0
QA/QC<br />1<br />Quality ManagementQA-GMP-QC<br />Mr. Dipak D. Gadade, <br />Dept. of Pharmaceutics,<br />Shri Bhagwan Co...
What is Quality ?<br /><ul><li>Fitness for use
Freedom from defects
Degree of excellence
Customer/Buyer’s satisfaction
Compliance with specified/official requirements </li></ul>QA/QC<br />2<br />
What is Quality ?<br /><ul><li>USFDA</li></ul>	A measure of a product’s or service’s ability to satisfy the custmor’s stat...
What is Quality ?<br /><ul><li>Quality of a pharmaceutical product is measured by it’s fitness for intended use. Safety an...
Relation Between QM, QA, QC & GMP<br />QA/QC<br />5<br />
What is Quality Management?<br />WHO  Definition: <br />The aspect of management functions that determines and implements ...
What is Quality Assurance?<br />WHO definition : <br />It is a wide-ranging concept covering all matters that individually...
What is GMP?<br />GMP is that part of QA which ensures that products are consistently produced and controlled to the quali...
What is Quality Control?<br /> Is that part of GMP concerned with sampling, specification & testing, documentation & relea...
What is difference?<br />Q.A.<br />sum total of organized  arrangements made with the object of ensuring that product will...
What is difference?<br />Q.A.<br />Systematic actions necessary to provide adequate confidence that a product will satisfy...
Total Quality Control<br />Process of striving to produce a perfect product by a series of measures requiring an organized...
Sources of Quality Variation and its Control<br /> Quality Control<br />
Variation<br />There is no two natural items in any category are the same.<br />Variation may be quite large or very small...
Categories of variation<br />Within-piece variation<br />One portion of surface is rougher than another portion.<br />Apie...
Sources of variation<br />Equipment<br />Tool wear, machine vibration, …<br />Material<br />Raw material quality<br />Envi...
Control of Quality Variation<br />Raw Materials <br />Q.A. monograph<br />In process Quality Control<br />Q.A. before star...
Control of Quality Variation<br />In process Quality Control<br />Q.A. at startup<br />Raw materials processing<br />Compo...
Statistical Quality Control<br />Monitoring quality by application of statistical methods in all stages of production<br /...
Control Chart Viewpoint<br /><ul><li>Variation due to
Common or chance causes
 Assignable causes
Control chart may be used to discover “assignable causes” </li></li></ul><li>Control chart functions<br />Control charts a...
Control charts identify variation<br />Chance causes - “common cause”<br />inherent to the process or random and not contr...
Control charts help us learn more about processes<br />Separate common and special causes of variation<br />Determine whet...
Control charts to monitor  processes<br />To monitor output, we use a control chart<br />we check things like the mean, ra...
Types of Data<br />Variable data<br />Product characteristic that can be measured<br />Length, size, weight, height, time,...
Control chart for variables<br />Variables are the measurablecharacteristics of a product or service.<br />Measurement dat...
Control charts for variables <br />X-bar chart<br />In this chart the sample means are plotted in order to control the mea...
X-bar and R charts<br />The X- bar chart is developed from the average of each subgroup data. <br />used to detect changes...
Control chart components<br />Centerline<br />shows where the process average is centered or the central tendency of the d...
The Control Chart Method<br />X bar Control Chart:<br />UCL   = XDmean + A2 x Rmean        <br />LCL   = XDmean - A2 x Rme...
Control Chart Examples<br />UCL<br />Nominal<br />Variations<br />LCL<br />Sample number<br />
How to develop a control chart?<br />
Define the problem<br />Use other quality tools to help determine the general problem that’s occurring  and the process th...
Choose a subgroup size to be sampled<br />Choose homogeneous subgroups<br />Homogeneous subgroups are produced under the s...
Collect the data<br />Generally, collect 20-25 subgroups (100 total samples) before calculating the control limits.<br />E...
Determine trial centerline<br />The centerline should be the population mean, <br />Since it is unknown, we use X Double ...
Determine trial control limits - Xbar chart<br />The normal curve displays the distribution of the sample averages.<br />A...
