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- 1. 1 Training on Statistical Process Control (SPC)
- 2. Process “Any activity, or set of activities that uses resources to transform inputs to outputs can be considered a process.” SPC 2
- 3. PROCESS CONTROLS RESOURCES INPUTS OUTPUT MONITORING AND MEASUREMENT SPC 3
- 4. PROCESS CONTROLS RESOURCES INPUTS OUTPUT MONITORING AND MEASUREMENT SPC 4
- 5. Defect Detection Vs. Prevention • What is a defect? • Defect is deviation from the engineering specifications. • In reality defect is deviation from the targeted value of a characteristics. • The closer we go to the target , more is the customer satisfied and farther we go, we have dissatisfied customer. SPC 5
- 6. 6 Defect Detection Model People Material Methods Machines Environment People Material Methods Machines Environment The way we work Transformation Inputs Process Outputs Intensive Inspection Audits Checks Sampling (Within specs)Ok Not Ok Customer Scrap Rework SPC
- 7. 7 Defect Detection • It uses customer as final inspector • Is reactionary • Tolerates waste such as scrap/rework • Relies on inspection, audits, or checks of large samples of output • Treats all defects the same • Focuses on specs. • Involves action only on output • Provides late feedback for defect • Detection is not cost effective SPC
- 8. 8 Voice of process Every process generates information that can be used to measure it. The most effective way to take advantage of this is to: determine the process quality characteristics & respective target value Collect data about process. Compare process characteristics to pre-established target values. Act based on results of comparison. Following this procedure results in early feedback which leads to improvement SPC
- 9. 9 Defect Prevention Model People Material Methods Machines Environment People Material Methods Machines Environment The way we work Transformation Process Outputs (Within specs)Ok Customer • Reduced customer reported problems • Market research on customer perception of Quality Statistical Methods Timely feedback SPC
- 10. 10 Defect Prevention Is pro-active Avoids waste Uses small samples of product and process information Is analytically based Discriminates between potential defects based on causes Involves action on the process or process parameters Provides timely feedback It is cost effective Focuses on target value SPC
- 11. 11 Test for 100% inspection You are given a paper and in the paragraph written there , you have to find out how many times letter ‘ f ‘ has appeared in the paragraph. Time for visual inspection is 2 minutes SPC
- 12. 12 What is statistics? Even though theory behind statistics is hard to understand, we use it everyday extensively. To talk about sports. To shop intelligently. To complete tasks at home or at work. SPC
- 13. 13 Few fundamentals of Statistics Mean - It is arithmetic average of all observations. Mode -It is most commonly occurring observations. Median - It is the middle observation when all observations are arranged in order of magnitude SPC
- 14. 14 SPREAD Spread is how far apart the ends of the group are. Bowling average 4, 2 , 6 , 0 , 3 , 5 , 2 , 1 , 3 , 4 Average / mean = 3 Spread = 6 Income Rs. 400, 200 , 660 , 90 , 300 , 250 , 100 , 300 , 480 ,180 Average / Mean = 296 Spread=570 Another term that relates spread is Standard Deviation SPC
- 15. 15 Standard Deviation is a statistical measure of spread or variation that is present in the group data. To calculate SD(Sigma : )there are six steps 1. Calculate the average of all the observations. 2. Subtract this average from each observations. 3. Square each number obtained in step 2. 4. Find the sum of all numbers obtained in step 3. 5. Divide the number obtained instep 4 by number of observations minus one. 6. Take the square root of the number obtained in 5. = i=1 (xi-)2 = Average; n= no.of observations n-1 SPC
- 16. 16 Calculate Mean, Median, Mode and 5 ,4 ,4 ,3, 2 SPC
- 17. Variation • Variation is Natural • No two things are alike or identical • Variation is the cause of inconsistency • Variation is reason that components do not always fit together • Variations is the reason that quality levels of same product ,produced on the same line using parts of same suppliers are different. Our goal is to manage Variation SPC 17
