BMW says it has used artificial-intelligence applications in series production since 2018. One approach analyzes component images during production, comparing them with examples to flag parts that may be missing or misplaced. The company presents this as a way to shift repetitive visual checks from staff to software, not as a claim that people leave quality control. [shared-31a2e70f2a855f9f239d76f8c38d8d128435e1fab384316e4151e71fbe9019dc]
The process still depends on workers at the start: employees photograph components from different angles and mark potential deviations, creating examples used to train the system. At Dingolfing, BMW says another application compares a car’s model designation in a live image with its order data; a mismatch notifies final inspection. A human team therefore receives cases where the records disagree. [shared-31a2e70f2a855f9f239d76f8c38d8d128435e1fab384316e4151e71fbe9019dc]
For production staff, the possible gain is time away from routine inspection and toward judgments that need experience. But BMW’s account is a company description, not an independent measure of accuracy, labor saved or who receives the productivity benefit. The unanswered question is how those gains—and the work of labeling, checking and responding to exceptions—are distributed as these systems spread. [shared-31a2e70f2a855f9f239d76f8c38d8d128435e1fab384316e4151e71fbe9019dc]