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Control Charts in Lab and Trend Analysis

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Control Charts in Lab and Trend Analysis

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  1. USE OF CONTROL CHARTS IN LABS AND TREND ANALYSIS BY: YAMINI BHARDWAJ SIGMA TEST & RESEARCH CENTRE Email: Mail@sigmatest.org

  2. Why control charts and trend analysis • According to ISO/IEC 17025:2017 clause 7.7.1 Ensuring the Validity of Results. • The laboratory shall have a procedure for monitoring the validity of tests results. • The resulting data shall be recorded in such a way that trends are detectable and, where practicable, statistical techniques shall be applied to review the results. WWW.SIGMATEST.ORG

  3. CONTROL CHARTS • The control chart: chart on which some statistical measure of a series of sample is plotted in a timely order to steer the process with respect to that measure and to control and reduce variation. • By comparing current data with existing control charts, one can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).  WWW.SIGMATEST.ORG

  4. Theoretical basis of control chart • The control chart is a graphical display of data from process which allow a visual assessment of the process variability. • At defined intervals, subgroup of items of a specified size are obtained and value of characteristic or feature of the item is determined. The data obtained is summarized through use of statistics and these statistics are plotted on control chart. • A control chart consists of :- Central line : it reflects the level around which plotted statistics are expected to vary. Warning Limits : it reflect that increased attention to be paid to the process when the point of observations fall outside the warning limit but inside the control limits. Control Limits/ Action Limits: these are placed on both side of the central line defining the band within which the statistic can be expected to lie randomly when process is in control. WWW.SIGMATEST.ORG

  5. Theoretical basis of control chart WWW.SIGMATEST.ORG

  6. Benefits of using control charts • A very powerful tool for internal quality control • Changes in the quality of analysis can be detected very rapidly • Easier to demonstrate ones quality and proficiency to clients and auditors. • Indicate if the process is stable or not • Estimate the magnitude of the inherent variability of the process. • Identify, investigate and reduce the effect of special causes of variability. • Identification of patterns of variability such as trends, cycle, runs etc. • Assist in the assessment of the performance of a measurement system. WWW.SIGMATEST.ORG

  7. Types of control charts WWW.SIGMATEST.ORG

  8. Shewhart control chart: Control chart with shewhart control limits intended to distinguish between variation in a plotted measure due to random causes and that due to special causes. It is a graph of the values of a given subgroup characteristic versus the subgroup number. The control limits used are 3-sigma control limits. Control charts with no pre specified control limits : It is used to detect any lack of control in R&D stages, or in earlier pilot trials or initial studies. Control charts with pre specified control limits : it is based on adopted standard values applicable to statistical measures plotted on the chart. The standard values are based on: • Prior representative data. • Desired target value defined in specification. • An economic value derived from consideration of needs of service and cost of production. • Acceptance control chart: Control charts intended to evaluate whether or not the plotted measure can be expected to satisfy specified tolerance. WWW.SIGMATEST.ORG

  9. Shewhart Control chart WWW.SIGMATEST.ORG

  10. Types of Shewhart control charts WWW.SIGMATEST.ORG

  11. Variable Control Chart Control charts for variables is a means of visualizing the variations that occurred in the central tendency and mean of a set of observation. Benefits:- • Most of the process have the output characteristic that are measurable. So applicability is broad. • The measurement value contains more information. • The performance of the process can be analysed without regard to the specification. • The subgroup size of variables are much smaller than that of attribute charts so are more efficient. WWW.SIGMATEST.ORG

  12. Control limit formulae for shewhart variables control charts WWW.SIGMATEST.ORG

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  14. Mean charts • This type of chart graphs the means (or averages) of a set of samples, plotted in order to monitor the mean of a variable. • Mainly for precision check • This graph shows changes in process and is affected by changes in process variability. • It shows erratic and cyclic shifts in the process. • It can also detect steady process changes like equipment wear. WWW.SIGMATEST.ORG

  15. Range charts (r-chart) • An R-chart is a type of control chart used to monitor the process variability (as the range) when measuring small subgroups (n ≤ 10) at regular intervals from a process. • It is important for repeatability precision check. • For better understanding of the trend and variation in the process -R charts are used together. WWW.SIGMATEST.ORG

  16. -range control charts • CASE -1: No standard values given. Table 1 shows measurement of outside radius of a plug. Four measurements are taken every half an hour for a total of 20 samples. And the specified tolerance are 0.219 dm and 0.125 dm. WWW.SIGMATEST.ORG

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  20. On examination the chart reveal that last three points are out of control and it indicate that some cause of variation may be operating. • At this point remedial action is required and charting is continued by establishing revised control limits by discarding the out of control points. WWW.SIGMATEST.ORG

  21. Revised control limits WWW.SIGMATEST.ORG

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  23. CASE 2 -: Standard values given. The tea importer wants to control his packaging process such that the mean weight of packages is 100.6 g and based on previous packaging processes the standard deviation is 1.4g. Table 2 shows the subgroup average and subgroup average of 25 samples of size 5. WWW.SIGMATEST.ORG

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  27. Control charts for individuals, X and moving range R • Control charts for individuals are plotted when there is no rational subgroup possible to provide inter batch variability or when cost required for measurement is high so that repeated observations are not possible. • Moving range is the absolute difference between successive pair of measurements in a series. WWW.SIGMATEST.ORG

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  29. Cautions while preparing moving range charts:- • This chart is not sensitive to process change as mean and range chart. • Care should be taken in interpretation if the process distribution is not normal • This chart does not isolate piece-to-piece repeatability of a process. WWW.SIGMATEST.ORG

