Method and system for policy management, testing, simulation, decentralization and analysis
| Title: | Method and system for policy management, testing, simulation, decentralization and analysis |
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| Patent Number: | 12218,978 |
| Publication Date: | February 04, 2025 |
| Appl. No: | 17/864146 |
| Application Filed: | July 13, 2022 |
| Abstract: | A method of managing supply chain risks having a supply chain risk analysis implementation, includes loading from a data storage or a memory, supply chain data for a supply chain which indicates information about the supply chain; mapping the supply chain data to a consistent input model; automatically analyzing, by an analytics module implemented on a processor, the input model to detect supply chain anomalies indicating the supply chain risks; producing 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. A supply chain risk analysis system includes the processor, the data storage or the memory that stores the supply chain data for the supply chain which indicates information about the supply chain. The processor is configured to perform the processes. |
| Inventors: | Lang, Ulrich (San Diego, CA, US); Schreiner, Rudolf (Falkensee, DE) |
| Claim: | 1. A method for detecting anomalies in a supply chain, comprising: loading at least one predetermined supply chain data source in a form provided by a supply chain information technologies (IT) system, the at least one predetermined supply chain data including procurement data; automatically parsing the at least one predetermined supply chain data source and transforming the parsed at least one predetermined supply chain data source into at least one consistent, consolidated normalized supply chain data source; automatically selecting, based on the at least one normalized supply chain data source, at least one anomaly detection algorithm designed to indicate anomalies related to supply chain risks on the at least one normalized supply chain data source; indicating the anomalies in the at least one normalized supply chain data source by executing the at least one anomaly detection algorithm on the at least one normalized supply chain data source pertaining to information about organizations in the supply chain, products/services in the supply chain, logistical events and data; mapping any of the indicated anomalies in the at least one normalized supply chain data source to identified supply chain risk indicators; and outputting any of the identified supply chain risk indicators. |
| Claim: | 2. The method according to claim 1 , wherein the anomalies pertain to systems, devices, or parts being counterfeit, subpar in quality, recycled, second-hand, maliciously tampered, damaged, or transferred outside approved shipping/handling routes. |
| Claim: | 3. The method according to claim 1 , wherein at the at least one supply data source is an Enterprise Resource Planning (ERP) system, SAP, or Oracle ERP. |
| Claim: | 4. The method according to claim 1 , wherein transforming includes a model based on a data schema or a metamodel with the purpose of decoupling the anomaly analysis from the at least one supply chain data source. |
| Claim: | 5. The method according to claim 1 , wherein selecting the at least one anomaly detection algorithm is determined by the type of the data to be analyzed. |
| Claim: | 6. The method according to claim 1 , wherein the at least one anomaly detection algorithm comprises price outlier detection indicating supply chain risks that systems, devices, or parts with anomalous prices compared to normal prices are more likely to be counterfeited, repurposed, recycled, or sold by untrusted suppliers. |
| Claim: | 7. The method according to claim 6 , wherein the price outlier detection is calculated using for example historic price information, list price information, comparison price information. |
| Claim: | 8. The method according to claim 6 , wherein the price outlier detection is calculated per supplier, indicating outliers within shipments from one supplier, or shipments across multiple suppliers, for any given system, device, or part. |
| Claim: | 9. The method according to claim 1 , wherein outputting furthermore comprises presenting interactive visualization, report documents, or alerts. |
| Claim: | 10. The method according to claim 1 , wherein outputting furthermore comprises presenting indicated anomalies in the at least one normalized supply chain data for each supply chain risk indicators. |
| Claim: | 11. The method according to claim 1 , wherein access to the outputted identified supply chain risk indicators is restricted only to certain users based, particular users, roles groups, or departments. |
| Claim: | 12. The method according to claim 1 , wherein access to the outputted identified supply chain risk indicators is restricted using contextual access control depending on which data resources of the supply chain data source or normalized supply chain data source a user has access to types of information, data fields, columns, data labels, or data sources. |
