A picture is worth thousand words...

I always thought that pictures were more effective and efficient in communicating a message. People around the world come up with more and more innovative ways to present business data as pictures that are easy to read and convey the message quickly.

In the Quality world, I deal with pictures in the form of charts(SPC charts, affinity diagrams, relation diagrams etc) or maps (SIPOC maps, VSM etc). These pictures (charts, diagrams and maps) capture lot of meaningful information about the
quality of the product or service. However, many quality practitioners struggle with proper interpretations of these pictures and fail to take necessary action to improve quality.

Control charts for example are designed to communicate what's happening in the process. They provide signals that indicate the presence of special causes in the process. All we have to do is pick up these signals and act on them!

Rather than looking through paper reams full of data that could take hours, pictures tell us the real story in just a few minutes.
A picture is truly worth a thousand words...but will be effective only if it is looked at by the right people, interpreted correctly and acted upon.

Do quality charts or maps help you find quality problems in your process?
Have you improved a process by acting on signals from SPC charts?

Is my data variable or attribute?

From my training class last week, I had an interesting question from one of my class students who was from the healthcare industry.

The student asked "I measure success of tests conducted by lab personnel. For example, I monitor number of blood tests were conducted every day by each lab assistant. I also monitor how many attempts were required for the assistant to successfully draw blood and complete the test.
I want to plot an SPC chart for the number of successful blood draws in single attempt. Should I use variable or attribute control charts?"

My answer to her was a series of questions..
Question 1: What do want the SPC chart to tell you?
Answer : Number of successful blood draws in single attempt.
Also, success rate of each lab personnel.

Question 2: Are either of the above measurements or counts?
Answer : They are counts.

Question 3: What SPC chart helps chart count data?
Answer: Attribute control charts

My student was able to answer her own question after breaking down her problem in small sections.

Have you struggled with data types?
Do you have questions about type of SPC charts you should use?
Please visit our tutorial on "SPC in healthcare" at www.sybeq.com/Tutorial.aspx

Difference between p, percent and ppm

In the Quality world, practitioners may choose to measure quality on different scales. Some may use the p or the proportion metric, others may choose percent or ppm calculation.

What is the difference between p, percent or ppm metric?

There is a fundamental difference between the proportion, percentage, ppm calculations. And that is the base each of these calculations use. The proportion calculation uses a base of 1, percent uses a base of 100 and ppm uses a base of million.

For example, if I had to find proportion, percent and ppm for 5 defective parts found in a lot of 300..
proportion defective = 5/300 = 0.0167
percent defective = (5/300) x 100 = 1.67%
defective ppm = (5/300) x 1000000 = 16666.67ppm

The ratio is the same 5/30, but the multiplier changes when calculating proportion, percent or ppm values for defectives found in a lot.

What metric do you use for measuring quality of your product/service?
Which metric is better - p, percent or ppm?
Please share your views via comments on this blog or email us at info@sybeq.com

Can defects be more than defectives found?

In my training class, I explain the difference between defects and defectives using everyday examples. That way it is simple for the participants to understand these two categories for discrete data.

I had an interesting discussion with one of the participants from my training class last month. He was following well when I explained defects and defectives with examples. However, when we plotted the SPC chart, we entered the number 50 for the quantity inspected. Then we started inputing data for each defect category. We ended up with a count of 54.

This participant raised a question that practically this would never happen. When I asked him "Why he thought so?"
His reasoning was simple!
You cannot have defects more than the number of parts inspected.

After spending several minutes with him I was able to convince him otherwise.
The fact that a single defective product can have multiple defects is sometimes very hard to understand!