While we may tweak things in production, we rarely get permission to conduct formal experiments with an operating production line. Production’s job is to make product, whereas experiments are what they do in R&D. So how can we learn about an existing production process without rocking the boat? This is where input and outcome charts have a role to play.

In a production environment, things are generally assumed to be OK until the fraction of nonconforming product takes a turn for the worse. When this happens the production line is suddenly “in trouble,” and process engineers are called in to put out the fire. They go to the line, get in the way of the operators, and collect data. Then, after arguing about these data, they are likely to tweak one or more of the process inputs. If the fraction nonconforming drops, they take credit for fixing the problem as they race off to the next emergency. If the process continues to be in trouble, then they do another process trial to try to find another input variable to tweak. Occasionally a problem will prove resistant to this sequence of process trials and a more systematic approach will be needed.

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