CART® Regression for Predict Supply Chain Cycle Time

Use CART® Regression to use complex relationships with multiple predictors to predict supply chain cycle time.

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

Example

Supply chain cycle time is the length of time to complete an order if inventory levels are zero. Supply chain cycle time includes the amount of time to order, fulfill, and receive the order.

In this worksheet, Cycle Time is the response. Order Time, Manufacturing Time, and Number of Suppliers are the continuous variables. Incentive Plan, Standard Parts, and Supplier Location are the categorical variables. The predictors may explain differences in supply chain cycle time.

C1	C2	C3	C4	C5-T	C3-T	C3-T
`Cycle Time`	`Order Time`	`Manufacturing Time`	`Number of Suppliers`	`Incentive Plan`	`Standard Parts`	`Supplier Location`
52	18	4	8	Standard	Yes	Domestic
37	12	5	4	Gold	Yes	Domestic
56	16	8	12	Standard	Yes	Foreign
49	15	1	2	Gold	No	Foreign

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Cycle Time, select Supply chain cycle time.
Select Predict supply chain cycle time, then click OK.
Select Fit Regression Model, 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 supply chain 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 supply chain 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.