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spc
Statistical process control Key Quality characteristic :Forecast Error for demand
BENEFITS of SPC
Monitors and provides feedback for keeping processes in control.
Triggers when a problem occurs
Differentiates between problems that are correctable and those
that are due to chance.
Gives you better control of your process.
Reduces need for inspection
Provides valuable knowledge in the working of the process
Have you ever…
Shot a rifle? Played darts? Played basketball? Shot a round of golf?
What is the point of these sports?
What makes them hard?
Have you ever…
Shot a rifle? Played darts? Shot a round of golf? Played basketball?
Player A
Player B
Who is the better shot?
Discussion
What do you measure in your process? Why do those measures matter? Are those measures consistently the same? Why not?
Variability
Deviation = distance between observations and the mean (or average)
Player A
Player B
Observations
10
9
8
8
7
averages 8.4
Deviations
10 - 8.4 = 1.6
9 – 8.4 = 0.6
8 – 8.4 = -0.4
8 – 8.4 = -0.4
7 – 8.4 = -1.4
0.0
871089
Variability
Deviation = distance between observations and the mean (or average)
Emmett
Jake
Observations
7
7
7
6
6
averages 6.6
Deviations
7 – 6.6 = 0.4
7 – 6.6 = 0.4
7 – 6.6 = 0.4
6 – 6.6 = -0.6
6 – 6.6 = -0.6
0.0
76776
Variability
Variance = average distance between observations and the mean squared
Emmett
Jake
Observations
10
9
8
8
7
averages 8.4
Deviations
10 - 8.4 = 1.6
9 – 8.4 = 0.6
8 – 8.4 = -0.4
8 – 8.4 = -0.4
7 – 8.4 = -1.4
0.0
871089
Squared Deviations
2.56
0.36
0.16
0.16
1.96
1.0 Variance
Variability
Variance = average distance between observations and the mean squared
Emmett
Jake
Observations
7
7
7
6
6
averages
Deviations Squared Deviations 76776
Variability
Variance = average distance between observations and the mean squared
Emmett
Jake
Observations
7
7
7
6
6
averages 6.6
Deviations
7 - 6.6 = 0.4
7 - 6.6 = 0.4
7 - 6.6 = 0.4
6 – 6.6 = -0.6
6 – 6.6 = -0.6
0.0
Squared Deviations
0.16
0.16
0.16
0.36
0.36
0.24
76776
Variance
Variability
Standard deviation = square root of variance
Emmett
Jake
Variance Standard Deviation
Emmett 1.0 1.0
Jake 0.24 0.4898979
But what good is a standard deviation
Variability
The world tends to be bell-shaped
Most outcomes occur in the
middle
Fewer in the “tails”
(lower)
Fewer in the “tails” (upper)
Even very rare outcomes are
possible(probability > 0)
Even very rare outcomes are
possible(probability > 0)
Variability
Add up the dots on the dice
0
0.05
0.1
0.15
0.2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Sum of dots
Pro
ba
bili
ty 1 die
2 dice
3 dice
Here is why:
Even outcomes that are equally likely (like dice), when you add them up, become bell shaped
“Normal” bell shaped curve
Add up about 30 of most things and you start to be “normal”
Normal distributions are divide upinto 3 standard deviations on each side of the mean
Once your that, you know a lot about what is going on
And that is what a standard deviation is good for
Usual or unusual?
1. One observation falls outside 3 standard deviations?
2. One observation falls in zone A?
3. 2 out of 3 observations fall in one zone A?
4. 2 out of 3 observations fall in one zone B or beyond?
5. 4 out of 5 observations fall in one zone B or beyond?
6. 8 consecutive points above the mean, rising, or falling? X XXXX XX X XX1 2 3 4 5 6 7 8
Causes of Variability
Common Causes: Random variation (usual) No pattern Inherent in process adjusting the process increases its variation
Special Causes Non-random variation (unusual) May exhibit a pattern Assignable, explainable, controllable adjusting the process decreases its variation
SPC uses samples to identify that special causes have occurred
Limits
Process and Control limits: Statistical Process limits are used for individual items Control limits are used with averages Limits = μ ± 3σ Define usual (common causes) & unusual (special causes)
Specification limits: Engineered Limits = target ± tolerance Define acceptable & unacceptable
Process vs. control limits
Variance of averages < variance of individual items
Distribution of averages
Control limits
Process limits
Distribution of individuals
Specification limits
Usual v. Unusual, Acceptable v. Defective
μ Target
A B C D E
More about limits
Good quality: defects are rare (Cpk>1)
Poor quality: defects are common (Cpk<1)
Cpk measures “Process Capability”
If process limits and control limits are at the same location, Cpk = 1. Cpk ≥ 2 is exceptional.