- 18. To manage Variation we must understand it. • Where from variation comes ? • Variation can result from different procedures used by a manufacturer or an operator. • Variation can result from different machines used by us. • Variation can result from wear in mechanism of machine. • Variation can result from different batches of material used. Variation can be result of endless list of reasons. SPC 18
- 19. Main sources of variation • In all processes variation is inevitable ,difference between individual outputs of a process. • A process contains many sources of variability. • The main sources of variability are Machine , Method , Material , Men, Environment etc. SPC 19
- 20. Variation There are two causes of variation Common cause Special causes • Common Causes :- A source of variation that affect all the individual values of the process output and inherent in the process itself and can not be eliminated totally. • Special Causes:- A source of variation that is intermittent, unpredictable, unstable. These causes can be identified and can be eliminated permanently. SPC 20
- 21. Variation Characteristics of processes with only common causes of variation The processes are stable I.e. under control Process outcomes are predictable The effects of common cause variation can only be eliminated by action on the system. Common causes of variation Are associated with the majority 85% of process concerns Arise from many small sources. SPC 21
- 22. Variation If only common causes of variation are present ,the output of a process forms a distribution that is stable over time. It is predictable. Variation from common cause is predictable. Variation from common cause is stable The shape is unchanged over time SPC 22
- 23. Variation • If special causes of variation are present ,the output of a process forms a distribution that is not stable over time. It is not predictable. • Variation from special cause is unpredictable. • Variation from special cause is unstable The mean changes over time The spread changes over time • The shape varies over time SPC 23
- 24. Variation • If special causes of variation are present ,the output of a process forms a distribution that is not stable over time. It is not predictable. • Variation from special cause is unpredictable. • Variation from special cause is unstable The mean changes over time The spread changes over time • The shape varies over time SPC 24
- 25. Stable Process • A stable process is the one with no indication of special cause of variation. • Behaviour of stable process is predictable • A process may be stable, but may produce defective output. • A stable process is not an end to improvement. SPC 25
- 26. Variation • The variation due to common causes contributes to 85 % and variation due to special causes contributes to 15 %. • Common causes are management responsibility & hence 85 % it is management responsibility to reduce variation. • Only 15% is workers responsibility. SPC 26
- 27. Principles & Objectives of SPC: -- Variation is inevitable -- Variation is predictable -- Variation is measurable • Statistical process control: 10% is statistics and 90% is product & process knowledge • SPC means applying statistics for output of a process in order to control the process • SPC is preventive technique • Economic control over process SPC 27
- 28. • PROCESS CONTROL:- A process is said to be operating in state of statistical control when the only source of variation is common causes. • PROCESS STABILITY:- A process is said to be stable when the process is in control and variation is constant with respect to time. • PROCESS CAPABILITY:- The measure of inherent variation of the process when it is in stable condition is called as process capability • OVER ADJUSTMENT:- It is the practice of adjusting each deviation from the target as if it were due to a special cause of variation in the process. If stable process is adjusted on the basis of each measurement made, then adjustment comes an additional source of variation. SPC 28
- 29. Techniques for Process control: • Mistake proofing :- In this technique 100% process control is achieved by sealing all types of failures by using modern techniques to get defect free product. Here causes are prevented from making the effect. • 100 % Inspection:- In this technique 100% checking of all the parameters of all products have been done to get defect free product. Here only defect is detected. • Statistical Process Control:- In this Statistical technique such as Control Chart, Histogram etc are used so as to analyse the process and achieve and maintain state of statistical control to get defect free product. Causes are detected and prompting CA before detect occurs. SPC 29