UCL & LCL calculation<br />
Determining an alternative value for the standard deviation<br />
Determine trial control limits - R chart<br />The range chart shows the spread or dispersion of the individual samples wit...
Example: Control Charts for Variable Data<br />                        Tablet thickness (mm)<br />Sample 	1	2	3	4	5	    X	...
Calculation<br />From Table above:<br />Sigma X-bar = 50.09<br />Sigma R = 1.15<br />m = 10<br />Thus;<br />X-Double bar =...
Trial control limit<br />UCLx-bar = X-D bar + A2 R-bar <br />			= 5.009 + (0.577)(0.115) <br />			= 5.075 mm<br />LCLx-bar...
3-Sigma Control Chart Factors<br />Sample size	         X-chart       		R-chart<br />nA2D3D4<br />		2	1.88	0		3.27<br />		...
X-bar Chart<br />
R Chart<br />
Run Chart<br />
Another Example of X-bar & R chart <br />
Given Data (Table 5.2)<br />
Calculation<br />From Table 5.2:<br />Sigma X-bar = 160.25<br />Sigma R = 2.19<br />m = 25<br />Thus;<br />X-double bar = ...
Trial control limit<br />UCLx-bar = X-double bar + A2R-bar = 6.41 + (0.729)(0.0876) = 6.47 mm<br />LCLx-bar = X-double bar...
X-bar Chart<br />
R Chart<br />
Revised CL & Control Limits<br />Calculation based on discarding subgroup 4 & 20 (X-bar chart) and subgroup 18 for R chart...
New Control Limits<br />New value:<br />Using standard value, CL & 3 control limit obtained using formula:<br />
From Table B:<br />A = 1.500 for a subgroup size of 4,<br />d2 = 2.059, D1 = 0, and D2 = 4.698<br />Calculation results:<b...
Trial Control Limits & Revised Control Limit<br />Revised control limits<br />UCL = 6.46<br />CL = 6.40<br />LCL = 6.34<br...
Revise the charts<br />In certain cases, control limits are revised because:<br />out-of-control points were included in t...
Revising the charts<br />Interpret the original charts<br />Isolate the causes<br />Take corrective action<br />Revise the...
Process in Control<br />When a process is in control, there occurs a natural pattern of variation.<br />Natural pattern ha...
LSL<br />USL<br />Mean<br />-3  -2  -1         +1  +2  +3<br />68.26%<br />95.44%<br />99.74%<br />-3<br />+3<br /...
Normal Distribution Review<br /><ul><li>Define the 3-sigma limits for sample means as follows:
What is the probability that the sample means will lie outside 3-sigma limits?
Note that the 3-sigma limits for sample means are different from natural tolerances which are at</li></li></ul><li>Process...
Assignable Causes<br />(a) Mean<br />Average<br />Grams<br />
Assignable Causes<br />Average<br />(b) Spread<br />Grams<br />
Assignable Causes<br />Average<br />(c) Shape<br />Grams<br />
Control Charts<br />Assignable causes likely<br />UCL<br />Nominal<br />LCL<br />1                        2               ...
