5th Edition PMBOK® Guide—Chapter 8: Seven Basic Quality Tools


 

1.  Introduction

The planning process in the Quality Management Area is process 8.1 Plan Quality Management.  Among the tools & techniques used in planning quality are the Seven basic quality tools (which collectively are listed as the third in the list of tools & techniques by the PMBOK® Guide for this process).  These seven basic quality tools can can also be used to monitor and control quality in process 8.3 Control Quality.

2.  The Seven Quality Tools

Here’s a description of the Seven Quality Tools used in Quality Management.

i.  Cause-and-Effect Diagrams

Also known as Ishikawa or fishbone diagrams.  The problem (or “special variation” in quality speak) is placed at the “head” of the fishbone and the various “bones” that come off of the “spine” of the fishbone some possible source of the problem.

ii.  Flowcharts

These are useful for diagramming the inputs, processing steps, and outputs that make up a process.  They can be used to better understand a process in order to determine which steps add value to the process and which ones do not (and which therefore can be eliminated).  They can also be used for estimating the cost of implementing quality or for estimating the cost of a failure or nonconformance.

iii.  Checksheets

Checksheets help collect useful data about a potential quality problem, including identifying defects.

iv.  Pareto Diagrams

Once the sources of a problem are identified (for example, through the cause-and-effect diagrams mentioned as tool #1), the Pareto Diagram is a bar chart arranged so that the sources that cause the most of a problem’s effects are listed to the left, and so on towards the right until you get a threshold under which you list the rest of the sources as “other”.

v.  Histograms

They are used to answer basic questions about the statistical distribution of quality data, for example:

–are they normally distributed, skewed in any particular direction?

–are they tightly distributed around a central mean, or loosely distributed?

vi.  Control Charts

When a measurement from a process are taken over time, you can see if the process stays within certain predetermined limits, called control limits, and if they remain randomly distributed around these limits (“random variation”) or if there is any sort of drift that takes place as a result of a “special variation”.

The control limits play the same function that the lane markers do on a highway, to help  drivers steer their cars between them.  This helps prevent cars from going off the road, which would be the equivalent of going beyond the “specification limits”.

vii.  Scatter diagrams

This compares two variables compared to see if changes in one variable are correlated with the changes in the other.  However, one of the basic rules of statistics is that “correlation is not causation”.  If  two variables are correlated, it may be because they are both related to a third variable which causes them both.

This was brought home to me in a psychology class, when the teacher showed a curve which showed for a series of rural French villages the number of storks seen in the village and the number of children both in the village that year.  There was a strong correlation between the two, but although I was a college freshmen, I already knew enough biology to know that an increase of storks were probably not the cause of the increased number of children.

Then I remembered a fact from my French class that storks (or cigognes in French) often nested in the chimneys of rural French houses because the shape and size were convenient for the storks to place their nests there.  That’s when I realized why there was no causation, although there was a correlation.  If there were more houses, there were more storks because there were more chimneys to place their nests in.  But if there were more houses, there were also more families, and therefore more children being born to them.  So both the number of storks and the number of children were dependent on the variable of the number of houses, but independent from each other.

3.  Quality and Health—An analogy

You can understand how the different quality tools are used for different purposes by making an analogy to the health of a patient.  A doctor sees a patient on a regular basis and does tests to see how the patient’s health stacks up against the baseline, which the doctor would figure out beforehand based on the patient’s age, sex, medical history, etc.  This is the monitoring that a doctor has to do.

What if a problem is detected, i.e., some of the medical tests show up some abnormalities?  Then the doctor has to control these problems by diagnosing what the cause is.  Once the doctor figures out the cause or causes, he or she can administer a cure.  Of course, then the doctor must go back and demonstrate that the cure has actually worked.

Then the doctor should try to encourage the patient to improve his or her health by eliminating those things which are bad for it and doing more things that improve it.

With this analogy in mind, let’s take a look at the seven quality tools.

 

  Quality Tool Purpose Medical analogy
1. Ishikawa or fishbone

(cause-and-effect) diagrams

For finding root cause of quality problems Diagnosing cause of health problems
2. Flowcharts For analyzing processes as step towards improving them Improving patient health on ongoing basis (exercise, etc.)
3. Checksheets For collecting data on a quality problem Monitoring patient health
4. Pareto diagrams Identifies sources that are responsible for the most quality problems Diagnosing cause of health problems
5. Histograms Describes the statistical distribution of quality data Analyzing patient health data
6. Control charts Determines whether the process is stable or unstable Monitoring patient health
7. Scatter diagrams Used to indicate correlation between variables Diagnosing cause of health problems

The next post will discuss some of the additional quality tools listed for process 8.1 Plan Quality Management

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