μtarget
μtarget
Process capability
Good quality: defects are rare (Cpk>1)Poor quality: defects are common (Cpk<1)
Cpk = min
USL – x3σ
=
x - LSL3σ
=
3σ = (UPL – x, or x – LPL) = =
14 20 26 15 24
24 – 203(2)
= = .667
20 – 153(2)
= = .833
Going out of control
When an observation is unusual, what can we conclude?
μ2
The mean has changed
X
μ1
Going out of control
When an observation is unusual, what can we conclude?
The standard deviationhas changed
σ2
X
σ1
The SPC implementation process
•Identify what characteristics to be controlled
•Establish Control limits
•Find how to control the process
•Learn how to measure improvement of a process
•Learn how to detect shift and how to set alerts that take action.
•Learn about the two types of causes that affect your variation.
Setting up control charts:
Calculating the limits
1. Sample n items (often 4 or 5)
2. Find the mean of the sample x-bar
3. Find the range of the sample R
4. Plot x bar on the x bar chart
5. Plot the R on an R chart
6. Repeat steps 1-5 thirty times
7. Average the x bars to create (x-bar-bar)
8. Average the R’s to create (R-bar)
Setting up control charts:
Calculating the limits
9. Find A2 on table (A2 times R estimates 3σ)
10. Use formula to find limits for x-bar chart:
11. Use formulas to find limits for R chart:
RAX 2
RDLCL 3 RDUCL 4
Let’s try a small problem
smpl 1 smpl 2 smpl 3 smpl 4 smpl 5 smpl 6
observation 1 7 11 6 7 10 10
observation 2 7 8 10 8 5 5
observation 3 8 10 12 7 6 8
x-bar
R
X-bar chart R chart
UCL
Centerline
LCL
Let’s try a small problem
smpl 1 smpl 2 smpl 3 smpl 4 smpl 5 smpl 6 Avg.
observation 1 7 11 6 7 10 10
observation 2 7 8 10 8 5 5
observation 3 8 10 12 7 6 8
X-bar 7.3333 9.6667 9.3333 7.3333 7 7.6667 8.0556
R 1 3 6 1 5 5 3.5
X-bar chart R chart
UCL 11.6361 9.0125
Centerline 8.0556 3.5
LCL 4.4751 0
R chart
0123456789
10
1 2 3 4 5 6
9.0125
3.5
0
X-bar chart
11.6361
8.0556
4.4751
AP Uploads Quality control
Interpreting charts
Observations outside control limits indicate the process is probably “out-of-control”
Significant patterns in the observations indicate the process is probably “out-of-control”
Random causes will on rare occasions indicate the process is probably “out-of-control” when it actually is not
Interpreting charts
In the excel spreadsheet, look for these shifts:
A B
C D
Show real time examples of charts here
Lots of other charts exist
P chart C charts U charts Cusum & EWMA
For yes-no questions like “is it defective?” (binomial data)
For counting number defects where most items have ≥1 defects (eg. custom built houses)
Average count per unit (similar to C chart)
Advanced charts
“V” shaped or Curved control limits (calculate them by hiring a statistician)n
ppp
)1(3
cc 3
n
uu 3
SPC Station
SPC as a triggering tool
Selecting rational samples
Chosen so that variation within the sample is considered to be from common causes
Special causes should only occur between samples
Special causes to avoid in sampling passage of time workers shifts machines Locations
Chart advice
Larger samples are more accurate
Sample costs money, but so does being out-of-control
Don’t convert measurement data to “yes/no” binomial data (X’s to P’s)
Not all out-of control points are bad
Don’t combine data (or mix product)
Have out-of-control procedures (what do I do now?)
Actual production volume matters (Average Run Length)
This is a control system which uses statistical techniques for knowing, all the time, changes in the process.
It is an effective method in preventing defects and helps continuous quality improvement.
Statistical Process Control (S.P.C.)
What does S.P.C. mean?
Statistical: Statistics are tools used to make predictions on
performance.
