Systems and methods for reducing manufacturing failure rates
Systems and methods are provided for reducing failure rates of a manufactured products. Manufactured products may be clustered together according to similarities in their production data. Manufactured product clusters may be analyzed to determine mechanisms for failure rate reduction, including adjustments to test quality parameters, product formulas, and product processes. Recommended product adjustments may be provided.
1. A system for reducing failure rates of a manufactured product comprising:
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the system to perform:
receiving, from a database, a first product data set, the first product data set including a first product formula, a plurality of first product examples, and first test results of the plurality of first product examples;
receiving, from the database, a second product data set, the second product data set including a second product formula, a plurality of second product examples, and second test results of the plurality of second product examples;
clustering, by a machine learning technique, a product cluster including the first product data set and the second product data set according to the first product formula and the second product formula;
generating, based on the clustering, a correlation model according to an in-common ingredient in the first product formula and the second product formula and an in-common test result of the first test results and the second test results;
predicting, based on the correlation model, a test result of the first test results;
determining, based on the predicted test result and an actual test result of the first test results, a test failure mode; and
determining, based on the test failure mode, a failure rate reduction mechanism of at least a product of the product cluster.
2. The system of claim 1 , wherein the system is further caused to perform:
selecting one of a first product or a second product as a selected product for failure rate reduction according to a comparison between the first test results of the plurality of first product examples and the second test results of the plurality of second product examples; and
determining the failure rate reduction mechanism of the selected product.
3. The system of claim 2 , wherein the determining of the failure rate reduction mechanism of the product cluster further causes the system to perform:
determining a formula adjustment of the selected product.
4. The system of claim 1 , wherein the determining of the failure rate reduction mechanism of the product cluster further causes the system to perform:
receiving a plurality of additional product data sets including a plurality of additional product formulas;
scoring the first product formula according to a weight of ingredients of the first product formula and a frequency of ingredients of the first product formula, the second product formula, and the plurality of additional product formulas;
scoring the second product formula according to a weight of ingredients of the second product formula and a frequency of ingredients of the first product formula, the second product formula, and the plurality of additional product formulas; and
determining the failure rate reduction mechanism of the product cluster according to a comparison of the score of the first product and the score of the second product.
5. The system of claim 1 , wherein the determining of the failure rate reduction mechanism of the product cluster further causes the system to perform:
identifying a selected test, wherein at least one of the first test results and second test results includes a failing result for the selected test; and
modifying a passing quality parameter range of the selected test such that the failing result is within a modified passing quality parameter range of the selected test.
6. The system of claim 5 , wherein the system is further caused to perform:
receiving a plurality of additional product data sets including a plurality of additional test results; and
identifying the selected test according to a frequency of failing results for the selected test among the first test results, the second test results, and the plurality of additional test results.
7. A computer implemented method for reducing failure rates of a manufactured product, the method being performed on a computer system having one or more physical processors programmed with computer program instructions that, when executed by the one or more physical processors, cause the computer system to perform the method, the method comprising:
receiving, from a database, a first product data set, the first product data set including a first product formula, a plurality of first product examples, and first test results of the plurality of first product examples;
receiving, from the database, a second product data set, the second product data set including a second product formula, a plurality of second product examples, and second test results of the plurality of second product examples;
clustering, by a machine learning technique, a product cluster including the first product data set and the second product data set according to a comparison between the first product formula and the second product formula;
generating, based on the clustering, a correlation model according to an in-common ingredient in the first product formula and the second product formula and an in-common test result of the first test results and the second test results;
predicting, based on the correlation model, a test result of the first test results;
determining, based on the predicted test result and an actual test result of the first test results, a test failure mode; and
determining, based on the test failure mode, a failure rate reduction mechanism of at least a product of the product cluster.
8. The computer implemented method of claim 7 , wherein the system is further caused to perform:
selecting one of a first product or a second product as a selected product for failure rate reduction according to a comparison between the first test results of the plurality of first product examples and the second test results of the plurality of second product examples; and
determining the failure rate reduction mechanism of the selected product.