- 30. It is a technique whose aim is to prevent defective work being produced by focusing on the process rather than on the final product ,by using various statistical tools. SPC provides the operator an opportunity of correcting/tuning the process in time. It encourages continual improvement ,which is reflected in the product & the equipment. Statistical Methods are not only control charts. Some of the tools include Pareto Diagrams Check sheets Scatter Diagram Design of Experiments Histogram SPC 30
- 31. Histogram • It provides the information about the distribution of data • It provides information about the spread or variation of data. • It provides information about the location or centrality of distribution. • to display large amounts of data values in a relatively simple chart form. • to tell relative frequency of occurrence. • to easily see the distribution of the data. • to see if there is variation in the data. • to make future predictions based on the data. SPC 31
- 32. Histogram Making Step 1: Gather Data Step 2 : Tabulate data Step 3 : Count the number of data points ‘ n ‘ Step 4 : Determine the Range Range (R) = Max. reading - Min. reading Step 5 : Determine class width ‘ k ’ k = R/ Root of n Step 6 : Determine class boundaries Step 7 : Determine frequency .. Number of readings in each class boundary. Step 8 : Draw histogram with Class width on X axis and frequency on Y axis. SPC 32
- 33. Histogram SPC 33 Normal Distribution Positively Skewed Negatively Skewed Bi-Modal Distribution Multi-Modal Distribution
- 34. Histogram SPC 34 Data of 40 Diameter measurements ( Nominal value 25 mm) 23 22 24 25 27 25 26 24 26 26 25 23 24 27 25 25 22 24 24 26 25 25 25 26 24 23 25 27 28 27 27 28 23 28 28 26 24 23 26 26
- 35. Histogram SPC 35 Data of 40 Diameter measurements ( Nominal value 25 mm) 23 22 24 25 27 25 26 24 26 26 25 23 24 27 25 25 22 24 24 26 25 25 25 26 24 23 25 27 28 27 27 28 23 28 28 26 24 23 26 26 Range= 28 - 22=6 No of Class interval = 6 Interval = Range/Class interval =1 The Boundries - Frequency 21.5 - 22.5 2 22.5 - 23.5 5 23.5 - 24.5 7 24.5 - 25.5 9 25.5 - 26.5 8 26.5 - 27.5 5 27.5 - 28.5 4 Total 40 Histogram 2 5 7 9 8 5 4 0 1 2 3 4 5 6 7 8 9 10 21.5 - 22.5 22.5 - 23.5 23.5 - 24.5 24.5 - 25.5 25.5 - 26.5 26.5 - 27.5 27.5 - 28.5 Diameter Frequency
- 36. Types of data: • Variable data – Are quantitative data that can be counted. Eg. – Distance, diameter, thickness, hardness, length etc. • Attribute data – Are qualitative data that can not be counted. Eg. – Colour, texture, microstructure, go-no-go checking results, yes/no results SPC 36
- 37. SPC 1 2 3 4 5 6 7 8 Sample Number Sample Mean UCLx LCLx UCLr Control Charts
- 38. Control Charts: • Variable Charts – Commonly X-Bar – R Chart is used. X Bar-R chart (Average and Range) explains process data in terms of both its spread (piece to piece variability) and its location (process average). Other variable control charts: Average and SD Chart Median and Range Chart Individual and Moving Range Chart • Attribute Charts – Pass/Fail type measurements. P Chart for proportion of nonconforming nP Chart for number of nonconforming c Chart for number of non conformities u Chart for Non conformities per unit SPC 38
- 39. Control Charts: Benefits • Can be used by operators for ongoing control of a process • Can Help the process perform consistently, predictably for quality and cost • Allow the process to achieve - Higher Quality - Lower Unit cost • Provide a common language for discussing the performance of the process • Distinguish special from common causes of variation, as guide to local action or action on the system SPC 39
- 40. Control Charts In ‘ Statistical Process Control ‘ we have to Gather Data Plot data Calculate and plot control limits Interpret for process control corrective action Calculate process capability Continual Improvement SPC 40
- 41. DATA PLOTTED OVER TIME MONITORED CHARACTERISTIC UCL Center Line LCL UCL = Upper Control Limit / LCL = Lower Control Limit Plotted Data SPC Key Component - Control Charts
- 42. Control Charts for Variables Average and Range Charts (X bar and R) SPC 42
