Statistical Process Control (SPC) - QMS.pptx

Statistical Process Control in Pharmaceutical Quality Management System

1. STATISTICAL PROCESS CONTROL Presented by Mr. R. ARUN KUMAR, B.Pharm ., M.Pharm (PQA) - I Year , Department of Pharmacy, Annamalai University. Submitted to Dr. K. DEVI, M.Pharm ., Ph.D., Assistant Professor, Department of Pharmacy, Annamalai University. MQA102T – QUALITY MANAGEMENT SYSTEMS

2. Statistical Process Control (SPC) DEFINITION: Statistical Process Control is a statistical method to measure, monitor and control a process. It is a scientific visual method to monitor, control and improve the process by eliminating special cause variation in a process. It evaluates people, materials, methods, machines, and processes by stressing prevention rather than detection. 2

3. 3 It is a fast feedback system. The SPC concepts are include in the management philosophy by Dr.W.E . Deming just before World War II. Though SPC effectively used in western industries since 1980, it was started during twenties in America. SPC became famous after Japanese industries implement the concepts and complete with western industries.

4. The Terms, Statistics – It is a science which deals with a collection, summarization, analysis and drawing information from data. Process – It converts input resources into the desired output. Control – System, policies and procedures in place the overall output meets the requirement. 4

5. SPC Benefits Increases customer satisfaction Improves productivity Reduces the inspection Decreases operating costs Reduce scrap and rework 5

6. Importance of SPC Visibility into quality data prevents over-tampering Control charts provide operational insight for critical stakeholders Data accessibility and visibility levels the playing field Real – time SPC helps reduce the margin of error 6

7. 7 Minitab QDM SPC Platform SQCpack GainSeeker Suite Statgraphics Dot Compliance QMS Predator Software IntraStage WATS SPC for Excel SPC software:

8. CONTROL CHARTS: The control chart was invented by Walter A Shewhart in 1924. A chart that shows plotted values, a central line, and one or two control limits and is used to monitor a process over time. most important tools of SPC simple verifying the results of any improvement actions taken 8

9. powerful tools for checking the stability of a process over time The power of the control chart is in its ability to separate these assignable causes of quality variation from inherent, unavoidable causes. The two categories of control charts: Variables Control Charts Attribute Control Charts 9

10. 10 CONTROL CHART

11. Chart Preparation Before a control chart can be used, several steps must be taken: Management must prepare a responsive environment The process that is to be studied must be understood Unnecessary variation must be minimized The characteristics to be controlled must be determined The measurement system must be defined Step 1 Step 2 Step 3 Step 4 Step 5 11

12. Control Chart Functions Monitor process performance over time Describe what control there is Verify the results of any corrective action Signal when corrective action is needed Estimate the capability of the process Help attain control by detecting change in the performance of the process 12

13. Variables Control Charts Many quality characteristics like the height, weight, diameter, dimension, volume, pressure, temperature are represented numerically. The measurement of these characteristics through inspection is said to be expressed by variables. Variables Control Charts are used to record and monitor process performance with respect to the selected variable. 13

14. 14 The three types of variables charts are: Average and Range Charts Median and Range Charts Average and Standard Deviation Charts

15. Average-Range Charts It is one of the most powerful and sensitive SPC tools. It can be applied to any continuous variable like weight, size, cycle time and error rate. Subgroup size is 2 -10. These charts are widely used control chart for variable data to examine the process stability in many industries. 15

16. Steps used in preparing : Properly label the chart. Collect and record data. Select scales. Plot data. Develop chart Establish centerline Calculate control limits 16

17. Develop chart: Establish centerline Calculate control limits Draw lines on control chart. Interpret chart. 17

18. Median-Range Charts Median and Range control charts is very similar to those for Average-Range charts. Steps used to develop : Label chart. Collect data, establish scales and plot all data. Develop R chart 18

19. Develop chart Draw centerlines and control limits. Interpret chart. 19

20. Average-Standard Deviation Charts The same approach is taken in developing Average and Standard Deviation Charts as for . are used for sample sizes greater than 10. The is used primarily in laboratories and in research and development work. 20

21. Steps used to develop : Label chart. Collect data. Calculate and S for each sample. Establish scales and plot data. Calculate and to develop the centerlines. Draw the centerlines on the chart as solid lines. 21

