The document discusses Statistical Process Control (SPC), a methodology used to measure and control quality during manufacturing processes by establishing control limits to identify variations before they lead to defects. It outlines the purpose of control charts in continuous quality control, emphasizing their role in monitoring process performance and guiding improvements. The paper also details the steps involved in SPC, tools used, and methods for collecting and analyzing data to ensure processes remain in control.

1. PROCESS MONITORING
Bindutesh Saner
Instrumentation and Control Engg.
Pravara Rural Engineering College, Loni
India
Abstract—Statistical Process Control (SPC) is an industry
standard methodology for measuring and controlling quality dur-
ing the manufacturing process. Attribute data (measurements)
is collected from products as they are being produced. By
establishing upper and lower control limits, variations in the
process can be detected before they result in defective product,
entirely eliminating the need for final inspection.
I. INTRODUCTION
Statistical Process Control is an analytical decision making
tool which allows you to see when a process is working
correctly and when it is not. Variation is present in any process,
deciding when the variation is natural and when it needs
correction is the key to quality control. Statistical Process
Control (SPC) charts offer users the chance to monitor the
very heartbeat of their processes. By collecting data they can
predict performance. Taking sample readings from a process
seems straightforward. Of does it? Look more closely. Do we
understand our process fully?
In manufacturing areas we probably do. In non-manufacturing
areas we may be less confident. And who collects the data?
What sample size is required? How often are samples taken?
These are vital questions to those intending to daily use the
control chart with a view to improving process performance,
particularly in nonmanufacturing, or service, areas where the
techniques are new.
The control chart has been with us since 1924. It has been
tried and proven, and accepted as a highly effective tool in
improving processes. In view of the fact that there is currently
renewed interest in Shewhart’s work, it is important to consider
how the control limits were originally set up. However, at the
end of the day, it is the logic and rules of collecting data
and interpreting the pattern of points on the chart that is the
important issue in understanding process behaviour and the
discovery of insights for process improvement.
II. PURPOSE OF CHART
Control charts are an essential tool of continuous quality
control. Control charts monitor processes to show how the
process is performing and how the process and capabilities
are affected by changes to the process. This information is
then used to make quality improvements.
Control charts are also used to determine the capability of the
process. They can help identify special or assignable causes
for factors that impede peak performance. How they work?
Control charts show if a process is in control or out of
control. They show the variance of the output of a process over
time, such as a measurement of width, length or temperature.
Control charts compare this variance against upper and lower
limits to see if it fits within the expected, specific, predictable
and normal variation levels.
If so, the process is considered in control and the variance
between measurements is considered normal random variation
that is inherent in the process. If, however, the variance falls
outside the limits, or has a run of non-natural points, the
process is considered out of control.
III. CONTROL CHARTS ARE KEY TO QUALITY
IMPROVEMENT
There are a handful of control charts which are commonly
used. They vary slightly depending on their data, but all have
the same general fundamentals
Control charts have four key features:-
• Data points are either averages of subgroup measurements
or individual measurements plotted on the x/y axis and
joined by a line. Time is always on the x-axis.
• The Average or Center Line is the average or mean of
the data points and is drawn across the middle section of
the graph, usually as a heavy or solid line.
• The Upper Control Limit (UCL) is drawn above the
centerline and often annotated as ”UCL”. This is often
called the + 3 sigma line. This is explained in more detail
in Control Limits on page 16.
• The Lower Control Limit (LCL) is drawn below the
centerline and often annotated as ”LCL”. This is called
the - 3 sigma line.
The x and y axes should be labeled and a title specified
for the chart.
IV. SPECIFIC SPC TOOLS AND PROCEDURES
The preparatory phases of SPC involve several steps using
a number of different tools. These tools are described below
and most are available in Statit QC. Eight quality tools
are available to help organizations to better understand and
improve their processes. The essential tools for the discovery
process are:
• Check Sheet
• Cause-and-Effect Sheet
• Flow Chart
• Pareto Chart
• Scatter Diagram
• Probability Plot

