ELECTRONIC DATA MODELLING TOOL

• US 20160267583A1
• Filed: 03/09/2015
• Published: 09/15/2016
• Est. Priority Date: 03/09/2015
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Fei, Hongliang, He, Miao, Li, Hongfei, Qian, Buyue
• Application Number:
  US14/641,706
• Publication Number:
  US 20160267583A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

Generating an electronic data model of a logical supply chain and regenerating the model based on changes to modelling parameters, to guide decision making. The data model is generated using known information about the supply chain. One or more decision scenarios are modelled based on a simulated loan request, and the data model is regenerated to consider the consequences. A recommendation is made based on pre-defined or user-defined criteria.

Claims

1. A method for generating an electronic data model, comprising:

obtaining an electronic data model of a logical supply chain, the electronic data model comprising a lender node and a borrower node;

regenerating the electronic data model based on at least one decision scenario in response to a request by the borrower node to the lender node to finance a loan, wherein the request is based on one or more of a request received from the borrower node and a simulated request from the borrower node;

determining corresponding gain values and probabilities of occurrence, for the lender node, of the at least one decision scenario, wherein the corresponding gain values and probabilities of occurrence are determined with respect to the lender node and at least one node in the electronic data model other than the borrower node; and

recommending a decision according to the at least one decision scenario, in response to the request, based on the gain values and probabilities of occurrence.

2. The method of claim 1, wherein determining corresponding gain values and probabilities of occurrence comprises determining one or more of:

a chance of default value;

a change in sales value;

a change in market share value;

a change in liquidity value;

a change in strength of supplier relationships value;

a change in alliance strength value;

a probability of occurrence value of one or more decision scenarios, corresponding to a decision by a competing lender node, as to the request;

a loss value based on loss to the lender node if the competing lender node approves the request; and

an impact value that measures the impact of a response to the request on a structure of the electronic data model.

3. The method of claim 1, wherein determining corresponding gain values and probabilities of occurrence further comprises determining a long-term-impact value for the at least one decision scenario.

4. The method of claim 1, further comprising:

generating a graph of the electronic data model, wherein a size of a graphical representation of a given node in the electronic data model is based on a corresponding gain value of the given node; and

wherein a thickness of an edge of the graph between two given nodes indicates a flow value between the two given nodes.

5. The method of claim 1, further comprising:

communicating results of the recommending to a user;

receiving electronic input data in response to communicating the results, the electronic input data corresponding to one or more of (i) a new node definition, (ii) a new lender node definition, and (iii) a new relationship definition between two nodes;

regenerating the electronic data model based on the electronic input data; and

repeating one or more steps of the method to provide a new recommendation.

6. The method of claim 1, wherein determining corresponding probabilities of occurrence comprises:

retrieving historical data associated with properties of nodes of the electronic data model;

generating one or more electronic data models using the historical data; and

determining a probability of occurrence for at least one of the one or more electronic data models.

7. The method of claim 1, further comprising:

determining additional gain values and probabilities of occurrence, for one or more additional nodes, wherein the recommending is further based on the additional gain values and probabilities of occurrence.

8. A computer system for generating an electronic data model, comprising:

a computer device having a processor and a tangible storage device; and

a program embodied on the storage device for execution by the processor, the program having a plurality of program instructions to;

obtain an electronic data model of a logical supply chain, the electronic data model comprising a lender node and a borrower node;

regenerate the electronic data model based on at least one decision scenario in response to a request by the borrower node to the lender node to finance a loan, wherein the request is based on one or more of a request received from the borrower node and a simulated request from the borrower node;

determine corresponding gain values and probabilities of occurrence, for the lender node, of the at least one decision scenario, wherein the corresponding gain values and probabilities of occurrence are determined with respect to the lender node and at least one node in the electronic data model other than the borrower node; and

recommend a decision according to the at least one decision scenario, in response to the request, based on the gain values and probabilities of occurrence.