There are a number of simple methods for analysing data and, if applied correctly, can lead to predictions with a high degree of accuracy.
What does S.P.C. mean?
Process: The process involves people, machines, materials, methods,
management and environment working together to produce an output, such as an end product.
What does S.P.C. mean?
Control: Controlling a process is guiding it and comparing actual
performance against a target.
Then identifying when and what corrective action is necessary to achieve the target.
S.P.C.
Statistics aid in making decisions about a process based on sample data and the results predict the process as a whole.
PeopleMachines Material
Management Methods Environment
Output
The Aim of S.P.C. - Prevention Strategy
Prevention Benefits: Improved design and process capability. Improved manufacturing quality. Improved organisation. Continuous Improvement.
The S.P.C. has to be looked at as a stage towards completely preventing defects.
With stable processes, the cost of inspection and defects are significantly reduced.
S.P.C. as a Prevention Tool
Assesses the design intent. Achieves a lower cost by providing an early warning system. Monitors performance, preventing defects. Provides a common language for discussing process performance.
The Benefits of S.P.C.
Process Element Variable Examples
Machine………………………….Speed, operating temperature, feed rate
Tools………………………………..Shape, wear rateFixtures…………………………..Dimensional accuracyMaterials…………………………Composition, dimensionsOperator…………………………Choice of set-up, fatigueMaintenance…………………Lubrication, calibrationEnvironment…………………Humidity, temperature
Process Variations
No industrial process or machine is able to produce consecutive items which are identical in appearance, length, weight, thickness etc.
The differences may be large or very small, but they are always there. The differences are known as ‘variation’. This is the reason why
‘tolerances’ are used.
Process Variations
Common causes are the many sources of variation that are always present. A process operates within ‘normal variation’ when each element varies in a random
manner, within expected limits, such that the variation cannot be blamed on one element.
When a process is operating with common causes of variation it is said to be stable.
Stability
The process can only be termed ‘under control’ if it gives predictable results.
Its variability is stable over a long period of time.
Process Control
Graphs and charts have to be chosen for their simplicity, usefulness and visibility.
They are simple and effective tools based on process stability monitoring.
They give evidence of whether a process is operating in a state of control and signal the presence of any variation.
Process Control Charts
Data InterpretationConsider these 50 measurements
Bore Diameter 36.32 ±0.05mm (36.27 - 36.37mm)
1 36.36 11 36.37 21 36.34 31 36.35 41 36.362 36.34 12 36.35 22 36.37 32 36.35 42 36.373 36.34 13 36.32 23 36.34 33 36.36 43 36.374 36.33 14 36.35 24 36.35 34 36.37 44 36.355 36.35 15 36.34 25 36.34 35 36.34 45 36.376 36.33 16 36.34 26 36.35 36 36.36 46 36.367 36.33 17 36.35 27 36.36 37 36.38 47 36.358 36.34 18 36.33 28 36.33 38 36.34 48 36.349 36.35 19 36.32 29 36.36 39 36.35 49 36.35
10 36.35 20 36.35 30 36.38 40 36.35 50 36.34
Data Interpretation As a set of numbers it is difficult to see any pattern. Within the table, numbers 30 and 37 were outside the
tolerance – but were they easy to spot? A way of obtaining a pattern is to group the measurements
according to size.
Data Interpretation – Tally Chart
The tally chart groups the measurements together by size as shown.
The two parts that were out of tolerance are now easier to detect (36.38mm).
36.39
36.38
36.37
36.36
36.35
36.34
36.33
36.32
36.31
36.30
36.29
36.28
Tally Chart - Frequency
The tally chart shows patterns and we can obtain the RANGE - 36.32mm to 36.38mm.
The most FREQUENTLY OCCURRING size is 36.35mm.
36.39
36.38
36.37
36.36
36.35
36.34
36.33
36.32
36.31
36.30
36.29
36.28
2
6
7
16
12
5
2
Tally Chart - Information
The tally chart gives us further information: The number of bores at each size; The number of bores at the most common size; The number of bores above and below the most common size (36.35mm)
- number above 36.35mm is 7+6+2=15 number below 36.35mm is 12+5+2=19
HistogramWe can redraw the frequency chart as a bar chart known as a histogram:
16
14
12
10
8
6
4
2
0 36.31 36.32 36.33 36.34 36.35 36.36 36.37 36.38 36.39