- 43. X bar- R Chart • Preparatory Steps – Establish an environment suitable for action – Define process – Define characteristics to be charted – Define measurement system, do MSA – Minimize unnecessary variation SPC 43
- 44. X bar- R Chart • Gather Data • Choose sub group size o Sensitivity increases with the sub group size o Cost of sampling increases with size o Normally sub group size can be 4 or 5 o Subgroups should be collected often enough & at appropriate times such that they should reflect the potential opportunities for change. For example :Different shifts, Different operators, Different material lots o No. of subgroups : Ensure that the major sources of variation are captured Suggested is 25 nos. or more SPC 44
- 45. X bar- R Chart • Collect the data • Calculate average (X bar) and range (R) of each sub-group • X bar = (X1+X2+……+Xn)/n • R = Xhighest - Xlowest SPC 45
- 46. X bar- R Chart • Collect the data • Calculate average (X bar) and range (R) of each sub-group • X bar = (X1+X2+……+Xn)/n • R = Xhighest - Xlowest SPC 46 Part Operation Other Details Measurement SN Date Time X1 X2 X3 X4 Mean (X bar) Range (R) 1 12/12 10.25 35 40 32 33 35.0 8 2 12/12 13.45 46 42 40 38 41.5 8 3 12/12 15.34 34 40 34 36 36.0 6 ….. 25 15/12 10.30 38 34 44 40 39 10
- 47. X bar- R Chart Select proper scales for control charts Plot Averages and Ranges on control charts Calculate X double bar and R bar: X double bar = (X1 bar + X2 bar + ………..+Xn bar) / n R bar = (R1 + R2 +……….+ Rn) / n SPC 47
- 48. X bar- R Chart • Calculate control limits • For Average control chart Upper Control Limit, UCL = X(Double bar) + A2 x R bar Lower Control Limit, LCL = X(Double bar) - A2 x R bar • For range control chart Upper Control Limit, UCL = D4 x R bar Lower Control Limit, LCL = D3 x R bar SPC 48
- 49. X bar- R Chart • Calculate control limits • For Average control chart Upper Control Limit, UCL = X(Double bar) + A2 x R bar Lower Control Limit, LCL = X(Double bar) - A2 x R bar • For range control chart Upper Control Limit, UCL = D4 x R bar Lower Control Limit, LCL = D3 x R bar SPC 49 Sub Group Size A2 D4 D3 2 1.880 3.267 0 3 1.023 2.527 0 4 0.729 2.282 0 5 0.577 2.115 0 6 0.483 2.004 0 7 0.419 1.924 0.076
- 50. X bar- R Chart Draw lines for the following on the control chart X double bar R bar control limits SPC 50
- 51. X bar- R Chart SPC 51 0 Subgroup 10 20 30 65 70 75 80 1 1 X=72.81 3.0SL=80.71 -3.0SL=64.90 0 10 20 30 1 R=13.70 3.0SL=28.97 -3.0SL=0.00E+00 Xbar/R Chart for Output
- 52. SPC 52 Interpret for process control
- 53. X bar- R Chart SPC 53 UCL LCL 1 Sigma (Zone C) 2 Sigma (Zone B) 3 Sigma (Zone A) 1 Sigma (Zone C) 2 Sigma (Zone B) 3 Sigma (Zone A) The Item We Are Measuring TIME
- 54. X bar- R Chart SPC 54 The Item We Are Measuring TIME 1 Sigma 2 Sigma 3 Sigma 1 Sigma 2 Sigma 3 Sigma 60-75% 90-98% 99-99.9% UCL LCL Rules of Standard Deviation “Where should the data lie?”
- 55. X bar- R Chart What does Out-of-Control mean? Tests for Detecting Lack of Control (For variable control charts) 1) One point more than 3 sigmas from center line- beyond control line 2) Seven points in a row on same side of center line 3) Seven points in a row, all increasing or all decreasing 4) Fourteen points in a row alternating up and down 5) Two out of three points more than two sigmas from center line (Same side) 6) Four out of five points more than one sigma from center line (Same side) 7) Fifteen points in a row within 1 sigma of center line (Either side) 8) Eight points in a row more than one sigma from center line (Either side) SPC 55
- 56. X bar- R Chart SPC 56 30 20 10 0 10 5 0 -5 Observation Number Individual Value I Chart for C1 X=0.2800 3.0SL=5.416 -3.0SL=-4.856 UCL LCL One point more than 3 sigmas from center line
- 57. X bar- R Chart SPC 57 30 20 10 0 10 0 -10 Observation Number Individual Value I Chart for C1 X=0.000 3.0SL=9.000 -3.0SL=-9.000 UCL LCL Fifteen points in a row within 1 sigma of center line (either side)
- 58. X bar- R Chart SPC 58 Therefore, based on what you know so far, what percent of data points should fall between the upper control limit (UCL) and lower control limit (LCL) if your process is in-control? 99 to 99.9 % UCL LCL TIME
- 59. X bar- R Chart Corrective Action Find and address special causes Use Pareto analysis or Cause and effect analysis or Any problem solving technique SPC 59 What should you do if you determine that your process is “Out of Control?”
