1. Statistical Process Control

2. An Old Story abridged from ‘Right First Time’ An entrepreneur knows of a business opportunity of supplying plastic discs between diameter 39.5 mm to 40.5 mm. Each disc costs about Rs. 28 and the margin is Rs. 2 As a matter of caution, he moulds a sample lot of 32 discs, measures their diameter, finds all are within the range, except one which is on the fringe. Decides that now production can start Asking the production manager to be careful, he produces first lot of 10,000 discs, sends to the customer with an invoice of Rs. 300,000, hoping for a profit of Rs. 20,000

3. An Old Story Continued …. Three days later, the discs are rejected, with a note stating that they have more than 5% defectives. (i.e. too small or too large discs) Determined not to give up, the entrepreneur develops two gauges, puts a few spinsters to sort out discs. They find 9300 good discs. Finally sends 9300 discs. This whole business of inspection costs him 3 days & Rs. 21,000 considering the cost of rejected discs & wages. The customer however accepts this lot and places a trial order of 100,000 discs.

4. Determined not to loose money this time, he cautions the production manager to be extra careful A quality inspector is recruited to check the diameter of sample discs every hour and report if any defective is found. The moment a defective is found, the production manager swings into action, adjusting a few parameters to bring the deviation in control. After spending about 6 days & nearly Rs 28,00,00, the lot of 100,000 discs is sent with an invoice for Rs. 30,00,000

5. After 4 days, … … … … A large parcel of 100,000 discs arrives in place of a cheque for Rs. 30,00,000

6. What Went Wrong? –Error 1 : Threw away data on process capability –Error 2 : Inspection is not quality control –Error 3 : Introduction of variability in the process 4 Questions for SPC –Can we make it right? Process Capability –Are we making it right? Process Control –Have we made it right? Quality Assurance –Can we make it better? Process Improvement

7. Some Facts Any process, however well controlled, produces an output with some variation within. Most process variation follows a pattern called normal distribution An important property of normal distribution is that nearly 99.5% of output is within Mean ± 3 * SD For a process to be suitable, this variation should be smaller than specifications

8. An Old Story Continued …. What Should have been done Use the data of 32 discs to determine the Mean and SD of the process –For a sufficiently large sample (at least 20), Sample Mean = Population Mean. Sample SD = Population SD / √(sample size) –Generally Population Mean ± 3* Population SD covers nearly 99.5% of population Determine if process is capable to meet specifications Use Process Control Charts

9. Types of Control Charts Control Charts for Variables –Mean X and R Chart Control Charts for Attributes –Control Chart for fraction defectives p Chart & np chart –Control Chart for non-conformities c Chart u chart demerit system Cumsum & Moving Average Charts

10. General Procedure for Use of Control Charts Determine Process Capability Determine Sample Size Determine Center-Line and Upper and Lower Control Limits Take Periodic Sample and Plot the results on the chart

11. Control Charts for Variables Mean X & R chart –How to plot Mean X Chart –How to plot R chart –Interpretation Mean X Chart R Chart

12. Control Charts for Variables Mean X & R chart –How to plot Mean X Chart –How to plot R chart –Interpretation Mean X Chart R Chart

13. Table for Mean X & R Charts nA2D3D4d2D1D2 21.8800.0003.2671.12803.69 31.0230.0002.5741.69304.36 40.7290.0002.2822.05904.7 50.5770.0002.1152.32604.92 60.4830.0002.0042.53405.08 70.4190.0761.9242.7040.25.2 80.3730.1361.8642.8470.395.31 90.3370.1841.8162.9700.555.39 100.3080.2231.7773.0780.695.47

14. An Example From Process Capability Study, Sample Size 4 –Mean X = 40.1 –Mean R = 0.25

15. An Example For Range Chart –LCL = 0 –CL = 0.25 –UCL = 0.57 For Mean X Chart –LCL = 40.1-0.73*0.25 =39.92 –CL = 40.1 –UCL = 40+0.73*0.25 = 40.28 Population is likely to be between –LCL = 40.1-0.73*0.25*2 =39.735 –UCL = 40.1+0.73*0.25*2 = 40.465

16. Interpretation of charts Cyclic Pattern Mixture- Two or more overlapping distributions A Shift (5 or more consecutive readings on one side of mean) Trend Stratification

17. Type I & Type II errors Type I error –False alarm when process is in control –Risk of committing this error is denoted by α Type II error –No alarm when process has gone out of control –Risk of committing this error is denoted by β

18. Estimate SD of process from average range Calculate PCR as If PCR > 1, process is capable to meet requirements. Process Capability

19. Acceptance Sampling A method to evaluate quality post-production. Used extensively to check in-coming quality From a lot of N pieces, a sample of n pieces is drawn & inspected. The lot is rejected if more than a defectives are found in the lot. When using this method, there are 2 risks, viz. Producers risk – α and Consumer’s risk – β For a given sampling Plan, the OC curve shows the risks