Graph theory and network analytics and diagnostics for process optimization in manufacturing

Patent metadata

Field	Value
Publication number	US-10248110-B2
Application number	US-201815941911-A
Country	US
Kind code	B2
Filing date	Mar 30, 2018
Priority date	Mar 23, 2015
Publication date	Apr 2, 2019
Grant date	Apr 2, 2019

Abstract

A system, method, and computer-readable medium are disclosed for analysis and characterization of manufacturing information such as process trees or genealogies using graph theory. More specifically, using graph theory to analyze manufacturing information of a manufacturing operation allows for deep analysis of relationships between batches or units in a process tree and their closeness or distance, to identify clusters associated with specific quality characteristics or problems, to identify common antecedents of specifically labeled batches (e.g., problem batches), and/or to detect overall desirable or undesirable characteristics of the process tree (e.g., centrality, etc.).

First claim

What is claimed is:

1. A computer-implementable method for predicting characteristics of unmeasured batches in a manufacturing operation, comprising:
identifying, using one or more computing device processors, manufacturing units of a manufacturing operation;
characterizing, using the one or more computing device processors, the manufacturing units as nodes associated with a manufacturing operation graph representation;
characterizing, using the one or more computing device processors, at least one of an input material, a supplier, a part, or another input associated with the manufacturing operation as nodes associated with the manufacturing operation graph representation;
characterizing, using the one or more computing device processors, manufacturing steps associated with the manufacturing operation as connections associated with the manufacturing operation graph representation;
generating, using the one or more computing device processors, the manufacturing operation graph representation using the characterized nodes and connections;
measuring, using the one or more computing device processors, a characteristic of a batch associated with the manufacturing operation;
identifying, using the one or more computing device processors, connections between the measured batch and an unmeasured batch associated with the manufacturing operation;
computing, using the one or more computing device processors, based on the identified connections associated with the manufacturing operation graph representation, a degree of connectedness between the measured batch and the unmeasured batch; and
predicting, using the one or more computing device processors, based on the degree of connectedness, a characteristic of the unmeasured batch.

2. The method of claim 1 , further comprising:
performing, using the one or more computing device processors, graph analytics associated with the manufacturing operation graph representation.

3. The method of claim 2 , wherein the measured batch comprises a known bad batch.

4. The method of claim 2 , further comprising:
clustering, using the one or more computing device processors, in a first cluster, first batches associated with the manufacturing operation;
clustering, using the one or more computing device processors, in a second cluster, second batches associated with the manufacturing operation; and
determining, using the one or more computing device processors, differences between the first cluster and the second cluster, based on common antecedent batches between the first cluster and the second cluster.

5. The method of claim 2 , wherein the connections comprise at least one uni-directional connection, the uni-directional connection defining a workflow of the first batches.

6. The method of claim 5 , wherein the connections comprise at least one bi-directional connection, the bi-directional connection defining a workflow of the second batches.

7. The method of claim 6 , wherein the connections identify relationships of manufacturing items through upstream nodes and downstream nodes.

8. The method of claim 2 , wherein in response to determining the characteristic of the unmeasured batch does not meet a standard, the unmeasured batch is reused as an input material.

9. The method of claim 2 , wherein the graph analytics are used to determine a centrality characteristic of the manufacturing operation graph representation.

10. The method of claim 2 , wherein the manufacturing operation comprises a pharmaceutical manufacturing operation.

11. The method of claim 2 , wherein the manufacturing operation comprises an information handling system manufacturing operation.

12. A computer system for predicting characteristics of unmeasured batches in a manufacturing operation, the computer system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code associated with a plurality of computer operations and comprising instructions executable by the processor and configured for:
identifying manufacturing units of a manufacturing operation;
characterizing the manufacturing units as nodes associated with a manufacturing operation graph representation;
characterizing at least one of an input material, a supplier, a part, or another input associated with the manufacturing operation as nodes associated with the manufacturing operation graph representation;
characterizing manufacturing steps associated with the manufacturing operation as connections associated with the manufacturing operation graph representation;
generating the manufacturing operation graph representation using the characterized nodes and connections;

measuring a characteristic of a batch associated with the manufacturing operation;
identifying connections between the measured batch and an unmeasured batch associated with the manufacturing operation;
computing, based on the identified connections associated with the manufacturing operation graph representation, a degree of connectedness between the measured batch and the unmeasured batch; and
predicting, based on the degree of connectedness, a characteristic of the unmeasured batch.

13. The system of claim 12 , wherein the instructions executable by the processor are further configured for performing graph analytics associated with the manufacturing operation graph representation.

14. The system of claim 12 , wherein the measured batch comprises a known bad batch.

15. The system of claim 12 , wherein the instructions executable by the processor are further configured for:
clustering, in a first cluster, first batches associated with the manufacturing operation;
clustering, in a second cluster, second batches associated with the manufacturing operation; and
determining differences between the first cluster and the second cluster, based on common antecedent batches between the first cluster and the second cluster.

16. A non-transitory, computer-readable storage medium embodying computer program code for predicting characteristics of unmeasured batches in a manufacturing operation, the computer-readable storage medium comprising graph theory manufacturing operation representation code, the computer program code comprising computer executable instructions configured for:
identifying manufacturing units of a manufacturing operation;
characterizing the manufacturing units as nodes associated with a manufacturing operation graph representation;
characterizing at least one of an input material, a supplier, a part, or another input associated with the manufacturing operation as nodes associated with the manufacturing operation graph representation;
characterizing manufacturing steps associated with the manufacturing operation as connections associated with the manufacturing operation graph representation;
generating the manufacturing operation graph representation using the characterized nodes and connections;
measuring a characteristic of a batch associated with the manufacturing operation;
identifying connections between the measured batch and an unmeasured batch associated with the manufacturing operation;
computing, based on the identified connections associated with the manufacturing operation graph representation, a degree of connectedness between the measured batch and the unmeasured batch; and
predicting, based on the degree of connectedness, a characteristic of the unmeasured batch.

17. The non-transitory, comp

Assignees

• Tibco Software Inc

Inventors

• Hill Thomas
• Lewicki Pawel

Classifications

• G05B2219/32194

  Quality prediction · CPC title

• G05B19/41865 Primary

  characterised by job scheduling, process planning, material flow · CPC title

• G05B2219/32131

  Use job graph · CPC title

• Y02P90/02

  Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS] · CPC title