Statistical process control (SPC) uses statistical methods to monitor and control processes. The goal of SPC is to reduce process variability through the prevention of defects. SPC analyzes process data over time to distinguish common and special causes of variation. Key aspects of SPC include understanding variable and attribute data, using control charts to monitor processes, and calculating process capability indices to quantify a process's ability to meet specifications.

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

6. HISTOGRAM Sub- Groups Quantity

7. BELL SHAPE CURVE

8. BELL SHAPE CURVE T he Curve is Symmetrical either side is a Mirror Image. T he highest point of the Bell indicates the most common occurrence. This is called the “Arithmetic Average” Arithmetic Average X

9. AVERAGES Mean: Arithmetic Average of group of Measurements. The symbol for a sample is and for a batch is Median: The middle value in a group of measurements when arranged in ascending or descending order. Mode: The most frequently occurring number in a group of measurements. X X

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. STANDARD DEVIATION Standard Deviation Formula :-   (n - 1) (x-x) 2

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. DISTRIBUTION CURVES N ormal Distribution Process Performs around a Central Mean Repeatable. Predictable. B inomial Distribution Process has 2 means caused by: Resetting of Process. Changes in Operator. 2 Like processes measured as one. Spread X

15. PROCESS CAPABILITY Repeatability: The ability of a measuring device to duplicate measurements when used several times by one individual and measuring the identical characteristic. Reproducibility: The difference in the average of the measurements made by different persons using the same or different measuring device when measuring the identical characteristic

16. DISTRIBUTION CURVES F lat Top - Process Mean has Gradually shifted. Tool Wear. Too Many measurables in the Process. S kew - Mean of process Biased to one side. Biased Inspection.

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. PROCESS CAPABILITY C pk Is the actual capability index of a process. The Cpk index quantifies both the spread and the centring of the process in relation to the specified limits. 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

21. PROCESS CAPABILITY I f the process variability is wider than the Limits, it is not capable,and will produce Scrap Cpk < 1 LSL USL

22. PROCESS CAPABILITY I f a process is capable, it is able to produce nearly 100% within the specified Limits. But this Process is NOT ROBUST Cpk = 1 LSL USL

23. PROCESS CAPABILITY If the process variability is within the specified limits, the process is very capable, and will produce all good parts. This Process has ROBUSTNESS! Cpk > 1 LSL USL

24. PROCESS CAPABILITY Accurate:(Cpk) A tight cluster of measurements that lie within pre-determined limits. Precision:(Cp) A tight cluster of measurements that lie outside pre-determined limits.

25. PROCESS CAPABILITY Precise Accurate

26. PROCESS CAPABILITY

27. Variable Control Charts Variable Data X Sample Average Identify trends within the process. R Sample Range Identify changes in process variation. -5 -3 -1 1 3 5 0 1 2 3 4 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 X R

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!