Method and system for policy management, testing, simulation, decentralization and analysis

1. A method of managing supply chain risks including at least one supply chain risk analysis implementation, comprising:

loading, by a processor, from a data storage or a memory, at least one supply chain data for a product in at least one supply chain which indicates information about the at least one supply chain, the supply chain data including at least one supplier for the product;

mapping, by the processor, the at least one supply chain data by linking the at least one supplier of the product and the product to form a consistent input model;

automatically analyzing, by an analytics module implemented on the processor, the input model to detect supply chain anomalies indicating the supply chain risks by comparing a price of the product with specified or calculated known good parameters expected in the input model;

producing, by the processor, an analysis results output of the analyzed input model; and

outputting the analysis results output of the detected supply chain anomalies to the memory, the data storage, a display, or a message.

2. The method according to claim 1 , further comprising reading, by the processor, the at least one supply chain data for the at least one supply chain from one or more of an Enterprise Resource Planning (ERP) system, a procurement system, and/or a public data sources and storing the read one supply chain data for the at least one supply chain to the data storage or the memory for loading.

3. The method according to claim 1 , wherein the supply chain data includes at least one of supplies, products, prices, supplier relationships or purchase history.

4. The method according to claim 1 , wherein the supply chain data includes information about Information Technology (IT) products and/or parts.

5. The method according to claim 1 , wherein the input model includes a model defined by a consistent metamodel or schema.

6. The method according to claim 1 , wherein mapping the at least one supply chain data includes determining references between information contained in the at least one supply chain data.

7. The method according to claim 1 , wherein automatically analyzing the input model includes detecting deviations from an average of values contained in the input model, is determined from an average of multiple purchases, or from an average of multiple backorder timeframes.

8. The method according to claim 1 , wherein the supply chain risks include lack of at least one of quality/quality assurance (QA), counterfeits, or hidden malware.

9. The method according to claim 1 , wherein outputting the analysis results output of the detected supply chain anomalies includes outputting at least one of aggregated alerts, alarms, detailed information, graphical visualizations, or reports in human-readable form.

10. A supply chain risk analysis system, comprising:

a processor, the processor being implemented with at least one of a mapping module and an analytics module and reading at least one supply chain data for at least one supply chain which indicates information about the at least one supply chain from a data source; and

a data storage or a memory that stores the at least one supply chain data for at least one supply chain, wherein

the processor is configured to:

load, from the data storage or the memory, the at least one supply chain data for a product in at least one supply chain which indicates information about the at least one supply chain, the supply chain data including at least one supplier for the product,

map, by a mapping module, the at least one supply chain data by linking the at least one supplier of the product and the product to form a consistent input model,

automatically analyze, by the analytics module, the input model to detect supply chain anomalies indicating supply chain risks by comparing a price of

the product with specified or calculated known good parameters expected in the input model,

produce an analysis results output of the analyzed input model, and

output the analysis results output of the detected supply chain anomalies to the memory, the data storage, a display, or a message.

11. The supply chain risk analysis system according to claim 10 , wherein the processor is configured to read the at least one supply chain data for the at least one supply chain from one or more of an Enterprise Resource Planning (ERP) system, a procurement system, and/or a public data sources and stores the read one supply chain data for the at least one supply chain to the data storage or the memory for loading.

12. The supply chain risk analysis system according to claim 10 , wherein the supply chain data includes at least one of supplies, products, prices, supplier relationships or purchase history.

13. The supply chain risk analysis system according to claim 10 , wherein the supply chain data includes information about Information Technology (IT) products and parts.

14. The supply chain risk analysis system according to claim 10 , wherein the input model includes a model defined by a consistent metamodel or schema.

15. The supply chain risk analysis system according to claim 10 , wherein the processor is configured to map the at least one supply chain data by determining references between information contained in the at least one supply chain data.

16. The supply chain risk analysis system according to claim 10 , wherein the processor is configured to automatically analyze the input model by detecting deviations from an average of values contained the input model, is determined from an average of multiple purchases, or from an average of multiple backorder timeframes.

17. The supply chain risk analysis system according to claim 10 , wherein the supply chain risks include lack of at least one of quality/quality assurance (QA), counterfeits, or hidden malware.

18. The supply chain risk analysis system according to claim 10 , wherein the processor is configured to output the analysis results output of the detected supply chain anomalies by outputting at least one of aggregated alerts, alarms, detailed information, graphical visualizations, or reports in human-readable form.