CART® Regression for Predict Supply Chain Cost Per Unit

Use CART® Regression to use complex relationships with multiple predictors to predict supply chain cost per unit.

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

Example

Supply chain cost per unit is the total supply chain costs divided by the number of units sold.

In this worksheet, Supply Chain Cost/Unit ($) is the response. Sales Promotions, 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 supply chain cost per unit.

C1	C2	C3	C4	C5-T	C6-T	C7-T	C8-T
`Supply Chain Cost/Unit ($)`	`Sales Promotions`	`Invoice Accuracy`	`Number of Obsolete Items`	`Product`	`Auto Purchase Order`	`Shipping`	`Shipping Contract`
3.90	4	0.84	1500	Jigsaw	No	Partial Truck	Yes
2.56	1	0.89	675	Drill	Yes	Full Truck	Yes
4.70	6	0.93	2075	Jigsaw	No	Partial Truck	No
3.49	6	0.89	438	Drill	No	Parcel	Yes

How-to

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

Tip

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