel	Yes

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Costs, select Cost as a proportion of sales.
Select Predict total supply chain management cost as a proportion of sales, then click OK.
Select CART® Regression, then click OK.
In Responses, enter the column that contains the proportions 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 cost as a proportion of sales. The predictors are also called X variables.
In Categorical predictors, enter the categorical classifications or group assignments that may explain or predict changes in cost as a proportion of sales. The predictors are also called X variables.
Click OK.

Tip

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