  30. Case study :- The table shows the result of laboratory analysis of % moisture samples of 10 successive lots of skim milk powder. As the sampling variation is negligible, so it was decided to take only one observation per lot. WWW.SIGMATEST.ORG

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  33. Recovery control charts • These are charts created using a blank matrix that has been spiked with a known concentration of analyte. • We chart the percent recovery of the spike. As long as the results fall within specified criteria, the QC passes. • A typical acceptance for matrix spikes is 70 – 120%, but for large screens with many analytes, often 50 – 150% is acceptable WWW.SIGMATEST.ORG

  34. The following data were obtained for the repetitive spike recoveries of field samples. WWW.SIGMATEST.ORG

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  36. Control chart for Duplicate samples • An effective method for determining the precision of an analysis is to analyze duplicate samples. • Duplicate samples are obtained by dividing a single gross sample into two parts • We report the results for the duplicate samples, X1 and X2, by determining the standard deviation and relative standard deviation, between the two samples WWW.SIGMATEST.ORG

  37. ILLUSTRATION Consider the following analysis data of duplicate samples WWW.SIGMATEST.ORG

  38. Determination of central line and control limits. Central line = Std. Dev ( s )/Grand Average { X }*100 Upper control limit (UCL)= (UCL)s/ X *100 (UCL)s = B4 s Lower control limit (LCL)= (LCL)s/ X *100 (LCL)s = B3 s Here, B4 is the function of number of observation in subgroup. (n) Here, n=2 so from table B4 =3.267 WWW.SIGMATEST.ORG

  39. UCL WWW.SIGMATEST.ORG

  40. Applications in testing laboratory WWW.SIGMATEST.ORG

  41. Estimation of Measurement Uncertainty. Results from the control charts can, together with other data be used for calculating the measurement uncertainty, it may give a realistic estimate of the measurement uncertainty. • Method Validation /Verification When the method has been changed only slightly, or if a standard method is adopted in the laboratory, control charts can be used to complement that the process is still under control. • Performance of equipment. Equipment control charts can be drawn to monitor the bias, changes due to ageing, wear, drift & noise. WWW.SIGMATEST.ORG

  42. Method Comparison By plotting control charts for two methods in parallel, it is easy to compare important information: • spread (from the standard deviation or from the range) • bias (if a CRM is used) • matrix effects (interferences), if spiking or a matrix CRM is used • robustness, i.e. if one method is more sensitive to temperature shifts, handling etc. • Method Blank and Reagent blank Monitoring. The control chart drawn for matrix blank/reagent blank can help to monitor the contamination occurring in a process due to cross contamination, gradual build-up of the contaminant, procedure failure or instrument instability. WWW.SIGMATEST.ORG

  43. Person comparison or qualification Control charts are helpful in comparing the performance of different persons in the laboratory. control charts can be employed during training and qualifying new staff in the laboratory. It is a powerful tool to estimate inter-analyst variation. • Environmental parameters checks. The control charts give a very simple graphical presentation of any trends or unexpected variation that might influence the analyses. • Control charts can also help to identify the effect of matrix on the recovery of the analyte. WWW.SIGMATEST.ORG

  44. process control WWW.SIGMATEST.ORG

  45. Westgard rules • Westgard Rules are multirule QC rules to help analyze whether or not an analytical run is in-control or out-of-control. • It uses a combination of decision criteria, usually 5 different control rules to judge the acceptability of an analytical run. • The advantages of multirule QC procedures are that false rejection can be kept low while at the same time maintaining high error detection. This is done by selecting individual rules that have very low levels of false rejection, then building up the error detection by using these rules together WWW.SIGMATEST.ORG

  46. Rule 1-2s Definition: The 1-2s Control Rule indicates one control result has exceeded the established mean +/- 2SD range. This is a “warning rule,” which does not indicate an “out-of-control” condition, but is intended to initiate further testing. Interpretation:If no other control rule is violated, then the warning is attributed to normal random error. Patient results are acceptable. Corrective Action: No corrective action is required. However, the “warning” suggests a system error may be in the development. A comprehensive check of the routine maintenance schedule and review of the quality control & handling and sampling technique is recommended. WWW.SIGMATEST.ORG

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  48. Rule 1-3s Definition: The 1-3s Control Rule indicates one control result has exceeded the established mean +/- 3SD range. This is a “rejection rule,” which is sensitive to random error. Interpretation: Excessive random error exists. The analyzer is “out-of-control.” The results are not acceptable and should be re-analyzed after corrective actions have solved the problem. Corrective Action: Rerun the quality control level that is in question, emphasizing proper technique. If the repeated level is within +/- 2SD range then the problem can be attributed to random error. If the repeated level exceeds the +/- 2SD range, then further corrective action should be conducted. The following are probable causes: • Inadequate or wrong +/- 2SD range. • Improper storage temperature correction of quality control results. • Improper technique when handling the quality control. Change of quality control batch. • Inadequate maintenance of the instrument. WWW.SIGMATEST.ORG

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  50. Rule 2-2s Definition: The 2-2s Control Rule indicates that two consecutive control results have exceeded the same mean +/- 2SD limit. This is a “rejection rule,” which is sensitive to systematic errors. Interpretation: A systematic error exists. The analyzer is “out-of-control.” This may be an early indicator for a “shift” in the mean value. Patient results are not acceptable and should be re-analyzed after corrective action has solved the problem. Corrective Action: To resolve systematic errors, corrective action should be conducted to address the following probable causes: • Inadequate or wrong +/- 2SD range. • Improper technique when handling the quality control. • Improper storage temperature correction of the quality control results. • Change of the quality control batch. • Inadequate maintenance of the instrument. WWW.SIGMATEST.ORG

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