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S tatistical   P rocess  C ontrol
SPC STATISTICAL A mathematical technique to  interpret and organise numerical  data PROCESS A set of linked activities that add value or produce an item. It will comprise of the 5Ms and 1E CONTROL A regulatory mechanism to ensure correct characteristic performance
Statistical Process Control SPC exists because there is variation in the characteristics of all machines, people, materials, methods, measurements and environments. SPC has as its aim “Zero Defect” through the application of defect prevention. SPC has as its foundation a philosophy which reduces external inspection, turning the focus on encouraging individuals to  manage the process to allow their efforts to concentrate on eradicating sources of process variability. SPC seeks to ensure the consistent performance of a process over a long duration
VARIATION C ommon  C ause   is a source of variation that is  always present , part of  the  random variation inherent in the process itself S pecial  C ause is a source of variation which is unpredictable or intermittent. It is sometimes called an assignable cause There are two main types of variability in a process :-
DATA VARIABLE This data is a measurement of a characteristic along a scale ATTRIBUTE This data has only two possibilities Pass / Fail Yes / No There is no measurement. A judgement is made
HISTOGRAM Sub- Groups Quantity
BELL SHAPE CURVE
BELL SHAPE CURVE ,[object Object],[object Object],[object Object],[object Object],Arithmetic   Average X
AVERAGES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],X X
STANDARD DEVIATION SIGMA This indicates an area of deviation from a standard position.  For sample size data this Greek symbol is used  s  For batch size data this symbol is used
STANDARD DEVIATION ,[object Object],  (n - 1) (x-x) 2
STANDARD DEVIATION Mean 99.73 %       +/-  3 
STANDARD DEVIATION 99.73 % 13.59% 13.59%   34.13 % 34.13 %     0.13 % 0.13 % 2.14 % 2.14 %   Mean 8 8
DISTRIBUTION CURVES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Spread X
PROCESS CAPABILITY ,[object Object],[object Object]
DISTRIBUTION CURVES   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
C APABILITY  is a Comparison between Actual Performance  & A Defined Specification
PROCESS CAPABILITY Process Capability is a measure of the variation of a process and its ability to produce components consistently within specifications Process Capability can only be defined when a process is in statistical control; this occurs only when special cause variation has been eliminated
PROCESS CAPABILITY Cp   is the theoretical Capability index of a process. This index quantifies the spread of the process relative to the specified limits 6   Cp = USL - LSL Cp > 1 Cp = 1 Cp < 1
PROCESS CAPABILITY ,[object Object],[object Object],[object Object],[object Object],[object Object],Cpk = Minimum value of :- USL - X or X - LSL 3   3   Upper Tolerance Lower Tolerance High Cp, High  Cpk High Cp, Very Low  Cpk High Cp,  Very Low  Cpk Low  Cp, Low Cpk
PROCESS CAPABILITY ,[object Object],[object Object],LSL USL
PROCESS CAPABILITY ,[object Object],[object Object],LSL USL
PROCESS CAPABILITY ,[object Object],[object Object],[object Object],LSL USL
PROCESS CAPABILITY ,[object Object],[object Object]
PROCESS CAPABILITY Precise Accurate
PROCESS CAPABILITY
Variable Control Charts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],-5 -3 -1 1 3 5 0 1 2 3 4 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 X R
SIX SIGMA PROCESS LSL     X USL Cp = 2 Cpk  = 1.5 3.4 ppm Rejects
CAPABILITY EXAMPLE LSL USL PROCESS Cp Index Cpk Index A B C D Cp < 1 Cp < 1 Cp > 2 Cp > 2 Cpk < 1 Cpk = 1 Cpk = 1 Cpk > 2 RESULT
N ever ending improvement is Reflected in an  Increasing  Cpk value!

