Claims:WE CLAIM
1. A method of identifying root cause associated with risks in a supply chain network, the method comprising:
performing, by a root cause identification device, natural language processing and text analysis on a user query to derive keywords, entities involved, and relationships between the keywords and the entities;
categorizing, by the root cause identification device, the user query into a risk category selected from a plurality of risk categories;
creating, by the root cause identification device, a relationship mapping amongst at least one of a plurality of attributes associated with the risk category, in response to the categorization;
identifying, via the root cause identification device, a risk in the supply chain network based on the risk category and the relationship mapping; and
detecting, via the root cause identification device, a root cause from amongst a plurality of root causes associated with the risk.
2. The method of claim 1 further comprising receiving a user query, the user query comprising at least one of an audio query and a text query.
3. The method of claim 1 further comprising receiving a plurality of supply chain inputs associated with the supply chain network.
4. The method of claim 3, wherein the plurality of supply chain inputs are selected from a group comprising supply chain contributors, supply chain parameters, and supply chain data sources, the supply chain data sources being selected based on the supply chain parameters.
5. The method of claim 4, wherein the supply chain parameters are selected from a group comprising supply, demand, transportation, process, storage, information, finance, environment.
6. The method of claim 1, wherein performing the text analysis comprises iteratively classifying the user query to determine problem faced by the user based on the contextually relevant keywords derived.
7. The method of claim 1, wherein performing the text analysis comprises ignoring stop words in the user query.
8. The method of claim 1, wherein the plurality of risk categories comprises at least one of an external to supply chain category, an internal to supply chain category, and a management related category.
9. The method of claim 1 further comprising implementing incremental intelligence using machine learning techniques for future data analysis.
10. A root cause identification device for identifying root cause associated with risks in a supply chain network, the root cause identification device comprises:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to:
perform natural language processing and text analysis on a user query to derive keywords, entities involved, and relationships between the keywords and the entities;
categorize the user query into a risk category selected from a plurality of risk categories;
create a relationship mapping amongst at least one of a plurality of attributes associated with the risk category, in response to the categorization;
identify a risk in the supply chain network based on the risk category and the relationship mapping; and
detect a root cause from amongst a plurality of root causes associated with the risk.
11. The root cause identification device of claim 10, wherein the processor instructions further cause the processor to receive a user query comprising at least one of an audio query and a text query.
12. The root cause identification device of claim 10, wherein the processor instructions further cause the processor to receive a plurality of supply chain inputs associated with the supply chain network.
13. The root cause identification device of claim 12, wherein the plurality of supply chain inputs are selected from a group comprising supply chain contributors, supply chain parameters, and supply chain data sources, the supply chain data sources being selected based on the supply chain parameters.
14. The root cause identification device of claim 13, wherein the supply chain parameters are selected from a group comprising supply, demand, transportation, process, storage, information, finance, environment.
15. The root cause identification device of claim 10, wherein to perform the text analysis the processor instructions further cause the processor to iteratively classify the user query to determine problem faced by the user based on the contextually relevant keywords derived.
16. The root cause identification device of claim 10, wherein to perform the text analysis the processor instructions further cause the processor to ignore stop words in the user query.
17. The root cause identification device of claim 10, wherein the plurality of risk categories comprises at least one of an external to supply chain category, an internal to supply chain category, and a management related category.
18. The root cause identification device of claim 10, wherein the processor instructions further cause the processor to implement incremental intelligence using machine learning techniques for future data analysis.
19. A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising:
performing, by a root cause identification device, natural language processing and text analysis on a user query to derive keywords, entities involved, and relationships between the keywords and the entities;
categorizing, by the root cause identification device, the user query into a risk category selected from a plurality of risk categories;
creating, by the root cause identification device, a relationship mapping amongst at least one of a plurality of attributes associated with the risk category, in response to the categorization;
identifying, via the root cause identification device, a risk in the supply chain network based on the risk category and the relationship mapping; and

detecting, via the root cause identification device, a root cause from amongst a plurality of root causes associated with the risk.

Dated this 21st day of March, 2017

Swetha SN
Of K&S Partners
Agent for the Applicant
, Description:TECHNICAL FIELD
This disclosure relates generally to supply chain networks and more particularly to methods and devices for identifying root causes associated with risks in supply chain networks.