| Claim: | 13. The method according to claim 1 , wherein the procurement data includes purchase records related to at least one of an item and a supplier. |
| Claim: | 14. A supply chain anomalies detection system, comprising: a processor; and a data storage or a memory, wherein the processor is configured to: load at least one predetermined supply chain data source in a form provided by a supply chain information technologies (IT) system, the at least one predetermined supply chain data including procurement data; automatically parse the at least one predetermined supply chain data source and transform the parsed at least one predetermined supply chain data source into at least one consistent, consolidated normalized supply chain data source; automatically select, based on the at least one normalized supply chain data source, at least one anomaly detection algorithm designed to indicate anomalies related to supply chain risks on the at least one normalized supply chain data source; indicate the anomalies in the at least one normalized supply chain data source by executing the at least one anomaly detection algorithm on the at least one normalized supply chain data source pertaining to information about organizations in the supply chain, products/services in the supply chain, logistical events and data; map any of the indicated anomalies in the at least one normalized supply chain data source to identified supply chain risk indicators; and output any of the identified supply chain risk indicators to the memory, the data storage, a display, or a message. |
| Claim: | 15. The supply chain anomalies detection system according to claim 14 , wherein the anomalies pertain to systems, devices, or parts being counterfeit, subpar in quality, recycled, second-hand, maliciously tampered, damaged, or transferred outside approved shipping/handling routes. |
| Claim: | 16. The supply chain anomalies detection system according to claim 14 , wherein at the at least one supply data source is an Enterprise Resource Planning (ERP) system, SAP, or Oracle ERP. |
| Claim: | 17. The supply chain anomalies detection system according to claim 14 , wherein the processor transforms a model based on a data schema or a metamodel with the purpose of decoupling the anomaly analysis from the at least one supply chain data source . |
| Claim: | 18. The supply chain anomalies detection system according to claim 14 , wherein the processor selects the at least one anomaly detection algorithm by the type of the data to be analyzed. |
| Claim: | 19. The supply chain anomalies detection system according to claim 14 , wherein the at least one anomaly detection algorithm comprises price outlier detection indicating supply chain risks that systems, devices, or parts with anomalous prices compared to normal prices are more likely to be counterfeited, repurposed, recycled, or sold by untrusted suppliers. |
| Claim: | 20. The supply chain anomalies detection system according to claim 19 , wherein the price outlier detection is calculated using for example historic price information, list price information, comparison price information. |
| Claim: | 21. The supply chain anomalies detection system according to claim 19 , wherein the price outlier detection is calculated per supplier, indicating outliers within shipments from one supplier, or shipments across multiple suppliers, for any given system, device, or part. |
| Claim: | 22. The supply chain anomalies detection system according to claim 14 , wherein as an output of the identified supply chain risk indicators, the processor is configured to present interactive visualization, report documents, or alerts. |
| Claim: | 23. The supply chain anomalies detection system according to claim 14 , wherein as an output of the identified supply chain risk indicators, the processor is configured to present indicated anomalies in the at least one normalized supply chain data for each supply chain risk indicators. |
| Claim: | 24. The supply chain anomalies detection system according to claim 14 , wherein access to the outputted identified supply chain risk indicators is restricted only to certain users based, particular users, roles groups, or departments. |
| Claim: | 25. The supply chain anomalies detection system according to claim 14 , wherein access to the outputted identified supply chain risk indicators is restricted using contextual access control depending on which data resources of the supply chain data source or normalized supply chain data source a user has access to types of information, data fields, columns, data labels, or data sources. |
| Claim: | 26. The supply chain anomalies detection system according to claim 14 , wherein the procurement data includes purchase records related to at least one of an item and a supplier. |
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| Primary Examiner: | Lakhia, Viral S |
| Attorney, Agent or Firm: | Muncy, Geissler, Olds & Lowe, P.C. |
| Accession Number: | edspgr.12218978 |
| Database: | USPTO Patent Grants |
| Language: | English |
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