Manufacturers know variability is a big problem. But sometimes they don’t know how big. Manufacturing variability isn’t just an isolated anomaly. It’s a tangible and often widening gap between the intended objectives of a process and the actual result. It reflects inconsistencies in manufacturing operations that undercut reliability, increase costs, and degrade quality.
Quite simply, variability shouldn’t happen. Whether fabricating moving parts or producing chemical reactions, for example, the process should result in the same outcome, day after day. You want consistency. Knowing where the inconsistencies are coming from, however, is the challenge. The creeping causes that lead to variability are rarely obvious to the human eye. I’ve seen countless instances where small variations are overlooked and wind up becoming larger, costly failures. Fortunately, tools like AI can perceive them where people can’t.