2. • Histogram
• Control Charts
• Brainstorming
There are numerous SPC software programs available such
as SPC for MS Excel, Minitab and SPC X from Air Academy
Associates.
V. STEPS INVOLVED IN STATISTICAL PROCESS CONTROL
Proper Statistical Process Control starts with planning and
data collection. Statistical analysis on the wrong or incorrect
data is rubbish, the analysis must be appropriate for the data
collected.
Be sure to PLAN, then constantly re-evaluate your situation
to make sure the plan is correct.
The key to any process improvement program is the PDSA
cycle described by Walter Shewart.
• PLAN:Identify the problem and the possible causes. The
QC tools described in this manual can help organizations
identify problems and possible causes, and to prioritize
corrective actions.
• DO:Make changes designed to correct or improve the
situation.
• STUDY:Study the effect of these changes on the situa-
tion. This is where control charts are used they show the
effects of changes on a process over time. Evaluate the
results and then replicate the change or abandon it and
try something different.
• ACT:If the result is successful, standardize the changes
and then work on further improvements or the next
prioritized problem. If the outcome is not yet successful,
look for other ways to change the process or identify
different causes for the problem.
Control charting is one of a number of steps involved in Sta-
tistical Process Control. The steps include discovery, analysis,
prioritization, clarification, and then charting. Before using
Statit QC software, appropriate data must be collected for
analysis. Then, you need to begin again and do it over and
over and over. Remember, quality is a CYCLE of continuous
improvement.
VI. HISTOGRAM AND CHART
Below Figure shows a typical set of readings obtained
by collecting samples from a process. Control charting
requires the mean of each sample to be used, rather than the
individuals. Figure 4 also shows the calculated values of the
mean X and range R.
The first stage in constructing a control chart for X requires
X, the mean, to be plotted against time as shown in below
Figure
The histogram corresponding to these X values is also
shown to the right of the chart. This pattem of points over
time results in a unique profile of points or histogram.
However, the reverse is not true. For example, if all that is
known is the histogram, what can we determine about the
Fig. 1. CHART
Fig. 2. control chart ploting
process? Very little, in fact. below figure illustrates just three
of the many patterns which all give the same histogram.
What the histogram does not tell us, therefore, is the manner
in which it was built up. We need a graph over time to
determine this, and these graphs, commonly called run charts,
are a first step in generating control charts.
Fig. 3. Histogram
VII. COMMON AND SPECIAL CAUSES
The form of control chart which we use today was first
generated by Shewhart. He recognised that if a process was
stable it was also predictable. i.e., once the natural variation of
the process has been determined, it is then possible to predict
future performance. This natural variation of the process does
not alter over time unless action is taken to change the system.
A process is defined as being stable if its natural variation is
due to common causes. The process is then said to be under
statistical control. If a process is unstable, that is because

3. unusual factors are operating on the process. These factors,
known as special causes, result in the process being out of
statistical control. Shewhart recognised that we make mistakes
at times, in that we take action when we should not do so.
Equally.
we sometimes let things drift, assuming the process will right
itself, when in fact we should react at the first sign of trouble.
Shewhart was therefore aiming to devise a rule which would
be sensitive enough to pick up a special cause, but not so
sensitive as to react to extremes in terms of common causes.
Take the figures plotted in Figure 5 as an example. It makes
sense to use a central value as a reference point. The best
measure of central location is the mean value, that is the
average obtained by adding all one hundred readings and
dividing the total by 100, giving:
733/100 = 7.33
In fact, this mean can be obtained much more directly. We
already have the values of the 20 sample means. Hence the
overall mean is given by the following calculation:
x/20 = 146.6/20 = 7.33
This mean of the sample means is known as the grand mean,
or x (X double bar),thus
x = 7.33
Figure 7 shows our run chart of x values together with a line
for x conventionally drawn as a broken line and often called
the central line. The question is now where do we draw lines
on the chart which will sensibly indicate the presence of
special causes.
Fig. 4.
VIII. COLLECTING THE DATA
Whatever the organisation, manufacturing or otherwise,
personnel should be responsible for monitoring their
own processes. Hence, in the same way as operators in
manufacturing industry collect data, their equivalents in
non-manufacturing - clerical assistants, clerks, technical
support staff, managers-should all collect sampled data for
their administrative processes.
This may cause problems initially since it is a change from
the tradition of looking for trends in data or comparing
the data of one period with another. Administrative people
recognise that data has traditionally been collected in order
to measure levels of productivity, rather than the inherent
capability of their processes.
There may be a natural reluctance to assist in an activity
which may have repercussions on their own employment.
This is understandable but management has the duty to
re-assure those who may be confused if process change is to
be achieved by the informed analysis of available data with
the intention of improvement action.
Fig. 5.
Properly handled, however, and in an environment of trust
and co-operation, many difficulties should be surmountable.
The insights for improvement available from the analysis of
such data are considerable and should not be overlooked by
any organisation determined to improve its performance and
customer service levels.
IX. CONCLUSION
You now have the basic fundamentals of statistical process
control. You should understand that it measures variance, and
is used to determine if a process is in control. You should
understand the different steps necessary in performing SPC,
including problem identification, data gathering, prioritization
and analysis. You should understand the key concepts and vo-
cabulary for using SPC charts. Finally you should understand
the different types of data and be able to select which control
chart to use, depending on the data you have gathered.
REFERENCES
[1] JOSE A ROMAGNOLI and AHMET PALAZOGLU, INTRODUCTION
TO PROCESS CONTROL, 3rd ed. Harlow, England: CRC Taylor and
Francis Group.
[2] DAVID HOWARD, THE BASICS OF STATISTICAL PROCESS CON-
TROL PROCESS BEHAVIOUR CHARTING