Control Charts simply explained - Statistical process control - Xbar-R Chart, I-MR Chart,...
Summary
TLDRThis video script introduces control charts as statistical tools for monitoring and controlling processes by tracking key variables over time. It explains the purpose of control charts, such as identifying trends and unusual patterns, and provides an example using an x-bar R chart in a fulfillment center. The script also discusses different types of control charts for continuous and discrete data, including x-bar R, I-MR, NP, P, C, and U charts, and how to choose the appropriate chart based on data format and variability.
Takeaways
- 📊 Control charts are statistical tools used for monitoring and controlling processes by tracking key variables over time.
- 🔍 They help identify trends, shifts, or unusual patterns that may indicate a problem with the process.
- 📈 A decision tree can be used to determine the appropriate type of control chart based on the format of the available data.
- 📚 An example of an x-bar R chart is used to monitor the average processing time in a fulfillment center to ensure efficiency.
- 📝 Data for control charts is obtained by taking random samples and measuring key variables, such as processing time in the given example.
- 📉 The x-bar chart involves plotting mean values over time, with the center line representing the overall mean and control limits indicating process variation boundaries.
- 📌 The upper control limit (UCL) and lower control limit (LCL) are typically set at three standard deviations from the mean, representing the process's upper and lower variability thresholds.
- 🔢 Sigma, or standard deviation, can be calculated in different ways, with some methods being more accurate or straightforward.
- 📉 The R chart, often used alongside the x-bar chart, plots the range of values each day to provide additional insight into process variability.
- 🔄 The individual moving range (IMR) chart is used when there is only one observation per point in time, plotting the difference between consecutive points.
- 🔢 Discrete data control charts, such as the NP, p, C, and u charts, are used for processes with defects or events counted, and they can accommodate both constant and variable sample sizes.
Q & A
What are control charts and why are they important?
-Control charts are statistical process control tools used to monitor and control processes by tracking the performance of key variables over time. They are important because they help identify trends, shifts, or unusual patterns that might indicate a problem with the process, providing crucial information about the stability of the process.
How does a control chart differ from a decision tree?
-A control chart is a tool for monitoring process stability and identifying issues, while a decision tree is a flowchart-like structure used to help decide which control chart to use based on the format of the available data. The decision tree guides the selection of the appropriate control chart.
What is an x-bar (x̄) R chart and how is it used?
-An x-bar (x̄) R chart is a type of control chart used to monitor the average (x̄) and range (R) of a process over time. It is used to ensure that the process remains stable and within acceptable limits, such as the average processing time in a fulfillment center.
How is the data for an x-bar (x̄) R chart collected?
-Data for an x-bar (x̄) R chart is collected by taking random samples of the process at regular intervals. For example, in a fulfillment center, the processing time of five orders per day could be measured over a period of 25 days.
What are the three lines on an x-bar (x̄) R chart and what do they represent?
-The three lines on an x-bar (x̄) R chart are the center line, the upper control limit (UCL), and the lower control limit (LCL). The center line represents the mean value of all data points, the UCL is set at three standard deviations above the mean and indicates the upper boundary of process variation, and the LCL is set at three standard deviations below the mean, representing the lower bound of process variation.
What is the difference between control limits and specification limits?
-Control limits are based on process variability and statistical calculations, indicating the natural variation within a process. Specification limits, on the other hand, are defined by customer requirements or engineering tolerances and represent the acceptable range of product characteristics from a quality perspective.
What is an I-MR chart and when is it used?
-An I-MR chart, which stands for individual moving range chart, is used when there is only one observation at each point in time. It plots individual data points over time and uses the moving range (the difference between consecutive points) to monitor process stability.
How is the moving range calculated in an I-MR chart?
-The moving range in an I-MR chart is calculated as the difference between consecutive data points. For example, if the first and second data points are 10 and 11, respectively, the moving range would be 1.
What are the types of control charts for discrete data?
-Control charts for discrete data include the np chart (for constant sample size and one defect per unit), the p chart (for variable sample size and one defect per unit), the c chart (for multiple defects per unit and constant sample size), and the u chart (for multiple defects per unit and variable sample size).
When would you use an np chart instead of a p chart?
-An np chart is used when monitoring the number of defects in a process with a constant sample size and one defect per unit, such as when counting the number of defective light bulbs produced each day with a sample of 10 bulbs.
How can you create control charts for discrete data using a data tab?
-To create control charts for discrete data using a data tab, you can input your data into a table, select the appropriate variables, and use statistical process control tools to automatically generate the desired control chart, such as an np, p, c, or u chart.
