Statistical process control (SPC) involves collecting numerical process data, analyzing it using statistical methods like control charts, and making adjustments to maintain quality and reduce variation. The goal is to control processes by identifying and removing assignable causes of variation to leave only natural chance variation. Key aspects of SPC include calculating measures of central tendency and variation from data, setting control limits on charts to monitor the process, and determining process capability indices to quantify how well the process fits specifications.

2. is to influence and to control the quality at the time of manufacturing . Statistical Process Control (SPC) focuses on controlling the manufacturing process to prevent defects rather than detect them. Purpose and Application of SPC

9. Average ( ) or Mean : It simply means sum of all the individual observed data divided by the no. of observations. X 1 + X 2 + X 3 + - - - - + Xn n n = no. of Observations. Example : Observations = 20, 24, 26, 28, 43, 18 n = 6 20+24+26+28+43+18 = = 6 = 26.5 X X X

10. Range (R) : Range is measure of the variation in a set of data. It is calculated by subtracting the lowest value in the data set from the highest value in that same set. R = X max – X min X max = 43, X min = 18 R = 43 – 18 = 25 Observations = 20, 24, 26, 28, 43, 18

11. Assuming Normal distribution as the pattern of variation for measured characteristics, we know that Average + 3  / - 3  comprises almost all (99.73%) of the observations. Hence 6 sigma is used as a measure of process capability. Lack of statistical control results in a variability larger than 6 sigma. Thus 6 Sigma is valid for statistically stable process and sigma is estimated as  = / d 2 R

13. Indices Tolerance Process Capability = Cp = Potential Capability The Capability Index is defined as the ratio of the Specification spread or Tolerance to the Process Variation = USL - LSL 6 / d 2 R

15. Cpk = Achieved Capability 3  Min of Or - LSL 3  Cpk = USL – X X Where,  = / d 2 and the actual average is R X

16. CONSTANT X X median R S

18. Inferences based on Cpk : 3 . Cpk = 1.00 represents a just capable process and the process can give good results only if it is perfectly centered. Even a small deviation from the mid of specification will result in non conformance. Here 100% of the tolerance is used. Not a very desirable solution. 4. Cpk < 1.00 means that the process not capable of meeting the specified tolerance. In this case, rework and rejection will be inevitable.