- 60. X bar- R Chart Corrective Action Take corrective action on identified special causes Exclude any out of control points for which special causes have been found & removed Recalculate & plot the process average and control limits Confirm that all data points show control when compared to the new limits Otherwise repeat the cycle of corrective action SPC 60
- 61. X bar- R Chart Ongoing Control Ensure that process is in control (For trial control limits) Adjust the process to the target, if the process center is off target Extend the control limits to cover future periods Monitor for ongoing control SPC 61
- 62. X bar- R Chart Prepare X bar - R chart using following SPC 62 10.4 13.0 10.4 7.4 6.3 6.1 11.9 9.6 8.2 8.8 10.6 8.6 11.9 11.0 10.0 6.6 8.2 9.5 12.4 9.4 9.6 10.3 8.9 9.1 10.6 11.7 9.4 11.4 10.9 9.3 10.9 9.4 9.1 8.7 11.2 9.7 10.2 9.2 9.8 8.4 9.3 10.5 10.6 8.5 9.5 10.4 10.4 9.8 9.9 5.8
- 63. X bar- R Chart SPC 63 10 9 8 7 6 5 4 3 2 1 Subgroup 0 12 11 10 9 8 7 Sample Mean Mean=9.660 UCL=11.78 LCL=7.541 8 7 6 5 4 3 2 1 0 Sample Range R=3.674 UCL=7.768 LCL=0 Xbar/R Chart for C1
- 64. Control Charts for Attributes p Chart for proportion Nonconforming SPC 64
- 65. p Chart • It is important that – Each component / part / or item being checked is recorded as either conforming or non conforming – Results of these inspections are grouped on a meaningful basis, and non conforming items are expressed as a decimal fraction of the subgroup size. – meaningful basis, and non conforming items are expressed as a decimal fraction of the subgroup SPC 65
- 66. p Chart • Choose sub group size Sensitivity increases with the sub group size Cost of sampling increases with size Normally sub group size can be 50 to 200 or more – are expressed as a decimal fraction of the subgroup SPC 66
- 67. p Chart Subgroups should be collected often enough & at appropriate times such that they should reflect the potential opportunities for change. For example : Different shifts Different operators Different material lots No. of subgroups : Ensure that the major sources of variation are captured Suggested is 25 nos. or more SPC 67
- 68. p Chart Typical Data Collection Sheet – are expressed as a decimal fraction of the subgroup size SPC 68 art Operation Other Details SN Date Time Sample (n) No. of non conforming items in a sample (np) Proportion nonconforming (p = np/n) 1 12/12 10.25 2 12/12 13.45 3 12/12 15.34 ….. 25 15/12 10.30
- 69. p Chart • Select proper scales for control charts • Plot values of p for each subgroup on control charts • Calculate Process Average Proportion Nonconforming (p bar) n1p1, n2p2 : no. of non conforming items in subgroup n1,n2 : Subgroup size SPC 69 p bar = (n1p1 + n2p2 + …+nkpk) (n1 + n2 + …+ nk)
- 70. p Chart Formula for Control Limits – are expressed as a decimal fraction of the subgroup size SPC 70 Upper Control Limit, UCLp = p bar + 3 * Sq. root {p bar (1-p bar)} Sq. root (n) Lower Control Limit, LCLp = p bar - 3 * Sq. root {p bar (1-p bar)} Sq. root (n) n is constant sample size n n is constant sample size
- 71. p Chart Formula for Control Limits – are expressed as a decimal fraction of the subgroup size SPC 71 Upper Control Limit, UCLp = p bar + 3 * Sq. root {p bar (1-p bar)} Sq. root (n bar) Lower Control Limit, LCLp = p bar - 3 * Sq. root {p bar (1-p bar)} Sq. root (n bar) n is not constant sample size It is varying between +/- 25%
- 72. p Chart Formula for Control Limits – are expressed as a decimal fraction of the subgroup size SPC 72 If n is not constant sample size and varying beyond +/-25%, then recalculate the precise control limits by using following formula Upper Control Limit, UCLp = p bar + 3 * Sq. root {p bar (1-p bar)} Sq. root (n) Lower Control Limit, LCLp = p bar - 3 * Sq. root {p bar (1-p bar)} Sq. root (n)
- 73. p Chart SPC 73 Draw lines for the following on the control chart Process average (p bar) control limits
- 74. p Chart Interpret for Process Control 1) One point more than 3 sigmas from center line 2) Nine points in a row on same side of center line 3) Six points in a row, all increasing or all decreasing 4) Fourteen points in a row alternating up and down To be interpreted same as charts for variables SPC 74
- 75. p Chart Interpret for Process Control 1) Find and correct special causes 2) Recalculate control limits 3) Monitor for on going control Refer slides in X bar - R chart section SPC 75
- 77. SPC 77 Not Accurate & Not Precise Not Accurate But Precise Accurate & Not Precise Accurate & Precise
- 78. SPC 78 Control Limits vs. Specification Limits • Process Control Limits are calculated based on data from the process itself • They are based on +/- 3 (99.73% of the process variation is expected to fall between these limits) • Product Specification Limits ARE NOT found on the control chart • Understanding how the process matches up against customer requirements IS important to know To determine how the process performs to Customer Expectations, a Process Capability Study is required
- 79. SPC 79 Control Limits vs. Specification Limits TWO BIG CONTROL CHART ERRORS 1) Putting specification limits on a Control Chart 2 ) Treating UCL and LCL as a specification limit When you do either of these the control chart becomes just an inspection tool - it’s no longer a control chart UCL / LCL are not directly tied to customer defects !