Control Chart Examples<br />UCL<br />Nominal<br />Variations<br />LCL<br />Sample number<br />
Upcoming SlideShare
Loading in...5
×

QA QC

3,156

Published on

Published in: Technology
0 Comments
3 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
3,156
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
254
Comments
0
Likes
3
Embeds 0
No embeds

No notes for slide

Transcript of "QA QC"

  1. 1. QA/QC<br />1<br />Quality ManagementQA-GMP-QC<br />Mr. Dipak D. Gadade, <br />Dept. of Pharmaceutics,<br />Shri Bhagwan College of Pharmacy,<br />Auranagabad<br />
  2. 2. What is Quality ?<br /><ul><li>Fitness for use
  3. 3. Freedom from defects
  4. 4. Degree of excellence
  5. 5. Customer/Buyer’s satisfaction
  6. 6. Compliance with specified/official requirements </li></ul>QA/QC<br />2<br />
  7. 7. What is Quality ?<br /><ul><li>USFDA</li></ul> A measure of a product’s or service’s ability to satisfy the custmor’s stated or implied needs<br /><ul><li>Swiss Standard Association:-</li></ul> The degree to which the product characteristics conform to the requirement placed upon that product including reliability, maintainability and safety.<br />QA/QC<br />3<br />
  8. 8. What is Quality ?<br /><ul><li>Quality of a pharmaceutical product is measured by it’s fitness for intended use. Safety and efficacy are part of quality & not separable from Quality.</li></ul>QA/QC<br />4<br />
  9. 9. Relation Between QM, QA, QC & GMP<br />QA/QC<br />5<br />
  10. 10. What is Quality Management?<br />WHO Definition: <br />The aspect of management functions that determines and implements the ‘quality policy’<br />Responsible quality of pharm. product<br />Product must comply with basic requirements<br />Identity, Strength/ potency, Purity<br />Bioavailabity and Biopharmaceutical parameters<br />Basic Elements of QM<br />Quality system infrastructure & systematic actions<br />QA/QC<br />6<br />
  11. 11. What is Quality Assurance?<br />WHO definition : <br />It is a wide-ranging concept covering all matters that individually or collectively influence the quality of a product. It is the totality of the arrangements made with the object of ensuring that pharm. products are of quality required for their intended use.<br />QA/QC<br />7<br />
  12. 12. What is GMP?<br />GMP is that part of QA which ensures that products are consistently produced and controlled to the quality standards appropriate to their intended use and as required by the Marketing Authorization or product specification.<br />QA/QC<br />8<br />
  13. 13. What is Quality Control?<br /> Is that part of GMP concerned with sampling, specification & testing, documentation & release procedures which ensure that the necessary & relevant tests are performed & the product is released for use only after ascertaining it’s quality<br />QA/QC<br />9<br />
  14. 14. What is difference?<br />Q.A.<br />sum total of organized arrangements made with the object of ensuring that product will be of the Quality required by their intended use.<br />Q.C.<br />concerned with sampling,specifications, testing and with in the organization, documentation and release procedures which ensure that the necessary and relevant tests are carried out<br />QA/QC<br />10<br />
  15. 15. What is difference?<br />Q.A.<br />Systematic actions necessary to provide adequate confidence that a product will satisfy the requirements for quality<br />QA is ORGNIZATION based<br />Responsible for assuring adopted quality policies<br />Q.C.<br />Operational laboratory techniques and activities used to fulfill the requirement of Quality<br />QC is lab based<br />Responsible for day to day quality within org.<br />QA/QC<br />11<br />
  16. 16. Total Quality Control<br />Process of striving to produce a perfect product by a series of measures requiring an organized effort to prevent or eliminate errors at every stage in production <br />QA/QC<br />12<br />
  17. 17. Sources of Quality Variation and its Control<br /> Quality Control<br />
  18. 18. Variation<br />There is no two natural items in any category are the same.<br />Variation may be quite large or very small.<br />If variation very small, it may appear that items are identical, but precision instruments will show differences.<br />
