Computer assisted benchmarking system and method using induction based artificial intelligence

• US 8,788,452 B2
• Filed: 03/07/2002
• Issued: 07/22/2014
• Est. Priority Date: 03/08/2001
• Status: Active Grant

• Current Assignee: Deloitte Development LLC
• Original Assignee: Deloitte Development LLC
• Inventors: Stoneking, Michael D., Curet, Olivier L.
• Primary Examiner(s): LE, UYEN T
• Application Number:
  US10/093,073
• Publication Number:
  US 20030050814A1
• Time in Patent Office: 4,520 Days
• Field of Search: 705/7, 705 10- 11, 707/603
• US Class Current: 707/603
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

A system and method are provided for the collection of business performance data and the identification of patterns or rules from such data that are key predictors of future business performance. The performance data are preferably collected using one or several questionnaires containing a plurality of questions that probe into specific performance areas of companies. The questionnaires are used to collect responses applicable to a plurality of companies and the responses applicable to each company are stored in a database as separate company profiles to define the knowledge-base from which a rule induction engine may identify the key discriminators of business performance.

Claims

1. A computerized method of business performance benchmarking to inductively identify key factors that drive towards a specific outcome value, comprising:

obtaining business performance information for a plurality of business entities from one or more representatives of, or consultants to, each business entity, the business performance information including (i) a first set of performance criteria covering various different business performance areas, each performance criterion of the first set of performance criteria having an associated value for scoring the performance of the plurality of the business entities, and (ii) an outcome value representing a rank or state of the business entity; and

using a data processor, applying a classification and regression tree algorithm to jointly evaluate the first set of performance criteria and associated values for each of the plurality of business entities, and using said outcome value, automatically identify an optimal subset of performance criteria that most contribute to the outcome value for said plurality of business entities,

wherein said associated value to each performance criterion is a number chosen from a range of at least four possible numbers corresponding to how well a company implements or accomplishes the given business performance criterion.

2. The method of claim 1, wherein said obtaining the first set of business criteria and the associated values is done via a questionnaire containing a plurality of questions that probe into said various business performance areas.

3. The method of claim 1, wherein the outcome value is based on an average score of all the associated values for that entity.

4. The method of claim 3 wherein said outcome value is a quartile ranking such that the highest 25 percent of business entities in said plurality are ranked in the top quartile, the middle 50 percent are ranked in the inter-quartile and the lowest 25 percent are ranked in the bottom quartile.

5. The method of claim 1 further comprising the step of representing the optimal subset of performance criteria that most contribute to the outcome value as a classification tree wherein a first performance criterion of the subset of performance criteria is identified as a root node and all other performance criterion from the subset of performance criteria are identified as sub-nodes.

6. The method of claim 1 wherein the associated values comprise numerical values based on a scale between 1 and 5.

7. The method of claim 1 wherein the first set of performance criteria and the associated numerical values for scoring the performance of the plurality of entities are stored in a database as separate company profiles to define a knowledge-base from which a rule induction engine identifies the key discriminators of business performance.

8. The method of claim 1 wherein the associated values comprise numerical values based on a scale between 1 and 5.

9. A nontransitory computer readable medium, comprising a set of instructions that when executed, cause a computer or data processor to:

obtain business performance information for a plurality of business entities from one or more representatives of, or consultants to, each business entity, the business performance information including (i) a first set of performance criteria covering various different business performance areas, each performance criterion of the first set of performance criteria having an associated value for scoring the performance of the plurality of the business entities, and (ii) an outcome value representing a rank or state of the business entity; and

apply a classification and regression tree algorithm to jointly evaluate the first set of performance criteria and associated values for each of the plurality of business entities, and using said outcome value, automatically identify an optimal subset of performance criteria that most contribute to the outcome value for said plurality of business entities,

wherein said associated value to each performance criterion is a number chosen from a range of at least four possible numbers corresponding to how well a company implements or accomplishes the given business performance criterion.

10. The nontransitory computer readable medium of claim 9, further comprising a spreadsheet application and a presentation application for generating reports based on the business information data.

11. The nontransitory computer readable medium of claim 10 wherein the spreadsheet application supplies the business performance information to the rule induction engine.

12. The nontransitory computer readable medium of claim 9, wherein said instructions further cause said computer to obtain the first set of business criteria and the associated values via a questionnaire containing a plurality of questions that probe into specific performance areas of the business entities.

13. The nontransitory computer readable medium of claim 9, wherein said outcome value is based on an average score of all the associated numerical values for that entity.

14. The nontransitory computer readable medium of claim 9, wherein said outcome value is a quartile ranking such that the highest 25 percent of business entities in said plurality are ranked in the top quartile, the middle 50 percent are ranked in the inter-quartile and the lowest 25 percent are ranked in the bottom quartile.

15. The nontransitory computer readable medium of claim 9, wherein said instructions further cause said computer to represent the optimal subset of performance criteria that most contribute to the outcome value as a classification tree wherein a first performance criterion of the subset of performance criteria is identified as a root node and all other performance criterion from the subset of performance criteria are identified as sub-nodes.

16. The nontransitory computer readable medium of claim 9, wherein the first set of performance criteria and the associated numerical values for scoring the performance of the plurality of entities are stored in a database as separate company profiles to define a knowledge-base from which a rule induction engine identifies the key discriminators of business performance.