- 80. Relationship between Control Limits & Spec. Limits USL LSL + 3 - 3 Process natural limits are inside specification limits and the process is centered nominal Nominal SPC
- 81. 81 Relationship between Control Limits & Spec. Limits USL LSL + 3 - 3 Process natural limits are inside specification limits and the process is not centered nominal Nominal SPC
- 82. 82 Relationship between Control Limits & Spec. Limits USL LSL + 3 - 3 Process natural limits are outside specification limits and the process is centered nominal Nominal Out of spec Out of spec SPC
- 83. 83 Relationship between Control Limits & Spec. Limits USL LSL + 3 - 3 Process natural limits are outside specification limits and the process is not centered nominal Nominal SPC
- 84. 84 PROCESS CAPABILTY RATIOS SPC Cp & Cpk are two key measures of process capability Cp = USL – LSL ie Total Tolerance 6 Sigma Process Spread Cpk = Min X bar - LSL , USL – X bar Cpk accounts for process centering and spread 3 sigma 3 sigma Cpk will always be equal to or less than Cp Standard Deviation, Sigma = R bar/ d2 d2 is a constant varying by sample size, n 2 3 4 5 6 7 8 9 10 d2 1.13 1.69 2.06 2.33 2.53 2.70 2.85 2.97 3.08
- 85. 27 mm = LSL USL = 33 mm xbar = 30 mm s = 1 3 3 Cp = __________ s s C USL - LSL 6 p s SPC 85
- 86. 3 3s s xbar = 35 mm s = 1 mm Cp = __________ C USL - LSL 6 p s 27 mm = LSL USL = 33 mm SPC
- 87. CpU = ___________ CpL = ___________ Cpk = ____________ 3 3 s s xbar = 35 mm s = 1 mm 28 mm = LSL USL = 33 mm SPC
- 88. SPC Pre control Chart : Out of specifications Out of specifications Specifications Specifications ½ Specifications ½ Specifications
- 89. SPC Pre control: The logic:-probability based on normal distribution Specifications/2 GREEN ZONE Yellow zone Yellow zone RED ZONE RED ZONE 1/14 for 1 point out of green zone 1/14 * 1/14 = 1/196 for 2 consecutive points If even one point falls in Red zone, Stop and reset!!
- 90. SPC Pre control: The logic:-contd… If two consecutive points are outside the two pre control lines : May mean that the process variation has increased! Use a sample of two consecutive measurements A & B. If A is green , continue. If A is yellow check B and if B is also yellow, STOPAND INVESTIGATE. To qualify the process / set up: Take two consecutive measurements. If both Green, O.K. If one yellow, Restart the count. If both Yellow, Reset.
- 91. 92 Short term Capability Study SPC Short term capability : Based on measurements collected from say one operating run. The data are analyzed and checked further for the state of statistical control. If no special causes are found, a short term capability index can be calculated. This is useful for PPAP submissions, machine capability studies, check for process modifications, etc. Process Performance indices calculated are called Pp and Ppk
- 92. 93 Short term Capability Study SPC Pp & Ppk Cp &Cpk PROCESS PERFORMANCE INDEX PROCESS CAPABILITY INDEX USED DURING INITIAL PROCESS STUDY DURING PPAP ONGOING PROCESS CAPABILITY STUDY CAN BE CAPTURED FOR STABLE AND CHRONICALLY UNSTABLE PROCESSES USED ONLY FOR STABLE PROCESSES
- 93. 94 Short term Capability Study SPC Pp & Ppk Cp &Cpk CAPTURES VARIATION DUE TO BOTH COMMON & SPECIAL CAUSES CAPTURES VARIATION DUE TO COMMON CAUSES ONLY SIGMA IS CALCULATED USING n-1 FORMULA USING ALL INDIVIDUAL READINGS SIGMA IS CALCULATED USING R bar / d2 FORMULA Ppk > 1.67 Cpk 1.33 – 1.67
- 94. Thank You for Your Time 95