  19. 19. Categories of variation<br />Within-piece variation<br />One portion of surface is rougher than another portion.<br />Apiece-to-piece variation<br />Variation among pieces produced at the same time.<br />Time-to-time variation<br />Service given early would be different from that given later in the day.<br />
  20. 20. Sources of variation<br />Equipment<br />Tool wear, machine vibration, …<br />Material<br />Raw material quality<br />Environment<br />Temperature, pressure, humadity<br />Operator<br />Operator performs- physical & emotional<br />
  21. 21. Control of Quality Variation<br />Raw Materials <br />Q.A. monograph<br />In process Quality Control<br />Q.A. before startup<br />Environmental and microbiologic control, sanitation<br />MWFP<br />Raw materials<br />Mfg. equipment<br />QA/QC<br />17<br />
  22. 22. Control of Quality Variation<br />In process Quality Control<br />Q.A. at startup<br />Raw materials processing<br />Compounding<br />Labels Control<br />Finished product control<br />QA/QC<br />18<br />
  23. 23. Statistical Quality Control<br />Monitoring quality by application of statistical methods in all stages of production<br />QA/QC<br />19<br />
  24. 24. Control Chart Viewpoint<br /><ul><li>Variation due to
  25. 25. Common or chance causes
  26. 26. Assignable causes
  27. 27. Control chart may be used to discover “assignable causes” </li></li></ul><li>Control chart functions<br />Control charts are powerful aids to understanding the performance of a process over time.<br />Output<br />PROCESS<br />Input<br />What’s causing variability?<br />
  28. 28. Control charts identify variation<br />Chance causes - “common cause”<br />inherent to the process or random and not controllable<br />if only common cause present, the process is considered stable or “in control”<br />Assignable causes - “special cause”<br />variation due to outside influences<br />if present, the process is “out of control”<br />
  29. 29. Control charts help us learn more about processes<br />Separate common and special causes of variation<br />Determine whether a process is in a state of statistical control or out-of-control<br />Estimate the process parameters (mean, variation) and assess the performance of a process or its capability<br />
  30. 30. Control charts to monitor processes<br />To monitor output, we use a control chart<br />we check things like the mean, range, standard deviation<br />To monitor a process, we typically use two control charts<br />mean (or some other central tendency measure)<br />variation (typically using range or standard deviation)<br />
  31. 31. Types of Data<br />Variable data<br />Product characteristic that can be measured<br />Length, size, weight, height, time, velocity<br />Attribute data<br />Product characteristic evaluated with a discrete choice<br />Good/bad, yes/no<br />
  32. 32. Control chart for variables<br />Variables are the measurablecharacteristics of a product or service.<br />Measurement data is taken and arrayed on charts.<br />
  33. 33. Control charts for variables <br />X-bar chart<br />In this chart the sample means are plotted in order to control the mean value of a variable (e.g., Fill vol. of liquid , hardness of tablet, etc.).<br />R chart<br />In this chart, the sample ranges are plotted in order to control the variability of a variable. <br />S chart<br />In this chart, the sample standard deviations are plotted in order to control the variability of a variable.<br />S2 chart<br />In this chart, the sample variances are plotted in order to control the variability of a variable. <br />
  34. 34. X-bar and R charts<br />The X- bar chart is developed from the average of each subgroup data. <br />used to detect changes in the mean between subgroups.<br />The R- chart is developed from the ranges of each subgroup data <br />used to detect changes in variation within subgroups<br />
  35. 35. Control chart components<br />Centerline<br />shows where the process average is centered or the central tendency of the data<br />Upper control limit (UCL) and Lower control limit (LCL)<br /> describes the process spread<br />
  36. 36. The Control Chart Method<br />X bar Control Chart:<br />UCL = XDmean + A2 x Rmean <br />LCL = XDmean - A2 x Rmean <br />CL = XDmean <br />R Control Chart:<br />UCL = D4 x Rmean <br />LCL = D3 x Rmean <br />CL = Rmean <br />
