Techniques for performing business analysis based on incomplete and/or stage-based data

• US 7,676,390 B2
• Filed: 09/04/2003
• Issued: 03/09/2010
• Est. Priority Date: 09/04/2003
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Gambhir, Snehil, LaComb, Christina A., Senturk, Deniz, Hoerl, Roger W., Kalish, Peter A.
• Primary Examiner(s): Boswell; Beth V
• Assistant Examiner(s): Kardos; Neil R
• Application Number:
  US10/654,738
• Publication Number:
  US 20050055257A1
• Time in Patent Office: 2,378 Days
• Field of Search: 705/10, 705/7
• US Class Current: 705/7.11
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Electrical data processing techniques are described for performing business analysis based on datasets that are incomplete (e.g., contain censored data) and/or based on datasets that are derived from a stage-based business operation. A first technique offsets the effects of error caused by the incomplete dataset by performing a trending operation followed by a de-trending operation. A second technique provides a model containing multiple sub-models, where the output of one sub-model serves as the input to another sub-model in recursive fashion. A third technique determines when a specified event is likely to occur with respect to a given asset by first discriminating whether the event is very unlikely to occur; if the asset does not meet this initial test, it is further processed by a second sub-model, which determines the probability that the specified event will occur for each of a specified series of time intervals.

Claims

1. A method for performing business-related analysis, comprising:

generating a predicted value using a model that is based on censored data collected from a multi-stage business operation using an electronic data processing apparatus, the predicted value containing an error attributed to information that is missing from the censored data within a specified time interval;

performing a trending operation using trending logic executed by the electronic data processing apparatus to derive a standardized score that pertains to a variance of the predicted value with respect to other predicted values generated using the model for the specified time interval;

performing a de-trending operation using de-trending logic executed by the electronic data processing apparatus to reduce the error in the predicted value based on the standardized score calculated in the trending logic, wherein the de-trending operation comprises the steps of;

computing an actual mean of actual values for the specified time interval;

computing an actual standard deviation of actual values for the specified time interval; and

computing an output result by multiplying the standardized score calculated in the trending logic by the actual standard deviation to produce a product, and adding the actual mean to the product;

generating an electrical signal representative of the output result that includes probability information associated with the output result; and

controlling the multi-stage business operation based on the output result.

2. The method according to claim 1, wherein the trending operation comprises:

computing a predicted mean of a collection of predicted values within the specified time interval;

computing a predicted standard deviation of the predicted values within the specified time interval; and

computing the standardized score by subtracting the predicted mean from the predicted value to produce a difference, and dividing the difference by the predicted standard deviation.

3. The method according to claim 1, wherein the censored data contains at least 30 percent missing information relative to a total population of potential information.

4. The method according to claim 1, wherein the business operation pertains to a business in which vehicles are leased to customers, and wherein the censored data includes cycle time values that reflect the respective amounts of time for which the customers lease the vehicles.

5. The method according to claim 4, wherein missing information from the censored data corresponds to vehicles that have not yet been returned by respective customers, and thus for which the cycle time values are not yet determined.

6. The method according to claim 4, wherein the predicted value pertains to an estimate of when a customer will return a leased vehicle.

7. The method according to claim 1, wherein the model comprises a plurality of sub-models corresponding to a respective stage of the multi-stage business operation.

8. A program storage device readable by an electronic data processing apparatus, tangibly embodying a program of instructions executable by the apparatus to perform method steps, said method steps comprising:

generating a predicted value using a model that is based on censored data collected from a multi-stage business operation using an electronic data processing apparatus, the predicted value containing an error attributed to information that is missing from the censored data within a specified time interval;

performing a trending operation using trending logic executed by the electronic data processing apparatus to derive a standardized score that pertains to a variance of the predicted value with respect to other predicted values generated using the model for the specified time interval;

performing a de-trending operation using de-trending logic executed by the electronic data processing apparatus to reduce the error in the predicted value based on the standardized score calculated in the trending logic, wherein the de-trending operation comprises the steps of;

computing an actual mean of actual values for the specified time interval;

computing an actual standard deviation of actual values for the specified time interval; and

computing an output result by multiplying the standardized score calculated in the trending logic by the actual standard deviation to produce a product, and adding the actual mean to the product;

generating an electrical signal representative of the output result that includes probability information associated with the output result; and

controlling the multi-stage business operation based on the output result.

9. The device according to claim 8, wherein the trending logic is to further:

compute a predicted mean of the other predicted values within the specified time interval;

compute a predicted standard deviation of the other predicted values within the specified time interval;

compute the standardized score by subtracting the predicted mean from the computed predicted value to produce a difference; and

divide the different by the predicted standard deviation.

10. The device according to claim 8, wherein the model comprises a plurality of sub-models corresponding to a respective stage of the multi-stage business operation.