Outlines
📊 Introduction to Control Charts
This paragraph introduces control charts as statistical process control tools used to monitor and control processes by tracking key variables over time. It explains the purpose of control charts, which is to identify trends, shifts, or unusual patterns indicating process problems. The paragraph also mentions the use of a decision tree to select the appropriate type of control chart based on data format. An example of an Xbar-R chart in a fulfillment center is provided to illustrate the monitoring of average processing time, including how to collect data, calculate mean values, and plot the chart with control limits. The difference between control limits and specification limits is highlighted, with control limits being based on process variability and statistical calculations, while specification limits are set by customer requirements or engineering tolerances.
📈 Exploring Different Types of Control Charts
The second paragraph delves into the variety of control charts, starting with the Xbar-R chart and moving on to the I-MR chart, which is used when only one observation per time point is available. The I-MR chart calculates and plots the moving range between consecutive points. The paragraph then discusses control charts for discrete data, differentiating between charts for one defect per unit (NP chart) and multiple defects per unit with constant or variable sample sizes (C and U charts, respectively). Examples from light bulb manufacturing and car production illustrate the application of these charts. The paragraph also explains the difference between constant and variable sample sizes and how they affect the choice of control chart.
🛠️ Creating Control Charts for Discrete Data
The final paragraph focuses on creating control charts for discrete data, such as defects in a process. It outlines the steps for creating control charts using data, including selecting the appropriate type of chart based on the nature of the data (e.g., constant or variable sample size). The paragraph provides guidance on using online tools to create control charts by inputting measured values and specifying sample sizes. It concludes with a mention of the availability of instructions for creating various control charts, encouraging viewers to explore these resources further.
Mindmap
Keywords
💡Control Charts
💡Statistical Process Control
💡Trends
💡Shift
💡Unusual Patterns
💡Xbar-R Chart
💡Mean
💡Upper and Lower Control Limits (UCL and LCL)
💡Standard Deviation
💡Specification Limits
💡I-MR Chart
💡Discrete Data
💡Constant Sample Size
💡Variable Sample Size
💡Defects per Unit
💡Control Chart for Discrete Data
Highlights
Control charts are statistical process control tools used to monitor and control processes by tracking key variables over time.
They help identify trends, shifts, or unusual patterns indicating potential process problems.
A decision tree is provided to determine the appropriate type of control chart based on data format.
The x-bar R chart is an example of a control chart used in quality management to monitor average processing time.
Data for control charts is obtained by taking random samples and measuring key variables like processing time.
The x-bar chart involves plotting mean values over time with calculated upper and lower control limits.
The upper control limit (UCL) and lower control limit (LCL) are set at three standard deviations from the mean.
Sigma can be calculated in different ways, with the simplest being the standard deviation of all data.
Control limits are based on process variability and statistical calculations, distinct from specification limits defined by customer requirements.
The R chart extends the x-bar chart by plotting the range of values each day to monitor process stability.
The individual moving range (IMR) chart is used when only one observation per time point is available.
The IMR chart calculates and plots the difference between consecutive points instead of a range.
Discrete data control charts monitor the number of defects in a process, with options for one defect per unit or multiple defects.
The NP chart is used for monitoring the proportion of defective units with a constant sample size.
The p chart is used when there is one defect per unit but with a variable sample size.
The C chart is used for monitoring the total number of defects per unit when multiple defects are possible.
The u chart is suitable for monitoring defects per unit with a variable sample size and multiple defects.
Control charts for discrete data can be created online using data input and selecting the appropriate variables.
A list of control charts with instructions on how to create them is provided for both continuous and discrete data.
