IP Library Granted Patent US 11,287,809
Granted Patent B2
US 11,287,809 · App. 16/302,477 · Granted Mar 29, 2022

Apparatus, engine, system and method for predictive analytics in a manufacturing system

Inventor: Bruce Shibuya (St. Petersburg, FL)
Assignee: JABIL INC.
G05B23/0283G05B13/048G05B23/0243G05B23/0272G05B23/0289
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Quick Facts
Patent No.
US 11,287,809
App. No.
16/302,477
Granted
Mar 29, 2022
Kind
B2
Abstract

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. Included are a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor associated with a computing memory and suitable for executing code from the computing memory. The code may include an adaptor; an extractor; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model comprised of target data for the at least one machine, and capable of providing feedback to the at least one machine to modify performance of the at least one machine based on the application of the at least one predictive model; and a visualizer capable of providing a visualization of the feedback.

Claims (27)

1. A predictive analytics engine capable of providing real time analytics in a manufacturing system, comprising:

a data input to a processing system including at least one processor, the data input capable of receiving raw data output from at least one manufacturing machine operable to effect manufacturing in the manufacturing system;

the at least one processor being associated with a computing memory and being suitable for executing non-transitory code from the computing memory, the execution of the code causing to occur the steps of:

relationally pushing the received raw data to one or more databases stored in the computing memory to form processed data;

extracting the processed data from the one or more databases upon identification of a type of the at least one manufacturing machine;

predictively modelling the extracted processed data at least by applying thereto at least one predictive model comprised of operational target data for t h e at least one manufacturing machine to thereby generate feedback, including at least prospective first time failures, to the at least one manufacturing machine;

modifying performance of at least one manufacturing machine based on the feedback from the predictively modelling; and

displaying to a user of at least a visualization of the feedback and of the modified performance.

2. The predictive analytics engine of claim 1 , wherein the data input resides in a device layer.

3. The predictive analytics engine of claim 2 , wherein the device layer additionally comprises at least machine-language processing to, in part, provide the identification of the type of the at least one manufacturing machine.

4. The predictive analytics engine of claim 1 , wherein the visualization results from a reporting engine suitable to generate one or more reports.

5. The predictive analytics engine of claim 1 , wherein the predictively modelling comprises applying a learning app that learns over repeated applications of the at least one predictive model.

6. The predictive analytics engine of claim 5 , wherein the learning app comprises a supervised module.

7. The predictive analytics engine of claim 1 , wherein the predictive model includes minimized yield loss.

8. The predictive analytics engine of claim 1 , wherein the modifying performance includes increasing production capacity across multiple ones of the manufacturing machines substantially simultaneously.

9. The predictive analytics engine of claim 1 , wherein the displaying the visualization comprises a graphical user interface.

10. The predictive analytics engine of claim 9 , wherein the graphical user interface is a mobile device interface.

11. The predictive analytics engine of claim 1 , wherein the displaying further comprises intercommunicating the visualization between at least ones of cell managers, suppliers, operators, engineers and operations.

12. The predictive analytics engine of claim 11 , wherein the intercommunicating comprises indicating production line failures.

13. The predictive analytics engine of claim 11 , wherein the intercommunicating comprises publishing system alerts.

14. The predictive analytics engine of claim 1 , wherein the displaying is configurable by at least one authorized user.

15. The predictive analytics engine of claim 1 , wherein the predictive model is product-centric to a single manufactured product.

16. The predictive analytics engine of claim 1 , wherein the relational database further comprises an interrelation of operators, manufacturing engineers, operations, quality and component suppliers.

17. The predictive analytics engine of claim 1 , wherein the predictive model comprises a shop floor plan.

18. The predictive analytics engine of claim 1 , wherein the predictively analyzing is at a low data rate during manufacturing operations.

19. The predictive analytics engine of claim 1 , wherein the predictive model comprises upstream process inspection and control parameters.

20. The predictive analytics engine of claim 19 , wherein the predictive model comprises quality metrics.

Assignments (3)
CHANGE OF NAME Recorded Feb 9, 2022
From: JABIL CIRCUIT, INC.
To: JABIL INC.
Reel/Frame 058981/0721 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 3, 2022
From: SHIBUYA, BRUCE
To: JABIL CIRCUIT, INC.
Reel/Frame 058879/0386 →
CHANGE OF NAME Recorded Feb 3, 2022
From: JABIL CIRCUIT, INC.
To: JABIL INC.
Reel/Frame 058945/0163 →
Continuity (2)
Provisional Application 62337006 · May 16, 2016
Related Publication 20190278261A1 · Sep 12, 2019
Cited By (1)
US 12,645,214