CART® Classification

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

Predict Perfect Order Delivery Rate

Use CART® Classification to use complex relationships with multiple predictors to predict perfect order delivery rate.

Example

Perfect order delivery rate is the proportion of orders that are error-free from beginning to end.

In this worksheet, Order Status is the response. The response event is Error-free. Number of Items is the continuous predictor, and Region, Carrier, and Freight Class are categorical predictors.

C1-T	C2	C3-T	C4-T	C5-T
`Order Status`	`Number of Items`	`Region`	`Carrier`	`Freight Class`
Error-free	5	Northwest	Speedy Shipping	Class 125
Error-free	4	Northwest	Global Delivery	Class 100
Error	2	Midwest	Ship-It-Fast	Class 175
Error	1	Midwest	Ship Now	Class 100

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Quality, select Perfect order delivery rate.
Select Predict perfect order delivery rate, then click OK.
Select CART® Classification, then click OK.
In Response, enter the binary variable that contains the error data. Binary variables are categorical variables that have two possible levels, such as pass/fail or true/false. The response is also called the Y variable.
In Response event, select the value that represents an order is error-free.
In Continuous predictors, enter the continuous variables that may explain or predict whether an order is error-free. The predictors are also called X variables.
In Categorical predictors, enter the categorical variables that may explain or predict whether an order is error-free. The predictors are also called X variables.
Click OK.

Tip

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