1. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Philippine Institute of Pure and Applied Chemistry CONTROL CHARTS Dr. Ronald M. Fabicon

2. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress  Learning Objectives  The Control Chart Techniques  State of Introduction  Control  Specifications  Process Capability  Six Sigma  Different Control Charts  Control Charts in Analytical Laboratories OUTLINE

3. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress LEARNING OBJECTIVES When you have completed this part of the training course you should:  Know the three categories of variation and their sources.  Understand the concept of the control chart method.  Know the purpose of variable control charts.  Know how to select the quality characteristics, the rational subgroup and the method of taking samples

4. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress When you have completed this part of the training course you should:  Be able to calculate the central value, trial control limits and the revised control limits for X bar and R chart.  Be able to explain what is meant by a process in control and the various out-of-control patterns.  Know the difference between individual measurements and averages; control limits and specifications. LEARNING OBJECTIVES

5. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress When you have completed this training you should:  Know the different situations between the process spread and specifications and what can be done to correct the undesirable situation.  Be able to calculate process capability.  Know the statistical meaning of 6σ LEARNING OBJECTIVES

6. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress The variation concept is a law of nature in that no two natural items in any category are the same. VARIATION

7. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress  The variation may be quite large and easily noticeable  The variation may be very small. It may appear that items are identical; however, precision instruments will show difference  The ability to measure variation is necessary before it can be controlled VARIATION

8. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress VARIATION There are three categories of variation in piece part production: 1.Within-piece variation: Surface 2.Piece-to-piece variation: Among pieces produced at the same time 3.Time-to-time variation: Difference in product produced at different times of the day

9. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Materials Tools OperatorsMethods Measurement Instruments Instruments HumanInspectionPerformance EnvironmentMachines INPUTS PROCESS OUTPUTS Sources of Variation in production processes: VARIATION

10. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Sources of variation are: 1.Equipment: 1.Toolwear 2.Machine vibration 3.Electrical fluctuations etc. 2.Material 1.Tensile strength 2.Ductility 3.Thickness 4.Porosity etc. VARIATION

11. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Sources of variation are: 3. Environment 1. Temperature 2. Light 3. Radiation 4. Humidity etc. 4. Operator 1. Personal problem 2. Physical problem etc. VARIATION

12. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress There is also a reported variation which is due to the inspection activity. Variation due to inspection should account for one tenth of the four other sources of variation. VARIATION

13. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Variation may be due to chance causes (random causes) or assignable causes. When only chance causes are present, then the process is said to be in a state of statistical control. The process is stable and predictable. VARIATION

14. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHART Is a plot of a parameter being monitored (at the y-axis) versus run number (x-axis) Lines in the control chart: Mean line Warning Limits Critical/Action Limits

15. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHART

16. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHART Upper Critical Limit (UCL) = x + 3s Upper Warning Limit (UWL) = x + 2s Lower Warning Limit (LWL) = x - 2s Lower Critical Limit (UCL) = x - 3s Mean = x

17. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Variable data  x-bar and R-charts  x-bar and s-charts  Charts for individuals (x-charts) Attribute data  For “defectives” (p-chart, np-chart)  For “defects” (c-chart, u-chart) CONTROL CHARTS

18. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Control Charts R Chart Variables Charts Attributes Charts X Chart P C Continuous Numerical Data Categorical or Discrete Numerical Data CONTROL CHARTS

19. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress The control chart for variables is a means of visualizing the variations that occur in the central tendency and the mean of a set of observations. It shows whether or not a process is in a stable state. CONTROL CHART FOR VARIABLES

20. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Example of a control chart CONTROL CHARTS CONTROL CHARTS

21. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Control Charts Figure 5-1 Example of a method of reporting inspection results

22. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress The objectives of the variable control charts are :  For quality improvement  To determine the process capability  For decisions regarding product specifications  For current decisions on the production process  For current decisions on recently produced items VARIABLE CONTROL CHARTS

23. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Procedure for establishing a pair of control charts for the average X bar and the range R: 1. Select the quality characteristic 2. Choose the rational subgroup 3. Collect the data 4. Determine the trial center line and control limits 5. Establish the revised central line and control limits 6. Achieve the objective CONTROL CHART TECHNIQUES

24. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress The Quality Characteristic must be measurable. It can expressed in terms of the seven basic units: 1. Length 2. Mass 3. Time 4. Electrical current 5. Temperature 6. Substance 7. Luminosity, etc QUALITY CHARACTERISTICS

25. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress A rational subgroup is one in which the variation within a group is due only to chance causes. Within-subgroup variation is used to determine the control limits. Variation between subgroups is used to evaluate long- term stability. RATIONAL SUBGROUP

26. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress There are two schemes for selecting the subgroup samples: 1. Select subgroup samples from product or service produced at one instant of time or as close to that instant as possible 2. Select from product or service produced over a period of time that is representative of all the products or services RATIONAL SUBGROUP

27. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress The first scheme will have a minimum variation within a subgroup. The second scheme will have a minimum variation among subgroups. The first scheme is the most commonly used since it provides a particular time reference for determining assignable causes. The second scheme provides better overall results and will provide a more accurate picture of the quality. RATIONAL SUBGROUP

28. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress  As the subgroup size increases, the control limits become closer to the central value, which make the control chart more sensitive to small variations in the process average  As the subgroup size increases, the inspection cost per subgroup increases  When destructive testing is used and the item is expensive, a small subgroup size is required SUBGROUP SIZE

29. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress  From a statistical basis a distribution of subgroup averages are nearly normal for groups of 4 or more even when samples are taken from a non-normal distribution.  When a subgroup size of 10 or more is used, the s chart should be used instead of the R chart. SUBGROUP SIZE

30. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress It is necessary to collect a minimum of 25 subgroups of data. A run chart can be used to analyze the data in the development stage of a product or prior to a state of statistical control DATA COLLECTION

31. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Central Lines are obtained using: TRIAL CENTRAL LINES

32. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Trial control limits are established at ±3 standard deviations from the central value TRIAL CONTROL LIMITS

33. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress In practice calculations are simplified by using the following equations where A 2, D 3 and D 4 are factors that vary with the subgroup size and are found in Table B of the Appendix. TRIAL CONTROL LIMITS

34. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress

35. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress pH meters in a certain analytical laboratory are calibrated daily at the start of the working day (8:00 AM). A standard pH 7.000 solution was read using the instrument at the end of the day. The following data were collected over a 25 day period. Determine center line and trial control limits. TRIAL CONTROL LIMITS - SAMPLE

36. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress TRIAL CONTROL LIMITS - SAMPLE

37. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress REVISED CENTRAL LINES

38. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress REVISED CENTRAL LINES

39. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Continuing use of control charts, showing improved quality ACHIEVING THE QUALITY OBJECTIVE

40. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Process in Control  When special causes have been eliminated from the process to the extent that the points plotted on the control chart remain within the control limits, the process is in a state of control  When a process is in control, there occurs a natural pattern of variation STATE OF CONTROL

41. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Natural pattern of variation of a control chart STATE OF CONTROL

42. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Types of errors:  Type I, occurs when looking for a special cause of variation when in reality a common cause is present  Type II, occurs when assuming that a common cause of variation is present when in reality there is a special cause STATE OF CONTROL

43. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress When the process is in control: 1.Individual units of the product or service will be more uniform 2.Since the product is more uniform, fewer samples are needed to judge the quality 3.The process capability or spread of the process is easily attained from 6ơ 4.Trouble can be anticipated before it occurs STATE OF CONTROL

44. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress When the process is in control: 5.The % of product that falls within any pair of values is more predictable 6.It allows the consumer to use the producer’s data 7.It is an indication that the operator is performing satisfactorily STATE OF CONTROL

45. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Common Causes Special Causes 45

46. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Frequency Distribution of subgroup averages with control limits STATE OF CONTROL

47. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress When a point (subgroup value) falls outside its control limits, the process is out of control. Out of control means a change in the process due to a special cause. A process can also be considered out of control even when the points fall inside the 3ơ limits STATE OF CONTROL

48. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress  It is not natural for seven or more consecutive points to be above or below the central line.  Also when 10 out of 11 points or 12 out of 14 points are located on one side of the central line, it is unnatural.  Six points in a row are steadily increasing or decreasing indicate an out of control situation STATE OF CONTROL

49. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 1.Change or jump in level. 2.Trend or steady change in level 3.Recurring cycles 4.Two populations (also called mixture) 5.Mistakes OUT OF CONTROL CONDITIONS

50. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Some unnatural runs-process out of control PATTERNS IN CONTROL CHARTS

51. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Simplified rule for out-of-control pattern PATTERNS IN CONTROL CHARTS

52. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Change or jump in level Trend or steady change in level Recurring cyclesTwo populations OUT OF CONTROL PATTERNS

53. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Comparison of individual values compared to averages SPECIFICATIONS

54. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Calculations of the average for both the individual values and for the subgroup averages are the same. However the sample standard deviation is different. SPECIFICATIONS

55. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress If the population from which samples are taken is not normal, the distribution of sample averages will tend toward normality provided that the sample size, n, is at least 4. This tendency gets better and better as the sample size gets larger. The standardized normal can be used for the distribution averages with the modification. CENTRAL LIMIT THEOREM

56. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Illustration of central limit theorem CENTRAL LIMIT THEOREM

57. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Figure 5-20 Dice illustration of central limit theorem CENTRAL LIMIT THEOREM

58. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Relationship of limits, specifications, and distributions CONTROL LIMITS AND SPECIFICATIONS

59. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress  The control limits are established as a function of the average  Specifications are the permissible variation in the size of the part and are, therefore, for individual values  The specifications or tolerance limits are established by design engineers to meet a particular function CONTROL LIMITS AND SPECIFICATIONS

60. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress  The process spread will be referred to as the process capability and is equal to 6σ  The difference between specifications is called the tolerance  When the tolerance is established by the design engineer without regard to the spread of the process, undesirable situations can result PROCESS CAPABILITY AND TOLERANCE

61. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Three situations are possible:  Case I: When the process capability is less than the tolerance 6σ<USL-LSL  Case II: When the process capability is equal to the tolerance 6σ=USL-LSL  Case III: When the process capability is greater than the tolerance 6σ >USL-LSL PROCESS CAPABILITY AND TOLERANCE

62. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Case I: When the process capability is less than the tolerance 6σ<USL-LSL Case I 6σ<USL-LSL PROCESS CAPABILITY AND TOLERANCE

63. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Case II: When the process capability is less than the tolerance 6σ=USL-LSL Case I 6σ=USL-LSL PROCESS CAPABILITY AND TOLERANCE

64. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Case III: When the process capability is less than the tolerance 6σ>USL-LSL Case I 6σ>USL-LSL PROCESS CAPABILITY AND TOLERANCE

65. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress  The range over which the natural variation of a process occurs as determined by the system of common causes  Measured by the proportion of output that can be produced within design specifications PROCESS CAPABILITY

66. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress This following method of calculating the process capability assumes that the process is stable or in statistical control:  Take 25 (g) subgroups of size 4 for a total of 100 measurements  Calculate the range, R, for each subgroup  Calculate the average range, RBar= ΣR/g  Calculate the estimate of the population standard deviation  Process capability will equal 6σ 0 PROCESS CAPABILITY

67. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Charts for Better Operator Understanding: 1.Placing individual values on the chart: This technique plots both the individual values and the subgroup average. Not recommended since it does not provide much information. 2.Chart for subgroup sums: This technique plots the subgroup sum, ΣX, rather than the group average, Xbar. DIFFERENT CONTROL CHARTS

68. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Charts for Variable Subgroup Size: Used when the sample size is not the same  Different control limits for each subgroup  As n increases, limits become narrower  As n decreases, limits become wider apart  Difficult to interpret and explain  To be avoided DIFFERENT CONTROL CHARTS

69. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Chart for Trends: Used when the plotted points have an upward or downward trend that can be attributed to an unnatural pattern of variation or a natural pattern such as tool wear. The central line is on a slope, therefore its equation must be determined. DIFFERENT CONTROL CHARTS

70. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress DIFFERENT CONTROL CHARTS Chart for Trend

71. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Used when we cannot have multiple observations per time period ValueXbarR 44 46 5448.0010 3846.0016 4947.0016 4644.3311 4546.674 3140.6715 5543.6724 3741.0024 4244.6718 4340.676 4744.005 5147.008 n here is equal to 12, NOT 14 An example CONTROL CHARTS FOR MOVING AVERAGE

72. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Extreme readings have a greater effect than in conventional charts. An extreme value is used several times in the calculations, the number of times depends on the averaging period. CONTROL CHARTS FOR MOVING AVERAGE

73. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress This is a simplified variable control chart.  Minimizes calculations  Easier to understand  Can be easily maintained by operators  Recommended to use a subgroup of 3, then all data is used. CONTROL CHARTS FOR MEDIAN AND RANGE

74. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CHART FOR INDIVIDUAL VALUES Used when only one measurement is taken on quality characteristic  Too expensive  Time consuming  Destructive  Very few items

75. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Non-Acceptance limits have the same Relationship to averages as specifications have to individual values. Control Limits tell what the process is capable of doing, and reject limits tell when the product is conforming to specifications. CONTROL CHARTS with NON-ACCEPTANCE LIMITS

76. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Figure 5-35 Relationship of non-acceptance limits, control limits and specifications. CONTROL CHARTS with NON-ACCEPTANCE LIMITS

77. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES

78. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES ISO/IEC 17025 (3) 5.9: “The laboratory shall have quality control procedures for monitoring the validity of tests and calibrations undertaken. The resulting data shall be recorded in such a way that trends are detectable and statistical techniques shall be applied for reviewing the results. Quality control data shall be analyzed and where they are found to be outside predefined criteria, planned action shall be taken to correct the problem and to prevent incorrect results from being reported.”

79. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES Internal quality control at the chemical analytical laboratory, involves a continuous, critical evaluation of the laboratory’s own analytical methods and working routines. The control encompasses the analytical process starting with the sample entering the laboratory and ending with the analytical report.

80. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES The most important tool in this quality control is the use of control charts. The basis is that the laboratory runs control samples together with the routine samples. The control values are plotted in a control chart. In this way it is possible to demonstrate that the measurement procedure performs within given limits. If the control value is outside the limits, no analytical results are reported and remedial actions have to be taken to identify the sources of error, and to remove such errors.

81. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress

82. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES When the control program encompasses the whole analytical process from the sample entering the laboratory to the analytical report the control results will demonstrate the within-laboratory reproducibility. The within-laboratory reproducibility will indicate the variation in the analytical results if the same sample is given to the laboratory at different times.

83. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES The results of the control program may be used in several ways –  The analyst will have an important quality tool in his/her daily work,  The customer can get an impression of the laboratory’s quality  The laboratory can use the results in the estimation of the measurement uncertainty.

84. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES The QC has to be part of a quality system and should be formally reviewed on a regular basis. Other important elements of the quality system are the participation in interlaboratory comparisons (proficiency test), the use of certified reference materials and method validation. In practical work it is necessary that the quality control is limited to fulfilling the requirements on the analytical results – a good balance between control work and analyses of samples is essential.

85. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES  Control charting is a powerful and simple tool for the daily quality control of routine analytical work. The basis is that the laboratory runs control samples together with the routine samples in an analytical run.  Material of control samples can be standard solutions, real routine samples, blank samples, in-house control materials and certified reference materials.  Immediately after the analytical run is completed the control values are plotted on a control chart.

86. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES From the requirement on the analytical results the analyst sets up the control program: Type of quality control sample Type of quality control charts Control limits – warning and action limits (or warning and control limits) Control frequency

87. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL SAMPLES

88. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL SAMPLES Control samples - should go through the whole measurement procedure - very similar to test samples - stable over time - suitable analyte concentration

89. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL SAMPLES TYPESAMPLES ICertified Reference Materials (CRM) IIReference Materials, Standard Solutions, In House Material IIIBlank Sample IVTest Sample

90. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL SAMPLES Should be representative with respect to matrix and concentration Its content should be chosen so that analytically important regions (e.g. limit regions) can be secured Should be available in sufficient quantity, so that control analyses can be performed over a long period of time

91. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL SAMPLES Its stability must be proven and should be at least several months (under defined storage conditions) Influence of the containers on the shelf-life must be excluded Regular removal of partial samples for control analyses must not lead to changes in the control sample (e.g. evaporation of highly volatile components through opening of the container)

92. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress TYPES of CONTROL SAMPLES Standard Solutions –A solution of a standard substance of known purity in a suitable solvent, a solvent as similar as possible to that of the sample Blank Samples –An analysis sample that is presumably free of the component to be determined Natural Samples (Reference Material) –A sample whose matrix best resembles the sample being analyzed. The content of these actual samples must be analytically determined beforehand through validation studies

93. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress TYPES of CONTROL SAMPLES Synthetic Samples –A standard solution that contains other components (possibly interferring components or components to resemble the matrix) Certified Refence Material (CRM) –Are natural or synthetic samples whose content is determined by various laboratories by means of various methods

94. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Control limits may be set according to the performance of the analytical method used irrespectively of the requirement on analytical quality – statistical control limits. The control limits (X-charts) are set based on the analytical performance of a control sample over a long period of time e. g a year. Warning limts are: +2s and -2s Action limits are:+3s and -3s SETTING THE CONTROL LIMITS

95. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress For R charts The control limits are based on the analytical performance of the control sample over a long period of time Central Line = Mean Range/d2 SETTING THE CONTROL LIMITS

96. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress THE CENTRAL LINE The central line (CL) in the control chart is representing the mean value of the control values or the nominal value of a certified reference material. In addition to the central line, the control chart normally has four lines. Two of these, the so-called warning limits, are located at a distance of ± two times the standard deviation from the central line (CL ± 2s). Provided that the results are normally distributed, about 95 % of the results should be within these limits.

97. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL LIMITS  In the control chart two other lines are also drawn at a distance of ± three times the standard deviation from the central line (CL ± 3s).  These lines are called the action limits and 99.7 % of the data normally distributed should be within these limits.  Statistically only three out of 1000 measurements are thus located outside the action limits. If the control value is outside the action limits, there is a high probability that the analysis is in error.

98. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CENTRAL LINE and CONTROL LIMITS The control limits can be set based on method performance – statistical control limits or according to the requirement on within-laboratory reproducibility – target control limits. The central line in the control chart can be the calculated mean value of the control values or a reference value for the control sample. In most cases a mean central line is used.

99. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES Statistical control limits The control limits are set based on the analytical performance of the control sample. From a longer time period, e.g. a year, the standard deviation s is calculated from the control values. Warning limits will be +2 s and – 2 s. Action limits will be +3 s and – 3 s.

100. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES Target control limits The control limits are set based on the requirement on the analytical quality. The standard deviation for the control chart, s, is estimated from the requirement on sRw Warning limits will be +2 s and – 2 s. Action limits will be +3 s and – 3 s.

101. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CENTRAL LINE MEAN CENTRAL LINE The mean value is estimated from control values obtained during a longer time, e.g. a year. The central line is set to this mean value. REFERENCE CENTRAL LINE The control sample is a reference material or a well- characterized material. The central line is set to the nominal value.

102. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES Setting up a quality control programe  Control samples types  Concentration ranges  Frequency of control analyses  Position of control samples in an analytical run  A good balance between QC and test samples is important  Daily interpretation of quality control  Long term evaluation of quality control data

103. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress DAILY INTERPRETATION OF QUALITY CONTROL Three possible cases: The method is in control The method is in control but the long term evaluation shows that the method is out of statistical control The method is out of control

104. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress DAILY INTERPRETATION OF QUALITY CONTROL The method is in control: the control value is within warning limits. the control value is between warning limits and action limit and the two previous control values are within warning limits. The Analyst can report the analytical results!!

105. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress DAILY INTERPRETATION OF QUALITY CONTROL The method is in control but out of statistical control seven control values in consecutive order gradually increase or decrease 10 out of 11 values are lying on the same side of the central line. In these cases, the Analyst can report the analytical results but a problem may be developing: Each laboratory has to decide how to treat this trends!

106. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress DAILY INTERPRETATION OF QUALITY CONTROL The method is out of control: the control value is outside the control limits. the control value is between warning limits and action limit and at least one of the two previous control values is also between the warning and action limit. All samples analyzed since last control value in control must be reanalyzed.

107. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress What is the quality currently in the laboratory? Has the quality significantly changed? Are the control limits and central line in the control chart still optimal for detecting situations out of control? LONG TERM EVALUATION

108. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress REVIEW OF CURRENT QUALITY: Review the last 60 data points with at least 20 of them new. Count the number of cases where the results are outside the warning limits. If more than 6 or less than 1 – the spread of analysis has changed. LONG TERM EVALUATION

109. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress REVIEW OF CURRENT QUALITY: Review the last 60 data points with at least 20 of them new. Calculate the mean of the last 60 results and compare with the CL. If difference is more than 0.35s – means the mean value has changed. LONG TERM EVALUATION

110. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress EXAMPLE

111. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Internal Quality Control, Nordtest Report TR569, 2011

112. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress

113. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress

114. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES How often should control limits be evaluated? For successful use of control charts it is important that the control limits and the central line remain stable over a long period of time. The central line and control limits should not be changed frequently since this will make it difficult to detect gradual changes in analytical quality. The laboratory should have a policy for how often control limits are evaluated and how it is decided if a change is needed.

115. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES How often should control limits be evaluated? Control limits and central line should be evaluated every year or after collection of 20 (or 30) data sets as indicated above. But the evaluation does not necessarily mean that the control limits should be changed. A change should only be considered if a significant change in spread or the bias has taken place.

116. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress CONTROL CHARTS IN LABORATORIES Other uses of quality control data and control charts:  Measurement uncertainty  Method validation  Method comparison  Estimation of limit of detection (LOD)  Person comparison or qualification  Evaluation of proficiency tests  Environmental parameters and similar checks

117. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress Thank You!

118. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress STEPS TO CREATE A CONTROL CHART

119. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 1. Check to see that your data meets the following criteria: Data should usually be normally distributed revolving around a mean (average). Measurements need to be independent of one another. In the example, the measurements are in subgroups. The data in the subgroups should be independent of the measurement number; each data point will have a subgroup and a measurement number.

120. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 2.Find the mean of each subgroup. To find the mean, add all measurements in the subgroup and divide by the number of measurements in the subgroup. In the example, there are 20 subgroups and in each subgroup there are 4 measurements.

121. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 3.Find the mean of all of the means from the previous step (X). This will give you the overall mean of all the data points. The overall mean will be the centerline in the graph (CL), which is 13.75 for our example.

122. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 4.Calculate the standard deviation (S) of the data points.

123. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 5.Calculate the upper and lower control limits (UCL, LCL) using the following formula: UCL = CL + 3*S LCL = CL – 3*S The formula represents 3 standard deviations above and 3 standard deviations below the mean respectively.

124. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 6. Draw a line at each deviation. In the above example, there is a line drawn at one, two, and three standard deviations (sigma’s) away from the mean. Zone C is 1 sigma away from the mean Zone B is 2 sigma away from the mean Zone A is 3 sigma away from the mean

125. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 7.Graph the X-bar Control Chart, by graphing the subgroup means (x-axis) versus measurements (y-axis).

126. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 8.Evaluate the graph to see if the process is out- of-control. The graph is out-of-control if any of the following are true: Any point falls beyond the red zone (above or below the 3-sigma line). 8 consecutive points fall on one side of the centerline. 2 of 3 consecutive points fall within zone A. 4 of 5 consecutive points fall within zone A and/or zone B. 15 consecutive points are within Zone C. 8 consecutive points not in zone C.

127. Philippine Institute of Pure and Applied Chemistry In partnership with industry, A catalyst for progress 9.State whether the system is in-control or out- of-control.