22. Calculate the control limits Draw the control limits on the chart as dashed lines. Interpret the chart. 22

23. Attribute control charts Many quality characteristics can be observed only as attributes, which cannot be listed and plotted on a numerical chart. `Many quality characteristics like colour , taste, appearance, etc., Attributes generally fall into two classes: either good or bad, go or no-go, conforming or nonconforming. Unlike variable charts, only one chart is plotted for attributes. 23

24. Types of Attribute Control Charts p charts - for identifying the very minimal defective products. np charts – for controls the number of defects. c charts - for finding the number of nonconformities. u charts - for identifies the number of nonconformities per unit. 24

25. Control Charts for Proportion Defective (p Charts) p charts are statistical tools used to evaluate the proportion defective, or proportion non-conforming, produced by a process. p charts uses binomial distribution to measure the proportion of defectives or non conforming units in a sample. 25

26. Construction Steps for Constructing p Charts Gather data Calculate p Plot the data Calculate control limits. 26

27. Control Charts for Count of Defectives ( np Charts) np charts are statistical tools used to evaluate the count of defectives, or count of items non-conforming, produced by a process. it uses binomial distribution to measure the number of defectives or non-conforming units in a sample. It is very similar to the p chart. np chart plots the number of items, while p chart plot the proportion of defective items. 27

28. Construction Steps for Constructing np Charts Gather data Calculate np Plot the data Calculate control limits. 28

29. Control Charts for Average Occurrences-per-Unit (u Charts) u charts is also known as the control chart for defects. It is generally used to monitor the count type of data where the sample is greater than one. There may be single type of defect or several types, but u chart tracks the average number of defects per unit. It assumes the underlying data approximate the Poisson distribution. 29

30. Construction Steps for Constructing u Charts Gather data Calculate u Plot the data Calculate control limits. 30

31. Control Charts for Counts of Occurrences-per-Unit (c Charts) c charts is also known as the control chart for defects (counting of the number of defects). It is generally used to monitor the number of defects in constant size units. There may be single type of defect or several types, but c chart tracks the average number of defects in each unit. It assumes the underlying data approximate the Poisson distribution. 31

32. Construction Steps for Constructing c Charts Gather data Calculate c Plot the data Calculate control limits. 32

33. 33 Continuous data/ Discrete data? Defects/ Defectives Subgroup size? Xbar – S chart Xbar – R chart Sample size? Sample size? u- chart c chart p chart np chart Continuous data Discrete data 2<n>9 n>10 Defects Defectives Variable Variable Constant Constant Control Chart Selection

34. PROCESS CAPABILITY A planning tool assure process performance The ability to forecast that a process will be capable of turning out outputs conforming to quality requirements in measurable terms is called process capability. Process is what transforms an input or a set of outputs into output by using a combination of resources including time. Capability is used in the sense of tested ability to perform thereby achieving results that can be measured. 34

35. 35 PROCESS CAPABILITY

36. Cp index It is a fundamental indication of process capability. Most companies require that the process Cp = 1.33 or greater. In order to manufacture within a specification, the difference between the USL and the LSL must be less than the total process variation. 36

37. So a comparison of 6 σ with (USL – LSL) or 2T gives an obvious process capability index, known as the Cp of the process: Value of Cp below 1 means that the process variation is greater than the specified tolerance band so the process is incapable. For increasing values of Cp the process becomes increasingly capable. 37

38. Cpk index There are two Cpk values, Cpk u and Cpk l . The overall process Cpk is the lower value of Cpk u and Cpk l . A Cpk of 1 or less means that the process variation and its centring is such that at least one of the tolerance limits will be exceeded and the process is incapable. 38

39. As in the case of Cp , increasing values of Cpk correspond to increasing capability. A comparison of the Cp and the Cpk will show zero difference if the process is centred on the target value. The Cpk can be used when there is only one specification limit, upper or lower – a one-sided specification. 39

40. 40 REFERENCES: Statistical Process Control - Fifth Edition by John S. Oakland The Handbook for Quality Management - A Complete Guide to Operational Excellence - Second Edition by Thomas Pyzdek and Paul Keller Statistical Process Control – AIDT ISO 9001:2001 Total Quality Management by Dr. N. Ramachandran , Prof. C. Vimala , Prof.V.R . Vivekanandan and D.Umamaheshwari

41. 41 Thank You