  37. 37. Control Chart Examples<br />UCL<br />Nominal<br />Variations<br />LCL<br />Sample number<br />
  38. 38. How to develop a control chart?<br />
  39. 39. Define the problem<br />Use other quality tools to help determine the general problem that’s occurring and the process that’s suspected of causing it.<br />Select a quality characteristic to be measured<br />Identify a characteristic to study - for example, angle of repose or any other variable affecting performance.<br />
  40. 40. Choose a subgroup size to be sampled<br />Choose homogeneous subgroups<br />Homogeneous subgroups are produced under the same conditions, by the same machine, the same operator, the same mold, at approximately the same time.<br />Try to maximize chance to detect differences between subgroups, while minimizing chance for difference with a group.<br />
  41. 41. Collect the data<br />Generally, collect 20-25 subgroups (100 total samples) before calculating the control limits.<br />Each time a subgroup of sample size n is taken, an average is calculated for the subgroup and plotted on the control chart.<br />
  42. 42. Determine trial centerline<br />The centerline should be the population mean, <br />Since it is unknown, we use X Double bar, or the grand average of the subgroup averages.<br />
  43. 43. Determine trial control limits - Xbar chart<br />The normal curve displays the distribution of the sample averages.<br />A control chart is a time-dependent pictorial representation of a normal curve.<br />Processes that are considered under control will have 99.73% of their graphed averages fall within 3.<br />
  44. 44. UCL & LCL calculation<br />
  45. 45. Determining an alternative value for the standard deviation<br />
  46. 46. Determine trial control limits - R chart<br />The range chart shows the spread or dispersion of the individual samples within the subgroup.<br />If the product shows a wide spread, then the individuals within the subgroup are not similar to each other.<br />Equal averages can be deceiving.<br />Calculated similar to x-bar charts;<br />Use D3and D4 <br />
  47. 47. Example: Control Charts for Variable Data<br /> Tablet thickness (mm)<br />Sample 1 2 3 4 5 X R<br /> 1 5.02 5.01 4.94 4.99 4.96 4.98 0.08<br /> 2 5.01 5.03 5.07 4.95 4.96 5.00 0.12<br /> 3 4.99 5.00 4.93 4.92 4.99 4.97 0.08<br /> 4 5.03 4.91 5.01 4.98 4.89 4.96 0.14<br /> 5 4.95 4.92 5.03 5.05 5.01 4.99 0.13<br /> 6 4.97 5.06 5.06 4.96 5.03 5.01 0.10 <br /> 7 5.05 5.01 5.10 4.96 4.99 5.02 0.14 <br /> 8 5.09 5.10 5.00 4.99 5.08 5.05 0.11<br /> 9 5.14 5.10 4.99 5.08 5.09 5.08 0.15 <br /> 10 5.01 4.98 5.08 5.07 4.99 5.03 0.10<br /> 50.09 1.15 <br />
  48. 48. Calculation<br />From Table above:<br />Sigma X-bar = 50.09<br />Sigma R = 1.15<br />m = 10<br />Thus;<br />X-Double bar = 50.09/10 = 5.009 mm<br />R-bar = 1.15/10 = 0.115 mm<br />Note: The control limits are only preliminary with 10 samples.<br />It is desirable to have at least 25 samples.<br />
  49. 49. Trial control limit<br />UCLx-bar = X-D bar + A2 R-bar <br /> = 5.009 + (0.577)(0.115) <br /> = 5.075 mm<br />LCLx-bar = X-D bar - A2 R-bar <br /> = 5.009 - (0.577)(0.115) = 4.943 mm<br />UCLR=D4R-bar=(2.114)(0.115)=0.243 mm<br />LCLR = D3R-bar = (0)(0.115) = 0 cm<br />
  50. 50. 3-Sigma Control Chart Factors<br />Sample size X-chart R-chart<br />nA2D3D4<br /> 2 1.88 0 3.27<br /> 3 1.02 0 2.57<br /> 4 0.73 0 2.28<br /> 5 0.58 0 2.11<br /> 6 0.48 0 2.00<br /> 7 0.42 0.08 1.92<br /> 8 0.37 0.14 1.86<br />
  51. 51. X-bar Chart<br />
  52. 52. R Chart<br />
  53. 53. Run Chart<br />
  54. 54. Another Example of X-bar & R chart <br />
  55. 55. Given Data (Table 5.2)<br />
  56. 56. Calculation<br />From Table 5.2:<br />Sigma X-bar = 160.25<br />Sigma R = 2.19<br />m = 25<br />Thus;<br />X-double bar = 160.25/29 = 6.41 mm<br />R-bar = 2.19/25 = 0.0876 mm<br />
  57. 57. Trial control limit<br />UCLx-bar = X-double bar + A2R-bar = 6.41 + (0.729)(0.0876) = 6.47 mm<br />LCLx-bar = X-double bar - A2R-bar = 6.41 – (0.729)(0.0876) = 6.35 mm<br />UCLR = D4R-bar = (2.282)(0.0876) = 0.20 mm<br />LCLR = D3R-bar = (0)(0.0876) = 0 mm<br />
  58. 58. X-bar Chart<br />
  59. 59. R Chart<br />
  60. 60. Revised CL & Control Limits<br />Calculation based on discarding subgroup 4 & 20 (X-bar chart) and subgroup 18 for R chart:<br /> = (160.25 - 6.65 - 6.51)/(25-2) <br /> = 6.40 mm<br /> = (2.19 - 0.30)/25 - 1 <br /> = 0.079 = 0.08 mm<br />