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SPC WithAdrian Adrian Beale

  • 1. S tatistical P rocess C ontrol
  • 2. SPC STATISTICAL A mathematical technique to interpret and organise numerical data PROCESS A set of linked activities that add value or produce an item. It will comprise of the 5Ms and 1E CONTROL A regulatory mechanism to ensure correct characteristic performance
  • 3. Statistical Process Control SPC exists because there is variation in the characteristics of all machines, people, materials, methods, measurements and environments. SPC has as its aim “Zero Defect” through the application of defect prevention. SPC has as its foundation a philosophy which reduces external inspection, turning the focus on encouraging individuals to manage the process to allow their efforts to concentrate on eradicating sources of process variability. SPC seeks to ensure the consistent performance of a process over a long duration
  • 4. VARIATION C ommon C ause is a source of variation that is always present , part of the random variation inherent in the process itself S pecial C ause is a source of variation which is unpredictable or intermittent. It is sometimes called an assignable cause There are two main types of variability in a process :-
  • 5. DATA VARIABLE This data is a measurement of a characteristic along a scale ATTRIBUTE This data has only two possibilities Pass / Fail Yes / No There is no measurement. A judgement is made
  • 8.
  • 9.
  • 10. STANDARD DEVIATION SIGMA This indicates an area of deviation from a standard position.  For sample size data this Greek symbol is used  s  For batch size data this symbol is used
  • 11.
  • 12. STANDARD DEVIATION Mean 99.73 %       +/- 3 
  • 13. STANDARD DEVIATION 99.73 % 13.59% 13.59%   34.13 % 34.13 %     0.13 % 0.13 % 2.14 % 2.14 %   Mean 8 8
  • 14.
  • 15.
  • 16.
  • 17. C APABILITY is a Comparison between Actual Performance & A Defined Specification
  • 18. PROCESS CAPABILITY Process Capability is a measure of the variation of a process and its ability to produce components consistently within specifications Process Capability can only be defined when a process is in statistical control; this occurs only when special cause variation has been eliminated
  • 19. PROCESS CAPABILITY Cp is the theoretical Capability index of a process. This index quantifies the spread of the process relative to the specified limits 6  Cp = USL - LSL Cp > 1 Cp = 1 Cp < 1
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 27.
  • 28. SIX SIGMA PROCESS LSL     X USL Cp = 2 Cpk = 1.5 3.4 ppm Rejects
  • 29. CAPABILITY EXAMPLE LSL USL PROCESS Cp Index Cpk Index A B C D Cp < 1 Cp < 1 Cp > 2 Cp > 2 Cpk < 1 Cpk = 1 Cpk = 1 Cpk > 2 RESULT
  • 30. N ever ending improvement is Reflected in an Increasing Cpk value!

Editor's Notes

  1. Common and Special Causes. Shewhart identified that there are two types of variation, common cause and special cause . Common Cause variation is one which is contained within a natural process which is in a state of statistical control. This variation is inherent in the process and requires fundamental action to reduce it. In the process of a journey with the aim of getting to work this will mean things like waiting time at fixed traffic lights, only fundamental action on the process like changing route or removing the traffic lights will remove the cause of the variation. Special Cause Variation is one which stems from a change which is outside the system or process and is seen as additional variation. In the journey to work example this would include Roadwork&apos;s and Breakdowns. In most cases action can be taken to achieve a reduction in the future effect of these causes by better maintenance to avoid the breakdown.
  2. The histogram is used to show graphically the relative number of occurrences of a range of events. It uses vertical bars…..It plots frequency on the vertical axis against events one after the other on the horizontal axis
  3. 10
  4. The Diagram above shows the relationship between Standard Deviation and probability. Most of the time1 we relate to +/- 3 standard deviations, between which we can be sure that 99.73 % of our measured sample will fall between.
  5. Normal A normal distribution is repeatable and predictable. It defines a stable process The majority of measurements will fall around the mean and occasionally they will fall away from the mean, this is due to natural variation in the process. The natural variation is due to “Common Causes - these are causes common to the process and are always present. Bi - Modal distribution is a process that varies about 2 means. It is possible that 2 processes are being measured or a process has been stopped, reset and continued with the data being continuously collected. The process may also be effected by changes in people or materials. These are all “Special Causes” and can be controlled with relative ease.
  6. A flat Top distribution is usually due to a process that has drifted. The process could have drifted as a result of tooling wear, temperature change or continuous incremental adjustments or changes to a system. These are Special Causes and action should be taken to identify them. A Skewed distribution is a process where a bias may be present. Faulty measurement process or biased system operators could cause it. There could also be an incremental change in the process under certain conditions. These are also Special Causes.
  7. Lower Specified Limit = LSL Upper Specified Limit = USL