Transcripts
this video is about control charts first
we discuss what control charts are and
why you need them then we are going to
explore the different types of control
charts so what are control charts
control charts are a type of statistical
process control tool used to Monitor and
control processes by tracking the
performance of key variables over time
they help identify Trends shift
or any unusual patterns that might
indicate a problem with the process
therefore control charts give important
information about the stability of the
respective process depending on the
format in which the data is available
different types of control charts will
be used to find out which one is right
for you you can use this decision tree
we will go through this decision Tree in
more detail later don't worry it's not
complicated but what are control charts
used for to understand this let's first
look at an example of an xar R chart
let's say we work in quality management
at a fulfillment center in a fulfillment
center products are stored packed and
shipped to customers the primary purpose
of a fulfillment center is to ensure
that orders are processed efficiently
therefore the stability of this process
should be monitored for the this purpose
the time from order receipt to shipment
is measured so our objective is monitor
the average processing time to ensure it
stays within acceptable limits of course
we need data to monitor the process to
obtain data we take a random sample of
five orders per day so at the first day
we measure a processing time of five
orders for example the processing time
for the first order was 12 minutes the
processing time for the second order was
14 minutes and so on and so forth
similarly we measure the processing time
on the second day on the third day and
so on let's say we measure the times on
a total of 25 days but how do we get an
expired chart with this data to do this
we first calculate the mean values of
the five orders from all 25 days now we
can create the xar chart to do this we
plot The 25 Days on the xaxis and the
mean values we just calculated on the Y
AIS so we have the first point the
second the third and so on and so
forth now we are almost finished we only
need to calculate these three lines the
center line is simply the mean value of
all values so we just calculate the mean
value of all points the red lines are
the upper and lower
limits the upper control limit UCL is
the threshold above the central line
usually set at three deviations from the
mean it indicates the upper boundary of
process variation the lower control
limit LCL is the threshold below the
central line also set at three standard
deviations from the mean it represents
the lower bound rate of process
variation note there are different ways
to calculate the sigma some are a more
accurate approximation others are a more
straightforward way to calculate it the
simplest way to calculate Sigma is to
calculate the standard deviation of all
data in addition keep in mind that there
is a difference between control limits
and specification limits control limits
are based based on process variability
and statistical calculations whereas
specification limits are defined by
customer requirements or engineering
tolerances now we have the so-called xar
chart in most cases the xar chart is
extended by the r chart R stands for
range to create the r chart we simply
calculate the range of each day for
example on day one the low lowest value
is 12 and the highest is 15 so we get
the range of three we can now plot these
values 3 3 3 5 and so on until the end
three if you want to calculate an xar R
control chart with data tap simply copy
your data into this table and click on
statistical process control now you only
need to select the variables below and
you will get an xar R chart your data
can also be available in such a
format now we know what an xar chart is
but what about the other types let's
start with the IMR chart the IMR chart
stands for individual moving range chart
but what is the difference with the xar
r chart in the case of the xar r chart
we have several observations at each
point in time if if we do not have
several observations at each point in
time but only one we use an IMR chart so
to create the IMR chart we simply draw
the corresponding value at each point in
time since we have only one value per
point in time we cannot calculate the
range therefore we use a moving range in
the moving range chart we calculate and
plot the difference between the conse
itive points such as the difference
between two successive points between a
first and second point for example we
have a difference of one between the
second and the third point we have a
difference of two so we entered a point
at two here to create an IMR chart with
data tab copy your measured values into
this table again if you now simply click
on one variable an IMR chart will be
created automat ically now that we've
discussed the control chart for
continuous data what if we have discrete
data in the control charts of the
discrete data we look at the number of
defects in a process here we
differentiate between one defect per
unit or several defects per unit we'll
go through what that means in a moment
in addition in both cases we
differentiate between constant sample
size and variable sample size let's say
you work for a company that manufactures
light bulbs now you want to monitor the
proportion of defective bulbs produced
each day to do this you take a random
sample of 10 light bulbs each day and
count the number of defective light
bulbs the first day two were defective
on the second one on the third three and
so on and so forth okay hopefully in
reality there are far fewer defects and
Sample should therefore be larger in
this case we have one defect per unit a
bulb is either defective or not and we
have a constant sample size so we use an
NP chart so the NP chart is used to plot
the defect counts over time to identify
Trends or shifts in a defect rate on the
first day for example two lamps were
defective on the second day one was
defective and so on now the question is
what is the difference between constant
sample size and variable sample size in
our example with the lamps we took a
constant sample size every day now we
could also have a machine that randomly
sorts out a lamp from time to time one
day it sorts out 155 lamps the next day
180 lamps then 121 one and so on and so
forth in this case our sample would
contain a different number of lamps each
day and we would have a variable sample
size to get the error rate we then have
to divide the number of 40 lamps by the
number of randomly drawn lamps so we
have one def fact per unit and a
variable sample size we therefore use a
p chart to create a p chart we need two
columns one with the samp example size
and one with the number of defs found
with these values we can now calculate
the proportions and plot the values what
about multiple defects per unit let's
say a car production plant wants to
monitor the number of defects found in
each car body produced to maintain high
quality standards each day one car body
produced is inspected and the total
number of defects per car body is
recorded in this case we use the C chart
to draw the C chart we simply need the
number of defects found per car for
example four defects were found in the
first car so we enter a point at four
but what is an example of a u chart
there we have several defects per unit
and variable sample size imagine a
software development team wants to
monitor the number of B Max per software
release the individual releases are of
course different in size one way to
measure the scope is to measure the
number of lines of code added so we have
a column with the number of lines of
code and the number of reported box this
allows us to calculate the Box per line
of code with this data we can now create
a up plot of course you can also create
a control chats online with data step
for discrete data to do this simply
click on attributive now you can either
select one or more effects select the
measured values and then either specify
a constant sample size or specify the
variable with the sample size the
correct control chart will then be
displayed automatically below you can
see a list of the control charts with
instructions on how to create them
thanks for watching and I hope you
enjoyed the video
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