9. The system of claim 8, wherein instructions to determine corresponding gain values and probabilities of occurrence further comprise instructions to determine one or more of:

a chance of default value;

a change in sales value;

a change in market share value;

a change in liquidity value;

a change in strength of supplier relationships value;

a change in alliance strength value;

a probability of occurrence value of one or more decision scenarios, corresponding to a decision by a competing lender node, as to the request;

a loss value based on loss to the lender node if the competing lender node approves the request; and

an impact value that measures the impact of a response to the request on a structure of the electronic data model.

10. The system of claim 8, wherein instructions to determine corresponding gain values and probabilities of occurrence further comprise instructions to determine a long-term-impact value for the at least one decision scenario.

11. The system of claim 8, wherein the program instructions further comprise instructions to:

generate a graph of the electronic data model, wherein a size of a graphical representation of a given node in the electronic data model is based on a corresponding gain value of the given node; and

wherein a thickness of an edge of the graph between two given nodes indicates a flow value between the two given nodes.

12. The system of claim 8, wherein the program instructions further comprise instructions to:

communicate results of the recommending to a user;

receive electronic input data in response to communicating the results, the electronic input data corresponding to one or more of (i) a new node definition, (ii) a new lender node definition, and (iii) a new relationship definition between two nodes;

regenerate the electronic data model based on the electronic input data; and

repeat one or more steps of the method to provide a new recommendation.

13. The system of claim 8, wherein instructions to determine corresponding probabilities of occurrence further comprise instructions to:

retrieve historical data associated with properties of nodes of the electronic data model;

generate one or more electronic data models using the historical data; and

determine a probability of occurrence for at least one of the one or more electronic data models.

14. A computer program product for generating an electronic data model, comprising a tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, the method comprising:

obtaining, by the processor, an electronic data model of a logical supply chain, the electronic data model comprising a lender node and a borrower node;

regenerating the electronic data model based on at least one decision scenario in response to a request by the borrower node to the lender node to finance a loan, wherein the request is based on one or more of a request received from the borrower node and a simulated request from the borrower node;

determining, by the processor, corresponding gain values and probabilities of occurrence, for the lender node, of the at least one decision scenario, wherein the corresponding gain values and probabilities of occurrence are determined with respect to the lender node and at least one node in the electronic data model other than the borrower node; and

recommending, by the processor, a decision according to the at least one decision scenario, in response to the request, based on the gain values and probabilities of occurrence.

15. The computer program product of claim 14, wherein determining corresponding gain values and probabilities of occurrence further comprises determining, by the processor, one or more of:

a chance of default value;

a change in sales value;

a change in market share value;

a change in liquidity value;

a change in strength of supplier relationships value;

a change in alliance strength value;

a probability of occurrence value of one or more decision scenarios, corresponding to a decision by a competing lender node, as to the request;

a loss value based on loss to the lender node if the competing lender node approves the request; and

an impact value that measures the impact of a response to the request on a structure of the electronic data model.

16. The computer program product of claim 14, wherein determining corresponding gain values and probabilities of occurrence further comprises determining, by the processor, a long-term-impact value for the at least one decision scenario.

17. The computer program product of claim 14, wherein the method further comprises:

generating, by the processor, a graph of the electronic data model, wherein a size of a graphical representation of a given node in the electronic data model is based on a corresponding gain value of the given node; and

wherein a thickness of an edge of the graph between two given nodes indicates a flow value between the two given nodes.

18. The computer program product of claim 14, wherein the method further comprises:

communicating, by the processor, results of the recommending to a user;

receiving, by the processor, electronic input data in response to communicating the results, the electronic input data corresponding to one or more of (i) a new node definition, (ii) a new lender node definition, and (iii) a new relationship definition between two nodes;

regenerating, by the processor, the electronic data model based on the electronic input data; and

repeating, by the processor, one or more steps of the method to provide a new recommendation.

19. The computer program product of claim 14, wherein determining corresponding probabilities of occurrence further comprises:

retrieving, by the processor, historical data associated with properties of nodes of the electronic data model;

generating, by the processor, one or more electronic data models using the historical data; and

determining, by the processor, a probability of occurrence for at least one of the one or more electronic data models.

20. The computer program product of claim 14, further comprising:

determining, by the processor, additional gain values and probabilities of occurrence, for one or more additional nodes, wherein the recommending is further based on the additional gain values and probabilities of occurrence.