Do top managers still view financial performance as the sole indicator of success, despite mouthing platitudes about dazzled customers and fulfilled employees? Is there a point when reductions are done excessively in the name of squeezing out a few more percentage points of profit, moving companies from their “ideal weight” to a state of near-anorexia?

Shouldn’t success factors include happy customers, more motivated and committed workers, investment in communities, and concern for the environment? Is it possible to create a company whose objectives are worth sacrifice by those who work in it and by the society it serves?

Focusing solely on the numbers must be replaced by a philosophy of focusing on what drives the numbers. Happier customers require happier employees. Has today’s “bigger… better… faster… more… now” society come to the point where it is not only ignoring human needs, but demeaning them?

Work has become increasingly cerebral, and companies can’t treat new employees the way they treated those who worked with a pick and shovel—people won’t let themselves be treated like parts of a machine.

Click here to read part 1 of this series.

Analytic statistical methods are in very strong contrast with what is normally taught in most statistics textbooks, which describe the problem as one of “accepting” or “rejecting” hypotheses. In the real world of quality improvement, we must look for repeatability over many different populations. Walter Shewhart added the new concept of statistical control, which defines repeatability over time sampling from a process, rather than a population.

For example, the effectiveness of a drug may depend on the age of the patient, or previous treatment, or the stage of the disease. Ideally we want one treatment that works well in all foreseeable circumstances, but we may not be able to get it. Once we recognize that the aim of the study is to predict, we can see what range of possibilities are most important. We not only design studies to cover a wide range of circumstances, but to make the “inference gap” as small as possible.

As this is my last column for Quality Digest, I’m delighted to announce that my distinguished predecessor, Donald J. Wheeler, will be writing this column again. You’ll be in good hands.

When I got my master’s degree in statistics in 1980, jobs were plentiful for internal statistical consultants in corporate research and manufacturing. Their jobs involved educating scientists and engineers in the power of applied statistical methods, especially experimental design. Excellent research in applied methods was performed and collegially disseminated by distinguished groups, most notably DuPont, Kodak, and General Electric. I was part of a strong internal group at 3M. Those groups, for all intents and purposes, have now vanished.

Statistical process control (SPC) became more dominant during the late 1980s. Everything became “bigger, better, faster, more, now!” Suddenly there wasn’t time to do response surface experimental designs, except maybe 2² or 2³ factorials and a replication--if you were lucky. See “Using Design of Experiments as a Process Road Map” ( www.qualitydigest.com/feb06/articles/02_article.shtml ).

Suppose you had 16 months of data on an important process (as plotted on the run chart seen in figure 1). For improvement purposes, an intervention was made after the sixth observation to lower this key process indicator. This intervention is equivalent to creating a special cause for a desired effect.

There is no trend as defined statistically (despite the trend downward of length five from observations 5 to 9; with 16 data points, one would need length six, or five successive decreases). Neither is there any run of length eight either all above or below the median. So, would you want to conclude that the intervention had no effect? I doubt it!

Think of the median as a reference, and consider each data point as a 50-50 coin flip (heads = above the median; tails = below the median). The question is: Would you expect to flip a coin 16 times and obtain the specific pattern of seven heads (run of length seven above the median), immediately followed by seven tails (run of length seven below the median), then a head (run of length one above the median) and, finally, a tail (run of length one below the median)? Intuition would seem to say, “No.” Is there a statistical way to prove it?