Design of Experiments (DOE) helps improve products and processes more efficiently, providing a comprehensive understanding of influences on the end result.
In-process verification uses high-tech tools like 3D scanners and laser trackers on the factory floor to monitor products as they're made, allowing instant analysis and immediate problem-solving.
Monte Carlo simulation helps companies understand process variability and make informed decisions. It's beneficial for quality control in manufacturing.
Generative AI and machine learning tools are particularly appealing as they offer insights into the underlying relationships within manufacturing processes.
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Process mapping is a method to visualize and understand manufacturing processes, similar to a flowchart. It helps identify inefficiencies and delays in the workflow. For example, it can pinpoint the source of delays in an assembly line.
Real-world problems often have multiple causes, so it's beneficial to extend your root cause analysis by considering the problem from various perspectives.
The most frustrating moment in problem-solving is fixing an error only to see it reappear the next day. Until the root cause is identified and addressed, the issue will persist.