I would imagine that they just log everything. Serial number, temperature, which cycle is used, time of day, how long it takes to fill the washer, how long it takes to drain the washer. Everything. Put all data in a great big database. When something needs to be fixed and is covered by the warranty, mark that the failed part is associated with that serial number.
Then do some sort of a regression to discover what logged parameters are associated with what failure modes/broken parts. If washers that take less time to fill up have higher than normal failure rates for some elbow joint, that probably means that high water pressure causes the elbow joint to fail. If a certain elbow join's failure rate is simply correlated with the number of cycles, that tells you something different. If a certain elbow join has a high failure rate that's not associated with anything, that probably just means it's a shitty part. But you learn something.
By logging everything and running a regression analysis, when you develop next year's model, you know where to improve. Now when you tell an expensive engineer, "This elbow join failed on 1000 units of revision F. Make it fail on 100 or less units of revision G." you can also give them a starting point to work with.
I'm a software guy. If I get 10 crash dumps, and you don't tell me anything, I don't necessarily know what to work with. If you give me those same 10 crash dumps and tell me that 9 of them had the language set to Arabic or Hebrew I know it's probably a BOM bug. Same thing.
Or you just sell the data to ad companies and let them figure out how to get value from it.
Then do some sort of a regression to discover what logged parameters are associated with what failure modes/broken parts. If washers that take less time to fill up have higher than normal failure rates for some elbow joint, that probably means that high water pressure causes the elbow joint to fail. If a certain elbow join's failure rate is simply correlated with the number of cycles, that tells you something different. If a certain elbow join has a high failure rate that's not associated with anything, that probably just means it's a shitty part. But you learn something.
By logging everything and running a regression analysis, when you develop next year's model, you know where to improve. Now when you tell an expensive engineer, "This elbow join failed on 1000 units of revision F. Make it fail on 100 or less units of revision G." you can also give them a starting point to work with.
I'm a software guy. If I get 10 crash dumps, and you don't tell me anything, I don't necessarily know what to work with. If you give me those same 10 crash dumps and tell me that 9 of them had the language set to Arabic or Hebrew I know it's probably a BOM bug. Same thing.
Or you just sell the data to ad companies and let them figure out how to get value from it.