Tools For Recognizing And Quantifying Process Drift Statistical Process Control (SPC)

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1 Tools For Recognizing And Quantifying Process Drift Statistical Process Control (SPC) J. Scott Tarpley GE Intelligent Platforms, Inc. December, 200

2 Process Analytical Technology (PAT) brings us? Timely Data Analyses Improved Measurement Capability - Precision & Accuracy Increased Sample Sizes More data, but what do we do with it? Significant mistakes without proper understanding!

3 Topics of Discussion Time-Ordered and Global Data Analyses SPC Basics and Special Cause Tests Common Application Mistakes Summary Q&A

4 The components of a Quincunx: Hopper of beads which allows a drop of one bead at a time with the target determined by the operator

5 The components of a Quincunx: The process can shift by moving the hopper as needed

6 The components of a Quincunx: The pins represent common causes of variation!

7 The components of a Quincunx: The bins represent the measurement or result

8 What if one pin looked like this Is this one a common or special cause? (i.e., does it have a special influence on the process?)

9 What other ways could special causes be created (or identified) on this quincunx?

10 Global Analysis versus Time-Order Analysis A global analysis of the data does not take time ordering into account. For example, the resulting histogram or dot plot of data from the results of,000 dropped beads in the quincunx would be a global analysis of data. Let s take a look at the potential risk of missing important process behavior or process performance information by only looking at the data from a global viewpoint as opposed to considering the analysis in time order. In 924, Dr. Walter Shewhart drew his first control chart to study this process behavior considering time-ordering instead of a global analysis. * data on following 3 slides from the book, Normality and the Process Behavior Chart, written by Dr. Donald J. Wheeler

11 Summary for Wheeler Quincunx Example A nderson-d arling N ormality T est A -S quared.66 P -V alue < M ean S td ev V ariance S kew ness K urtosis N M inimum st Q uartile M edian rd Q uartile M aximum % C onfidence Interv al for M ean % C onfidence Interv al for M edian % C onfidence Inter vals 95% C onfidence Interv al for S td ev Mean Median This is an example of global analysis not in time order! What did we learn about process behavior?

12 Sa mple Range Sample M ean Xbar-R Chart of Wheeler Quincunx Example U C L=3.03 _ X= LC L= Sa m ple U C L= _ R= LC L= Sa m ple The same data in time order what do we learn?

13 Topics of Discussion Time-Ordered and Global Data Analyses SPC Basics and Special Cause Tests Common Application Mistakes Summary Q&A

14 Elements of a Control Chart The Mean is in the middle and the Upper (UCL) and Lower (LCL) Control Limits are defined by going up and down (+/-) 3 standard deviations from the mean as the general formula for CL s. Control limits are the VOICE OF THE PROCESS (VOP)!!! UCL Mean LCL +3s -3s

15 Traditional Tests for Special Causes: Rule Any data point outside the Control Limits UCL Mean LCL

16 Traditional Tests for Special Causes: Rule 2 6 Consecutive Data Points Increasing or Decreasing ( Trend ) UCL Mean LCL *Notice how none of the data points are outside the CL s!!!

17 Traditional Tests for Special Causes: Rule 3 8 Consecutive Data Points On One Side of the Centerline ( Shift ) UCL Mean LCL A shift is often the result of a SHOCK to the system new batch of materials, maintenance, shift change, process improvement implementation, etc.

18 Traditional Tests for Special Causes: Rule 4 4 Consecutive Data Points Alternating Up and Down ( Sawtooth ) UCL Mean LCL A sawtooth is often the result of a constant tweaking of the process material usage shift-to-shift, overreaction to natural variability, etc.

