REAL-TIME TRACKING OF PRODUCT USING A CLOUD PLATFORM

• US 20130211870A1
• Filed: 12/21/2012
• Published: 08/15/2013
• Est. Priority Date: 02/09/2012
• Status: Abandoned Application

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Inventors: Lawson, Douglas C., Reichard, Douglas J., Harkulich, Joseph A., Hessmer, Rainer, Chand, Sujeet, Farchmin, David W.
• Application Number:
  US13/725,543
• Publication Number:
  US 20130211870A1
• Time in Patent Office: Days
• US Class Current: 705/7.25
• CPC Class Codes: G05B 19/41855 by local area network [LAN], network structure
• CPC Class Codes: G05B 2219/31151 Lan local area network
• CPC Class Codes: G05B 2219/33148 CLS client server architecture, client consumes, server provides services
• CPC Class Codes: G06F 21/64 Protecting data integrity, e.g. using checksums, certificates or signatures
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: H04L 12/66 Arrangements for connecting between networks having differing types of switching systems, e.g. gateways
• CPC Class Codes: H04L 43/04 Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/045 for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/14 using software, i.e. software packages network security related monitoring H04L63/1408
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/125 involving control of end-device applications over a network
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/561 Adding application-functional data or data for application control, e.g. adding metadata
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

A cloud-based product tracking system is provided. The product tracking system runs on a cloud platform and collects data from multiple devices throughout a supply chain, the data relating to production, transportation, storage, and sales of a product. Related subsets of the collected data are aggregated and correlated at the cloud platform based on contextual metadata associated with the data, a data model of the supply chain and systems therein, or other such factors. The cloud-based tracking system analyzes the correlated information to determine a status of a product within the supply chain. The tracking system also leverages the correlated data to analyze product flow, identify inefficiencies within the supply chain, and generate recommendations for modifying portions of the supply chain to mitigate the identified inefficiencies.

Claims

1. A product tracking system, comprising:

a memory that stores computer-executable components;

a processor operatively coupled to the memory that executes computer-executable components, including;

a device interface component configured to receive supply chain data from devices of a supply chain, wherein the device interface component receives the supply chain data on a cloud platform;

a correlation component configured to aggregate and correlate subsets of the supply chain data to yield correlated data; and

a tracking component configured to determine a status of a product within the supply chain based on the correlated data.

2. The product tracking system of claim 1, further comprising a client interface component configured to serve a display screen to a client device and to visualize information regarding the status of the product on the display screen.

3. The product tracking system of claim 1, wherein the correlation component determines a correlation between subsets of the supply chain data based in part on a data model that models at least a portion of the supply chain.

4. The product tracking system of claim 1, wherein the tracking component is further configured to identify a product flow inefficiency associated with a portion of the supply chain based on an analysis of the correlated data over a time range.

5. The product tracking system of claim 4, wherein the tracking component is further configured to generate a recommendation for adjusting at least one supply chain process to mitigate the product flow inefficiency.

6. The product tracking system of claim 1, wherein the tracking component is further configured to predict a level of demand for the product based on an analysis of the supply chain data.

7. The product tracking system of claim 6, wherein the tracking component is further configured to generate a recommended modification to at least one supply chain process based on the level of demand.

8. The product tracking system of claim 7, wherein the recommended modification comprises a modification predicted to adjust an inventory level for the product to a level that satisfies the level of demand.

9. The product tracking system of claim 6, wherein the tracking component is further configured to generate or alter an order for materials based on the level of demand.

10. The product tracking system of claim 3, wherein the tracking component is further configured to output an estimated time of arrival for the product to reach a specified point in the supply chain based on the status and latency information modeled by the data model.

11. A method for tracking product through a supply chain, comprising:

receiving, at a cloud platform, supply chain data from a plurality of devices within a supply chain;

aggregating and correlating subsets of the supply chain data yielding correlated data; and

identifying a status of a product within the supply chain based on the correlated data.

12. The method of claim 11, further comprising:

delivering a display screen to a client device via the cloud platform; and

rendering information regarding the status on the display screen.

13. The method of claim 11, wherein the aggregating and correlating comprises determining a correlation between subsets of the supply chain data based in part on a data model of at least a portion of the supply chain.

14. The method of claim 11, further comprising identifying an inefficiency in a product flow through the supply chain based on an analysis of the correlated data over a time range.

15. The method of claim 14, further comprising outputting a recommended adjustment to at least one supply chain process predicted to mitigate the inefficiency.

16. The method of claim 11, further comprising predicting a demand level for the product based on an analysis of the supply chain data.

17. The method of claim 16, further comprising outputting a recommended adjustment to at least one supply chain process predicted to maintain an inventory level that satisfies the demand level.

18. The method of claim 16, further comprising generating or adjusting an order for materials based on the demand level.

19. The method of claim 13, further comprising outputting an estimated time at which the product will arrive at a specified point in the supply chain based on the status and latency information read from the data model.

20. A computer-readable medium having stored thereon computer-executable instructions that, in response to execution, cause a computing system to perform operations, including:

collecting, from multiple devices within a supply chain, data relating to a product in the supply chain;

storing the data in storage associated with a cloud platform;

aggregating and correlating subsets of the data at the cloud platform to yield correlated data; and

generating first output information identifying a status of the product within the supply chain.

21. The computer-readable medium of claim 20, wherein the status comprises at least one of a current location of the product within the supply chain, an estimated time of arrival of the product at a specified point within the supply chain, identification of whether the product is in production, identification of whether the product is in transit, an inventory level for the product, or a demand level for the product.

22. The computer-readable medium of claim 20, further comprising generating second output information identifying a product flow inefficiency associated with a system within the supply chain based on an analysis of the correlated data.