Hello Friends,
It is very necessary to know basic concepts before starting the six sigma study. I already covered some of them. In this video, I am going to explain “Control
Chart” which is very important and used everywhere in any kind of process to study
how a process changes over time. The control chart was invented by Walter A.
Shewhart while working for Bell Labs in the 1920s. The company's engineers had been seeking to
improve the reliability of their telephony transmission systems. Because amplifiers and other equipment had
to be buried underground, there was a stronger business need to reduce the frequency of failures
and repairs. By 1920, the engineers had already realized
the importance of reducing variation in the manufacturing process. Moreover, they had realized that continual
process-adjustment in reaction to non-conformance actually increased variation and degraded
quality. Shewhart framed the problem in terms of Common-
and special-causes of variation and, on May 16, 1924, wrote an internal memo introducing
the control chart as a tool for distinguishing between the two. Shewhart created the basis for the control
chart and the concept of a state of statistical control by carefully designed experiments. While Shewhart drew from pure mathematical
statistical theories, he understood that data from physical processes typically produce
a "normal distribution curve" (also commonly referred to as a "bell curve"). He discovered that observed variation in manufacturing
data did not always behave the same way as data in nature. Shewhart concluded that while every process
displays variation, some processes display controlled variation that is natural to the
process, while others display uncontrolled variation that is not present in the process
at all times. So, let’s begin the learning of the Control
chart……. CONTROL CHART
The control chart is a graph used to study how a process changes over time. Data are plotted in time order. A control chart always has a central line
for the average, an upper line for the upper control limit, and a lower line for the lower
control limit. These lines are determined from historical
data. By comparing current data to these lines,
you can draw conclusions about whether the process variation is consistent (in control)
or is unpredictable (out of control, affected by special causes of variation). I will explain the concepts of normal cause
variation and special cause variation and how to detect special cause presence with
examples after some time. When to Use a Control Chart
• When controlling ongoing processes by finding and correcting problems as they occur. • When predicting the expected range of
outcomes from a process. • When determining whether a process is
stable (in statistical control). • When analyzing patterns of process variation
from special causes (non-routine events) or common causes (built into the process). • When determining whether your quality
improvement project should aim to prevent specific problems or to make fundamental changes
to the process. Before going to the selection of chart type
and procedure to create a Control chart, let’s discuss some of the important concepts related
to it. What are control limits? The control limits of your control chart represent
your process variation and help indicate when your process is out of control. Control limits are the horizontal lines above
and below the centre line that are used to judge whether a process is out of control. The upper and lower control limits are based
on the random variation in the process. For example, this Xbar chart displays the
length of manufactured camshafts over time. Two points are above the upper control limit. These out-of-control points indicate that
the camshafts in these subgroups are longer than expected. Do not confuse control limits with specification
limits. Control limits are based on process variation. Specification limits are based on customer
requirements. A process can be in control and yet not be
capable of meeting specifications. What is the center line on a control chart? The centerline of your control chart represents
your actual process average, not necessarily your desired process average. The centerline is the horizontal reference
line on a control chart that is the average value of the charted quality characteristic. Use the centerline to observe how the process
performs compared to the average. If a process is in control, the points will
vary randomly around the centerline. For example, this Xbar chart displays the
length of manufactured camshafts over time. The centerline shows the process mean. The subgroup means vary randomly around the
process mean. Do not confuse the centerline with the target
value for your process. The target is your desired outcome, not the
actual outcome. Variation:
Every piece of data which is measured will show some degree of variation. No matter how much we try, we would never
attain identical results for two different situations: each result will be different
from the other. Variation may be defined as ‘the numerical
value used to indicate how widely individuals in a group vary’. Common Cause variation and Special cause variation:
Control charts are used to monitor two types of process variation, common-cause variation,
and special-cause variation. Some degree of variation will naturally occur
in any process. Common-cause variation is the natural or expected
variation in a process. Special-cause variation is an unexpected variation
that results from unusual occurrences. It is important to identify and try to eliminate
special-cause variation. Out-of-control points and non-random patterns
on a control chart indicate the presence of special-cause variation. Let’s take an example. Write the letter “a” three times using
your dominant hand, three times with your other hand, then two times with your dominant
hand. Make all of them the same. Are they the same? Why or why not? • The dominant hand-created variation because
of the pen and/or paper you used, amount of coffee you’ve had, friction of your hand,
etc., etc. This is a common cause variation. It is inherent in the process. • Using the non-dominant hand clearly shows
something very different. This is the special cause—it does not always
happen in the process of writing. •
Using tests for special causes in control charts • Nelson rules are a method in process control
of determining if some measured variable is out of control. Rules, for detecting "out-of-control" or non-random
conditions were first postulated by Walter A. Shewhart in the 1920s. The Nelson rules were first published in the
October 1984 issue of the Journal of Quality Technology in an article by Lloyd S Nelson. • The rules are applied to a control chart
on which the magnitude of some variable is plotted against time. The rules are based on the mean value and
the standard deviation of the samples. • The dashed horizontal lines in the following
illustrations represent distances of 1σ and 2σ from the centerline. • Test 1: One point more than 3σ from the
centerline • Test 1 evaluates the pattern of variation
for stability. Test 1 provides the strongest evidence of
lack of control. If small shifts in the process are of interest,
Tests 2, 5, and 6 can be used to supplement Test 1 to create a control chart with greater
sensitivity. • Test 2: Nine points in a row on the same
side of the centerline • Test 2 evaluates the pattern of variation
for stability. If small shifts in the process are of concern,
Test 2 can be used to supplement Test 1 to create a control chart with greater sensitivity. • Test 3: Six points in a row, all increasing
or all decreasing • Test 3 detects a trend or continuous movement
up or down. This test looks for a long series of consecutive
points without a change in direction. • Test 4: Fourteen points in a row, alternating
up and down • Test 4 detects the presence of a systematic
variable. The pattern of variation should be random,
but when a point fails Test 4 it means that the pattern of variation is predictable. • Test 5: Two out of three points more than
2σ from the centerline (same side) • Test 5 evaluates the pattern of variation
for small shifts in the process. • Test 6: Four out of five points more than
1σ from the centerline (same side) • Test 6 evaluates the pattern of variation
for small shifts in the process. • Test 7: Fifteen points in a row within
1σ of centerline (either side) • Test 7 identifies a pattern of variation
that is sometimes mistaken as a display of good control. This type of variation is called stratification
and is characterized by points that follow the centerline too closely. • Test 8: Eight points in a row more than
1σ from the centerline (either side) • Test 8 detects a mixture pattern. A mixture pattern occurs when the points tend
to avoid the center line and instead fall near the control limits. Which tests should I use to detect specific
patterns of special-cause variation? Apply certain tests based on your knowledge
of the process. If it is likely that your data might contain
particular patterns, you can look for them by choosing the appropriate test. Adding more tests makes the chart more sensitive,
but may also increase the chance of getting a false signal. When you use several tests together, the chance
of obtaining a signal for lack-of-control increases. As this is a very critical concept, l am taking
little time to explain the Control chart, so that you can understand it in a better
way. Please have a study of it, so that later part
will be easy for you. The remaining part like types of control chart,
how to select control chart based on data, which test you should select in determining
special cause based on data to avoid false signal and thereby to avoid tempering to process
and many more in the next video.
