## spctoolkit

by Donald J. Wheeler

### Description or Analysis?

Analysis discourages inappropriate actions by
filtering out the noise before potential signals are identified.

he supervisor of Department 17 has just been asked to write a report-the in-process inventory of his department was at an all-time high of 2,800 pounds last month. He had to explain this value at the next management meeting, so he began by analyzing the numbers.

First he looked at the current value of the inventory. The value of 2,800 pounds was 42 percent above the "plan value." It was also 12 percent above the value for the same month last year. There was no joy to be found in its current value.

Next he looked at the year-to-date average of the in-process inventory for Department 17. The value was 2,160 pounds, which was 9.6 percent above the plan and 5.9 percent above the year-to-date value for the same month last year-two more bad values.

Then the supervisor compared the percentage differences with the percentage changes in other departments. He prepared a bar graph for all the measures listed on the monthly report and discovered that the 42-percent value was the greatest percentage difference on the report. No luck here, either. In fact, having the greatest percentage difference, he realized that other managers would start the meeting by asking for his report.

No matter how he packaged the numbers, the story looked bad. While he was required to explain these values, he had no idea what to say. So he made up something that sounded plausible and which shifted the blame to forces beyond his control. He hoped no one would quiz him too closely on the findings in his report.

Sound familiar? It ought-to this little drama is acted out thousands of times each day. Of course, there are two problems with this "write a report" approach. The first is that these reports are usually works of fiction whose sole purpose is to enable some manager to pretend that something is being done about a perceived problem. The second is that the approach is based upon the assumption that the current value of the in-process inventory is actually a signal. But is it a signal-or is it just noise? How can you know?

Before you can detect signals within the data, you must first filter out the probable noise. And to filter out noise, you must start with past data. In short, the supervisor, with his limited comparisons, could not fully understand the current values, and he suffered the consequences of his ignorance.

The traditional analysis is nothing more than a collection of descriptive statistics. These days, most statistical analyses are little more than description. Bar graphs comparing unlike measures, pie charts showing proportions and rudimentary comparisons like those in the story above are more descriptive than anything else.

Descriptive measures are concerned with how much or how many. They provide no insight into why there are so many, or why there is so much. Because analysis focuses on answering "why" questions, we must analyze data in the context of the question and begin to separate the potential signals from the probable noise. Managers beginning the analysis process should start by looking at a measure in a time series plot, which should include methods for filtering out routine variation.

So what would the story have been for Department 17 if the manager had analyzed the values of the in-process inventory? Some of the past monthly in-process inventory values are seen on the X-chart in Figure 1. The limits on this chart define how large or small a single monthly value must be before its deviation from the historical average can be measured. Here, a monthly value in excess of 3,160 would be a signal that the amount of in-process inventory had risen. Likewise, a monthly value below 850 would signal a fall. In either case, you would be justified in looking for the cause of such movements.

The July value of 2,800 is not a signal. There is no evidence of any real change in the in-process inventory. This means that asking for an explanation of July's value was futile. There was nothing to explain. Department 17 had 2,800 because it was averaging 2,004, and the routine variation caused about half of the values to fall between 2,004 and 3,160. There is no other explanation for the value of 2,800. Anything else is pure fiction.

Some may feel disconcerted when they see limits that go from 850 to 3,160. Surely we can hold the in-process inventory more steadily than that! But that is precisely what cannot be done. At least it cannot be done unless some fundamental changes are made in the underlying process.

The natural process limits are the voice of the process. They define what the process will deliver as long as it continues to operate as consistently as possible. The way to calculate these limits was discussed in the January 1996 "SPC Toolkit."

When a process displays a reasonable degree of statistical control, it's operating as consistently as possible. The process doesn't care whether you like the natural process limits, and it certainly doesn't know what the specifications may be (specifications should be thought of as the voice of the customer, which is distinctly different from the voice of the process).

Therefore, if you are not pleased with the amount of variation shown by the natural process limits, then you must change the underlying process, rather than setting arbitrary goals, asking for reports, jawboning the workers or looking for alternative ways for computing the limits.

Mere description encourages inappropriate actions. It makes routine variation look like signals that need attention. In this case, there were no signals in the data, yet traditional ways of viewing the data didn't reveal this absence of signals. Analysis discourages inappropriate actions by filtering out the noise before potential signals are identified. The difference is profound.