SYSTEMS AND METHODS FOR PRIME PRODUCT FORECASTING

• US 20150371242A1
• Filed: 06/23/2014
• Published: 12/24/2015
• Est. Priority Date: 06/23/2014
• Status: Abandoned Application

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: RAMASWAMY, Sridhar, RICHARDSON, Mark Carl, MASON, James Robert, GRAY, Kenneth Dale, TRIPATHI, Bhargvendra, NAM, Sangjeong
• Application Number:
  US14/311,682
• Publication Number:
  US 20150371242A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Systems and methods are disclosed for forecasting future sales of a prime product. The system includes at least one processor configured with instructions to collect historical sales data of the prime product, collect historical telematics data from one or more machines that were sold as the prime product, and collect historical econometric data relevant to the prime product. The at least one processor also generates a group of candidate predictors from the historical telematics data and the historical econometric data, select predictors from the group of candidate predictors, and establish a forecasting model representing a relationship between the selected predictors and the historical sales data of the prime product. The at least one processor forecasts future sales of the prime product by using the established forecasting model.

Claims

1. A computer system for forecasting future sales of a prime product, the computer system comprising:

at least one processor configured with instructions to;

collect historical sales data of the prime product;

collect historical telematics data from one or more machines that were sold as the prime product;

collect historical econometric data relevant to the prime product;

generate a group of candidate predictors from the historical telematics data and the historical econometric data;

select predictors from the group of candidate predictors;

establish a forecasting model representing a relationship between the selected predictors and the historical sales data of the prime product; and

forecast future sales of the prime product by using the established forecasting model.

2. The computer system of claim 1, wherein, in the step of generating the group of candidate predictors from the historical telematics data and the historical econometric data, the at least one processor is further configured to:

perform data cleansing on the historical telematics data and the historical econometric data.

3. The computer system of claim 2, wherein, in the step of generating the group of candidate predictors from the historical telematics data and the historical econometric data, the at least one processor is further configured to:

perform data validation on the cleansed data.

4. The computer system of claim 1, wherein, in the step of generating the group of candidate predictors from the historical telematics data and the historical econometric data, the at least one processor is further configured to:

create seasonality indices for prime product sales based on the historical sales data of the prime product; and

add the created seasonality indices into the group of candidate predictors.

5. The computer system of claim 1, wherein, in the step of generating the group of candidate predictors from the historical telematics data and the historical econometric data, the at least one processor is further configured to:

create leading predictors for each of the candidate predictors; and

add the created leading predictors into the group of candidate predictors.

6. The computer system of claim 1, wherein, in the step of selecting the predictors from the group of candidate predictors, the at least one processor is configured to:

remove highly correlated candidate predictors;

perform stepwise regression analysis on the candidate predictors;

perform best subsets analysis on the candidate predictors; and

perform variance influence factor analysis on the candidate predictors.

7. The computer system of claim 1, wherein, in the step of establishing the forecasting model, the at least one processor is configured to:

generate a plurality of candidate forecasting models based on the selected predictors by performing a Lazy Evaluation Algorithm for Production Systems (LEAPS) algorithm; and

select the forecasting model from the plurality of candidate forecasting model based on at least one of a probability value, a fixation index, or a residual standard error of each candidate forecasting model.

8. The computer system of claim 1, wherein, in the step of forecasting sales of the prime product, the at least one processor is configured to:

forecast future data of each predictor included the forecasting model; and

forecast the future sales by using the established forecasting model based on the future data of each predictor.

9. The computer system of claim 1, wherein the historical telematics data includes at least one of service meter hours, idle hours, working hours, service meter hours per gallon of fuel, or number of machines reporting telematics data.

10. The computer system of claim 1, wherein the historical econometric data includes at least one of an industrial production index, a construction indictor, or an econometric indictor.

11. A method for forecasting future sales of a prime product, the method comprising the following operations performed by at least one processor:

collecting historical sales data of the prime product;

collecting historical telematics data from one or more machines that were sold as the prime product;

collecting historical econometric data relevant to the prime product;

generating a group of candidate predictors from the historical telematics data and the historical econometric data;

selecting predictors from the group of candidate predictors;

establishing a forecasting model representing a relationship between the selected predictors and the historical sales data of the prime product; and

forecasting future sales of the prime product by using the established forecasting model.

12. The method of claim 11, further including:

performing data cleansing on the historical telematics data and the historical econometric data.

13. The method of claim 12, further including:

performing data validation on the cleansed data.

14. The method of claim 11, further including:

creating seasonality indices for prime product sales based on the historical sales data of the prime product; and

adding the created seasonality indices into the group of candidate predictors.

15. The method of claim 11, further including:

creating leading predictors for each of the candidate predictors; and

adding the created leading predictors into the group of candidate predictors.

16. The method of claim 11, wherein the step of selecting predictors from the group of candidate predictors including:

remove highly correlated candidate predictors;

perform stepwise regression analysis on the candidate predictors;

perform best subsets analysis on the candidate predictors; and

perform variance influence factor analysis on the candidate predictors.

17. The method of claim 11, wherein the step of establishing the forecasting model including:

generate a plurality of candidate forecasting models based on the selected predictors by performing a Lazy Evaluation Algorithm for Production Systems (LEAPS) algorithm; and

select the forecasting model from the plurality of candidate forecasting model based on at least one of a probability value, a fixation index, or a residual standard error of each candidate forecasting model.

18. The method of claim 11, wherein the step of forecasting sales of the prime product including:

forecasting future data of each predictor included the forecasting model; and

forecasting the future sales by using the established forecasting model based on the future data of each predictor.

19. The method of claim 11, wherein,

the historical telematics data includes at least one of service meter hours, idle hours, working hours, service meter hours per gallon of fuel, or number of machines reporting telematics data, and

the historical econometric data includes at least one of monthly industrial production indices of various industries, monthly average prices of various raw materials, or construction indicators.

20. A non-transitory computer-readable storage device storing instructions for forecasting future sales of a prime product, the instructions causing one or more computer processors to perform operations comprising:

collecting historical sales data of the prime product;

collecting historical telematics data from one or more machines that were sold as the prime product;

collecting historical econometric data relevant to the prime product;

generating a group of candidate predictors from the historical telematics data and the historical econometric data;

selecting predictors from the group of candidate predictors;

establishing a forecasting model representing a relationship between the selected predictors and the historical sales data of the prime product; and

forecasting future sales of the prime product by using the established forecasting model.