9. The computer implemented method of claim 8 , wherein the determining of the failure rate reduction mechanism of the product cluster further causes the system to perform:
determining a formula adjustment of the selected product.
10. The computer implemented method of claim 7 , wherein the determining of the failure rate reduction mechanism of the product cluster further causes the system to perform:
receiving a plurality of additional product data sets including a plurality of additional product formulas;
scoring the first product formula according to a weight of ingredients of the first product formula and a frequency of ingredients of the first product formula, the second product formula, and the plurality of additional product formulas;
scoring the second product formula according to a weight of ingredients of the second product formula and a frequency of ingredients of the first product formula, the second product formula, and the plurality of additional product formulas; and
determining the failure rate reduction mechanism of the product cluster according to a comparison of the score of the first product and the score of the second product.
11. The computer implemented method of claim 7 , wherein the determining of the failure rate reduction mechanism of the product cluster further causes the system to perform:
identifying a selected test, wherein at least one of the first test results and second test results includes a failing result for the selected test; and
modifying a passing quality parameter range of the selected test such that the failing result is within a modified passing quality parameter range of the selected test.
12. The computer implemented method of claim 11 , wherein the system is further caused to perform:
receiving a plurality of additional product data sets including a plurality of additional test results; and
identifying the selected test according to a frequency of failing results for the selected test among the first test results, the second test results, and the plurality of additional test results.
13. A non-transitory computer readable medium comprising instructions that, when executed, cause one or more processors to perform:
receiving, from a database, a first product data set, the first product data set including a first product formula, a plurality of first product examples, and first test results of the plurality of first product examples;
receiving, from the database, a second product data set, the second product data set including a second product formula, a plurality of second product examples, and second test results of the plurality of second product examples;
clustering, by a machine learning technique, a product cluster including the first product data set and the second product data set according to a comparison between the first product formula and the second product formula; and
generating, based on the clustering, a correlation model according to an in-common ingredient in the first product formula and the second product formula and an in-common test result of the first test results and the second test results;
predicting, based on the correlation model, a test result of the first test results;
determining, based on the predicted test result and an actual test result of the first test results, a test failure mode; and
determining, based on the test failure mode, a failure rate reduction mechanism of at least a product of the product cluster.
14. The non-transitory computer readable medium of claim 13 , wherein the non-transitory computer readable medium is further caused to perform:
selecting one of a first product or a second product as a selected product for failure rate reduction according to a comparison between the first test results of the plurality of first product examples and the second test results of the plurality of second product examples; and
determining the failure rate reduction mechanism of the selected product.
15. The non-transitory computer readable medium of claim 14 , wherein the determining of the failure rate reduction mechanism of the product cluster further causes the non-transitory computer readable medium to perform:
determining a formula adjustment of the selected product.
16. The non-transitory computer readable medium of claim 14 , wherein the determining of the failure rate reduction mechanism of the product cluster further causes the non-transitory computer readable medium to perform:
receiving a plurality of additional product data sets including a plurality of additional product formulas;
scoring the first product formula according to a weight of ingredients of the first product formula and a frequency of ingredients of the first product formula, the second product formula, and the plurality of additional product formulas;
scoring the second product formula according to a weight of ingredients of the second product formula and a frequency of ingredients of the first product formula, the second product formula, and the plurality of additional product formulas; and
determining the failure rate reduction mechanism of the product cluster according to a comparison of the score of the first product and the score of the second product.
17. The non-transitory computer readable medium of claim 14 , wherein the determining of the failure rate reduction mechanism of the product cluster further causes the non-transitory computer readable medium to perform:
identifying a selected test, wherein at least one of the first test results and second test results includes a failing result for the selected test; and
modifying a passing quality parameter range of the selected test such that the failing result is within a modified passing quality parameter range of the selected test.