19 Additional Tests for Special Causes: 5. 2 out of 3 data points more than 2 standard deviations from the centerline (same side) UCL Mean LCL +2s +s -s -2s 6. 4 out of 5 data points more than standard deviation from the centerline (same side) 7. 5 data points consecutively within +/- standard deviation of the centerline (either side) 8. 8 data points consecutively outside +/- standard deviation of the centerline (either side) These tests are typically used for more advanced control

20 Topics of Discussion Time-Ordered and Global Data Analyses SPC Basics and Special Cause Tests Common Application Mistakes Summary Q&A

21 Control charting myths A. Data must be normally distributed before a control chart can be used B. Individual data must be independent and have no auto-correlation with other collected data C. Data must be in control before a control chart can be utilized

22 Common mistakes. Treating a common cause of variation as if it is special (Deming called this tampering ) 2. Ignoring a special cause of variation (Treating as if common) 3. Using specification limits as control limits (Spec limits are the VOC voice of the customer) 4. Do not refresh control limits often enough (Data from many years ago affecting current process) Design the control charts to scream at you whenever and however you desire

23 Individual Value Common mistakes 5. Too much data I Chart for Dissolution SL= X= SL= Observation Number

24 Common mistakes 6. Focusing on select few data points in time (Same month year before and previous month) Nov 2009 Oct 200 Nov 200 Potential for being fooled into believing no change and missing out on important process information

25 Values Moving Range Individual Value Common mistakes 7. Measurement system not granular enough Process Capability Sixpack of SPECIFIC VOLUME (AS IS) I Chart UCL=.9573 _ X=.793 LSL Capability Histogram USL S pecifications LS L.6 USL LCL= Moving Range Chart UCL=0.207 Normal Prob Plot A D: 0.660, P: < MR= LCL= Last 2 5 Observations Observation 5 Within S td ev C p.52 C pk.8 Capability Plot Within O v erall Specs O v erall S td ev Pp.24 Ppk 0.96 C pm *

26 Topics of Discussion Time-Ordered and Global Data Analyses SPC Basics and Special Cause Tests Common Application Mistakes Summary Q&A

27 Suggestions. Develop a rational subgrouping strategy 2. Invest in shop floor SPC software 3. Institute a broad QC Plan 4. Deploy the correct control charts (I/MR, X-Bar/R, p, etc.) 5. Avoid mass deployment overnight 6. Proper training is critical 7. Like all initiatives, leadership is required!!!

28 Values Moving Range Individual Value Example Process Capability Sixpack of Core Weight I Chart Capability H istogram UCL= _ X= LCL= Moving Range Chart UCL=.782 Normal Prob Plot A D: 0.30, P: MR= LCL= Last 2 5 Observations Capability Plot Observation Within S tdev C p 0.34 C pk 0.07 C C pk 0.34 Within Overall Specs O v erall S td ev Pp 0.48 Ppk 0.20 C pm *

Values Moving Range Individual Value Example Process Capability Sixpack of Dissolution 20 min Low 04 I Chart UCL=0.98 Capability H istogram 96 _ X=93.5 88 6 6 2 26 3 36 4 46 LCL=85.

29 Values Moving Range Individual Value Example Process Capability Sixpack of Dissolution 20 min Low 04 I Chart UCL=0.98 Capability H istogram 96 _ X= LCL= Moving Range Chart UCL=0.40 Normal Prob Plot A D: 0.390, P: MR= LCL= Last 2 5 Observations Observation Within S tdev C p * C pk C C pk Capability Plot Within O v erall Specs O v erall S td ev Pp * Ppk 0.7 C pm *

30 Contact Info Thank you for your attention! I welcome your feedback! Scott Tarpley Product Quality Manager GE Intelligent Platforms, Inc. Embedded Systems Business (Phone) ( ) james.tarpley@ge.com Visit GEIP on the net at:

Statistical Techniques for Validation Sampling. Copyright GCI, Inc. 2016

Statistical Techniques for Validation Sampling Tie Risk to Sampling Data Type Confidence Level Reliability and Risk Typical Performance Levels One-sided or two-sided spec Distribution (variables) Risk