  61. 61. New Control Limits<br />New value:<br />Using standard value, CL & 3 control limit obtained using formula:<br />
  62. 62. From Table B:<br />A = 1.500 for a subgroup size of 4,<br />d2 = 2.059, D1 = 0, and D2 = 4.698<br />Calculation results:<br />
  63. 63. Trial Control Limits & Revised Control Limit<br />Revised control limits<br />UCL = 6.46<br />CL = 6.40<br />LCL = 6.34<br />UCL = 0.18<br />CL = 0.08<br />LCL = 0<br />
  64. 64. Revise the charts<br />In certain cases, control limits are revised because:<br />out-of-control points were included in the calculation of the control limits.<br />the process is in-control but the within subgroup variation significantly improves.<br />
  65. 65. Revising the charts<br />Interpret the original charts<br />Isolate the causes<br />Take corrective action<br />Revise the chart<br />Only remove points for which you can determine an assignable cause<br />
  66. 66. Process in Control<br />When a process is in control, there occurs a natural pattern of variation.<br />Natural pattern has: <br />About 34% of the plotted point in an imaginary band between 1s on both side CL.<br />About 13.5% in an imaginary band between 1s and 2s on both side CL.<br />About 2.5% of the plotted point in an imaginary band between 2s and 3s on both side CL.<br />
  67. 67. LSL<br />USL<br />Mean<br />-3 -2 -1 +1 +2 +3<br />68.26%<br />95.44%<br />99.74%<br />-3<br />+3<br />CL<br />The Normal<br />Distribution<br /> = Standard deviation<br />
  68. 68. Normal Distribution Review<br /><ul><li>Define the 3-sigma limits for sample means as follows:
  69. 69. What is the probability that the sample means will lie outside 3-sigma limits?
  70. 70. Note that the 3-sigma limits for sample means are different from natural tolerances which are at</li></li></ul><li>Process Out of Control<br />The term out of control is a change in the process due to an assignable cause.<br />When a point (subgroup value) falls outside its control limits, the process is out of control.<br />
  71. 71. Assignable Causes<br />(a) Mean<br />Average<br />Grams<br />
  72. 72. Assignable Causes<br />Average<br />(b) Spread<br />Grams<br />
  73. 73. Assignable Causes<br />Average<br />(c) Shape<br />Grams<br />
  74. 74. Control Charts<br />Assignable causes likely<br />UCL<br />Nominal<br />LCL<br />1 2 3<br />Samples<br />
  75. 75. Control Chart Examples<br />UCL<br />Nominal<br />Variations<br />LCL<br />Sample number<br />
  76. 76. Achieve the purpose<br /><ul><li>Our goal is to decrease the variation inherent in a process over time.
  77. 77. As we improve the process, the spread of the data will continue to decrease.
  78. 78. Quality improves!!</li></li></ul><li>Improvement<br />
  79. 79. Examine the process<br />A process is considered to be stable and in a state of control, or under control, when the performance of the process falls within the statistically calculated control limits and exhibits only chance, or common causes.<br />
  80. 80. Consequences of misinterpreting the process<br />Blaming people for problems that they cannot control<br />Spending time and money looking for problems that do not exist<br />Spending time and money on unnecessary process adjustments<br />Taking action where no action is warranted<br />Asking for worker-related improvements when process improvements are needed first<br />
  81. 81. Process variation<br />When a system is subject to only chance causes of variation, 99.74% of the measurements will fall within 6 standard deviations<br />If 1000 subgroups are measured, 997 will fall within the six sigma limits.<br />Mean<br />-3 -2 -1 +1 +2 +3<br />68.26%<br />95.44%<br />99.74%<br />
  82. 82. Chart zones<br />Based on our knowledge of the normal curve, a control chart exhibits a state of control when:<br /><ul><li>Two thirds of all points are near the center value.
  83. 83. The points appear to float back and forth across the centerline.
  84. 84. The points are balanced on both sides of the centerline.
  85. 85. No points beyond the control limits.
  86. 86. No patterns or trends.</li></li></ul><li>QA/QC<br />75<br />Quality should be built into product and testing alone can not relied on to ensure product quality<br />
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×