CART® Regression for Predict Customer Order Cycle Time

Use CART® Regression to use complex relationships with multiple predictors to predict customer order cycle time.

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

Example

Customer order cycle time is the number of days between a customer order and delivery of the product.

In this worksheet, Order Time is the response. Number of Orders, Number of Items, and Distance from Center are the continuous variables. Product, Quarter, and Region are the categorical variables. The predictors may explain differences in customer cycle time.

C1	C2	C3	C4	C5-T	C6-T	C7-T
`Order Time`	`Number of Orders`	`Number of Items`	`Distance from Center`	`Product`	`Quarter`	`Region`
18	6	1500	428	Jigsaw	Q1	Northwest
12	6	675	315	Drill	Q1	Northwest
16	12	2075	200	Jigsaw	Q1	Midwest
15	7	438	189	Drill	Q2	Midwest

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Cycle Time, select Customer order cycle time.
Select Predict customer order cycle time, then click OK.
Select CART® Regression, then click OK.
In Responses, enter the column that contains the cycle time 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 customer cycle 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 customer cycle time. The predictors are also called X variables.
Click OK.

Tip

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