Quality
Tools
Continuous quality
improvement process assumes and even demands that a team of experts in field as
well as a company leadership actively use quality tools in their improvement
activities and decision making process. Quality tools can be used in all phases
of production process, from the beginning of product development up to product marketing
and customer support. At the moment there are a significant number of quality
assurance and quality management tools on disposal to quality experts and
managers, so the selection of most appropriated one is not always an easy task.
In the conducted research it is investigated possibilities of successful
application of 7QC tools in several companies in power and process industry as
well as government, tourism and health services. The seven quality tools are:
1. Cause
and Effect Diagrams
2. Flow
Charts
3. Checklists
4. Control
Charts
5. Scatter
Diagrams
6. Pareto
Analysis
7. Histograms
You
can see that TQM places a great deal of responsibility on all workers. If
employees are to identify and correct quality problems, they need proper
training. They need to understand how to assess quality by using a variety of
quality control tools, how to interpret findings, and how to correct problems.
In this section we look at seven different quality tools. These are often
called the seven tools of quality control and they are easy to understand, yet
extremely useful in identifying and analyzing quality problems. Sometimes
workers use only one tool at a time, but often a combination of tools is most
helpful.
Cause and Effect
Diagrams
Cause
and effect diagrams are charts that identify potential causes for particular
quality problems. They are often called fishbone diagrams because they look
like the bones of a fish. The “head” of the fish is the quality problem, such
as damaged zippers on a garment or broken valves on a tire. The diagram is
drawn so that the “spine” of the fish connects the “head” to the possible cause
of the problem. These causes could be related to the machines, workers,
measurement, suppliers, materials, and many other aspects of the production
process. Each of these possible causes can then have smaller “bones” that
address specific issues that relate to each cause. For example, a problem with
machines could be due to a need for adjustment, old equipment or tooling
problems. Similarly, a problem with workers could be related to lack of
training, poor supervision, or fatigue. Cause and effect diagrams are problem solving
tools commonly used by quality control teams. Specific causes of problems can
be explored through brainstorming. The development of a cause and effect
diagram requires the team to think through all the possible causes of poor
quality.
Flow Charts
A
flowchart is a schematic diagram of the sequence of steps involved in an
operation or process. It provides a visual tool that is easy to use and
understand. By seeing the steps involved in an operation or process, everyone
develops a clear picture of how the operation works and where problems could
arise.
Checklists
A
checklist is a list of common defects and the number of observed occurrences of
these defects. It is a simple yet effective fact finding tool that allows the
worker to collect specific information regarding the defects observed. This
means that the plant needs to focus on this specific problem; for example, by
going to the source of supply or seeing whether the material is the issue;
during a particular production process. A checklist can also be used to focus
on other dimensions, such as location or time. For example, if a defect is
being observed frequently, a checklist can be developed that measures the
number of occurrences per shift, per machine, or per operator. In this fashion
we can isolate the location of the particular defect and then focus on correcting
the problem.
Control Charts
Control
charts are a very important quality control tool. These charts are used to
evaluate whether a process is operating within expectations relative to some
measured value such as weight, width, or volume. For example, we could measure
the weight of a sack of flour, the width of a tire, or the volume of a bottle
of soft drink. When the production process is operating within expectations, we
say that it is “in control.” To evaluate whether or not a process is in
control, we regularly measure the variable of interest and plot it on a control
chart. The chart has a line down the center representing the average value of
the variable we are measuring. Above and below the center line are two lines,
called the upper control limit (UCL) and the lower control limit (LCL). As long
as the observed values fall within the upper and lower control limits, the
process is in control and there is no problem with quality. When a measured
observation falls outside of these limits, there is a problem.
Scatter Diagrams
Scatter
diagrams are graphs that show how two variables are related to one another.
They are particularly useful in detecting the amount of correlation, or the
degree of linear relationship, between two variables. For example, increased
production speed and number of defects could be correlated positively; as
production speed increases, so does the number of defects. Two variables could
also be correlated negatively, so that an increase in one of the variables is
associated with a decrease in the other. For example, increased worker training
might be associated with a decrease in the number of defects observed.
The
greater the degrees of correlation, the more linear are the observations in the
scatter diagram. On the other hand, the more scattered the observations in the
diagram, the less correlation exists between the variables. Of course, other
types of relationships can also be observed on a scatter diagram, such as an
inverted U. This may be the case when one is observing the relationship between
two variables such as oven be the case when one is observing the relationship
between two variables such as oven temperature and number of defects, since
temperatures below and above the ideal could lead to defects.
Pareto Analysis
Pareto
analysis is a technique used to identify quality problems based on their degree
of importance. The logic behind Pareto analysis is that only a few quality
problems are important, whereas many others are not critical. The technique was
named after Vilfredo Pareto, a nineteenth century Italian economist who
determined that only a small percentage of people controlled most of the wealth.
This concept has often been called the 80 – 20 rule and has been extended into
many areas. In quality management the logic behind Pareto’s principle is that
most quality problems are a result of only a few causes. The trick is to
identify these causes.
One
way to use Pareto analysis is to develop a chart that ranks the causes of poor
quality in decreasing order based on the percentage of defects each has caused.
For example, a tally can be made of the number of defects that result from
different causes, such as operator error, defective parts, or inaccurate
machine calibrations. Percentages of defects can be computed from the tally and
placed in a chart. We generally tend to find that a few causes account for most
of the defects.
Histograms
A
histogram is a chart that shows the frequency distribution of observed values
of a variable. We can see from the plot what type of distribution a particular
variable displays, such as whether it has a normal distribution and whether the
distribution is symmetrical.
In
the food service industry the use of quality control tools is important in
identifying quality problems. Grocery store chains must record and monitor the
quality of incoming produce, such as tomatoes and lettuce. Quality tools can be
used to evaluate the acceptability of product quality and to monitor product
quality from individual suppliers. They can also be used to evaluate causes of
quality problems, such as long transit time or poor refrigeration. Similarly,
restaurants use quality control tools to evaluate and monitor the quality of
delivered goods, such as meats, produce, or baked goods.
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