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Chaos Re-visited -- Dickeson

December 2001
Some years ago I wrote a couple of columns on "Chaos Theory" and its impact on printing production. "Chaos," by James Gleick in 1987, was a classic book that explained why we can't forecast the weather, the stock market or the economy.

Too many seemingly tiny variables interacting and causing major changes in results, he told us. The oft-quoted example is that of a butterfly flapping its wings in a Brazilian rain forest and causing major storms in New York a week later.

We've known all about chaos in printing for years. We establish standards that are predictions of how a job will run in our plant. We use that prediction to quote a price, schedule work and order materials. We do this knowing full well that the standard can't forecast what will happen on Job 234 on third shift on Press 23. Too many interacting variables that cause major changes in results.

At one time we spoke of five "families" of variables: Materials, People, Equipment, Environment and Form-sequencing. The families interact both internally and externally. Not exactly wing flaps of Brazilian butterflies, but similar impacts on print production results.

So, I wrote another column titled, as I recall, Home on the Range, suggesting that we use standard ranges rather than single digit standards of time and materials. It had all the impact of lint falling from one's navel! "Too sophisticated for printers," I was told. "It screws up the pricing markups," others joked. It was true. It did mess up long-established standards assumptions and I offered no alternatives.

Finally, last year, "Understanding Variation—the Key to Managing Chaos," by Donald Wheeler was published. My buddy Peter Brehm said, "Rog, get this book and you'll never look at data in the same way again!" Peter was right. Get the book and it will change your thinking. Guaranteed. But be warned. You'll immediately want to make changes in production followed by some new administrative controls.

The secret of the book, illustrated by dozens of examples, lies in the use of a pair of charts called XmR. On a continuously moving basis the XmRs filter out the chaos noise and identify special disruptions of production. By constantly removing the disruptive causes, the process is continuously improved.

Common Causes of Chaos

At the same time, the day-to-day variances that fall within the limits are identified as inherent in the process. If you would modify these process variances you must change the system, says John Compton of the Fort Dearborn Co. You don't affect process variances by goal setting, imposing quotas, bonuses, threats and the like. This "common cause" variance is a product of chaos.

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