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Lean Six Sigma DMAIC process: Common Mistakes and Misconceptions During Data Collection and Analysis Lean Six Sigma DMAIC process: Common Mistakes and Misconceptions During Data Collection and Analysis Hans Vanhaute 04/08/ /08/2014

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Goal of tonights presentation Give you a few examples of common mistakes made during Measure phase of DMAIC projects. Draw more widely applicable lessons and conclusions that may benefit you (so you dont make the same mistakes). Hopefully provide you with some interesting insights (and dont put you to sleep).

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DMAIC and Projects A problem scheduled for a solution. Management decides the problem is important enough to provide the resources it needs to get the problem solved. A problem scheduled for a solution. Management decides the problem is important enough to provide the resources it needs to get the problem solved.

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DMAIC Projects Six Sigma DMAIC Project Six Sigma DMAIC Project Very Data-Intensive

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M – Measure Define a high-level process map. Define the measurement plan. Test the measurement system (Gauge Study). Collect the data to objectively establish current baseline. Typical tools: - Capability analysis - Gage R&Rs The DMAIC steps

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Y = f (unknown Xs) Initial Analysis Capability Analysis Conundrums Black Box Process Unknown Xs Black Box Process Unknown Xs C pk values that inaccurately predict process performance Non-normal data Instability of the process over time

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Case 1: Inherent non-normality of the process output. Some physical, chemical, transactional processes will produce outcomes that lean one way: -Time measurements -Values close to zero, but that are always positive (surface roughness RMS…) -… Process experts or careful analysis of the metric should be able to help with understanding. Some physical, chemical, transactional processes will produce outcomes that lean one way: -Time measurements -Values close to zero, but that are always positive (surface roughness RMS…) -… Process experts or careful analysis of the metric should be able to help with understanding. Capability Analysis Conundrums Good news: Capability Analysis of Non-Normal data is possible. Bad news: This situation doesnt happen very often. Good news: Capability Analysis of Non-Normal data is possible. Bad news: This situation doesnt happen very often.

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Case 2: Problematic measurement systems (well come back to that one when we discuss GR&R…) Capability Analysis Conundrums

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Case 3: Failure to stratify the data. Stratification is the separation of data into categories. It means to break-up the data to see what it tells you. Its most frequent use is when diagnosing a problem and identifying which categories contribute to the problem being solved. Stratification is the separation of data into categories. It means to break-up the data to see what it tells you. Its most frequent use is when diagnosing a problem and identifying which categories contribute to the problem being solved. Capability Analysis Conundrums This is the big one!

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Stream 1 Stream 2 Stream 3 Stream 4 C pk1 C pk2 C pk3 C pk4 C pk ??? Capability Analysis Conundrums

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1 C pk value? 2 C pk values? What is a C pk value supposed to tell us? Expected future performance of the process(es) assuming statistical stability over time. Capability Analysis Conundrums

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Over-estimating variation of the process. (Why?) Under-estimating process capability. Leading to all sorts of non-value-added activity for your organization. Over-estimating variation of the process. (Why?) Under-estimating process capability. Leading to all sorts of non-value-added activity for your organization. Recognize two of the four streams are main drivers of overall capability. Correct estimation of the two most important process capabilities. Points to appropriate improvement activities. Recognize two of the four streams are main drivers of overall capability. Correct estimation of the two most important process capabilities. Points to appropriate improvement activities. Capability Analysis Conundrums

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C pk = 0.67 C pk = 1.15 C pk = 1.50 C pk = 1.15 Prediction (stratified) Prediction (not stratified) Actual data (stratified) Actual data (not stratified) Capability Analysis Conundrums

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Problematic measurement systems: 2a: Limiting factors to how well you can measure something. 2b: I passed my GR&R but Im still getting weird results. 2c: Time effects Capability Analysis Conundrums

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Case 2a: Limits to measurements Game: Identify the dataset with the highest resolution. Resolution: a: The process or capability of making distinguishable the individual parts of an object, closely adjacent optical images, or sources of light b: A measure of the sharpness of an image or of the fineness with which a device can produce or record such an image. Resolution: a: The process or capability of making distinguishable the individual parts of an object, closely adjacent optical images, or sources of light b: A measure of the sharpness of an image or of the fineness with which a device can produce or record such an image.

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Case 2a: Limits to measurements Which dataset has the highest resolution? ? ? Measurement Resolution: a: The process or capability of making distinguishable the individual parts of a dataset or closely adjacent data points. b: A measure of the sharpness of a set of data or of the fineness with which a measurement device can produce or record such a dataset. Measurement Resolution: a: The process or capability of making distinguishable the individual parts of a dataset or closely adjacent data points. b: A measure of the sharpness of a set of data or of the fineness with which a measurement device can produce or record such a dataset.

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Limiting factors to how well you can measure something. Case 2a: Limits to measurements

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Limiting factors to how well you can measure something. Case 2a: Limits to measurements

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Limiting factors to how well you can measure something. S = S = (0.5% over) S = (4% over) S = (15% over) Case 2a: Limits to measurements

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Limiting factors to how well you can measure something: Case 2a: Limits to measurements Why?? Always done it that way, never given it any thought. Focus on meeting specs not on controlling process. Always round to x decimal places. Nobody told me how many decimals were needed … Why?? Always done it that way, never given it any thought. Focus on meeting specs not on controlling process. Always round to x decimal places. Nobody told me how many decimals were needed … The old 1 in 10 rule of thumb seems to make sense. Resolution must be at least 1/10 th of data range Resolution must be at least 1/10 th of spec range The old 1 in 10 rule of thumb seems to make sense. Resolution must be at least 1/10 th of data range Resolution must be at least 1/10 th of spec range

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Limiting factors to how well you can measure something: Case 2a: Limits to measurements

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Case 2b: Weird Stuff I passed my GR&R but Im still getting weird results. Distribution of Measurements Distribution of Measurements Distribution of measurement variability Distribution of measurement variability GR&R 101: P/TV ratio expresses the total measurement variability as a percentage of the total historical process variation. Here P/TV ~ 14% P/TV ratio expresses the total measurement variability as a percentage of the total historical process variation. Here P/TV ~ 14% Metrics

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Case 2b: Weird Stuff I passed my GR&R but Im still getting weird results. Distribution of measurement error Distribution of measurement error GR&R 101: Metrics P/T expresses the total measurement variability as a percentage of the tolerance width of the process: Here P/T ~ 12.5% P/T expresses the total measurement variability as a percentage of the tolerance width of the process: Here P/T ~ 12.5% Spec. limits

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Case 2b: Weird Stuff I passed my GR&R but Im still getting weird results. GR&R 101: Metrics P/TVP/T Very good <10% Marginal 10 – 30% Needs Improvement > 30% Simple, right? Not so fast…

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Case 2b: Weird Stuff I passed my GR&R but Im still getting weird results. R chart by operator: Points inside control limits indicate that operator is consistent between repeat measurements made on same sample (GOOD) Points outside control limits indicate that operator is not consistent between repeat measurements made on same sample (BAD) R chart by operator: Points inside control limits indicate that operator is consistent between repeat measurements made on same sample (GOOD) Points outside control limits indicate that operator is not consistent between repeat measurements made on same sample (BAD)

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Case 2b: Weird Stuff I passed my GR&R but Im still getting weird results. P/T = 22%P/TV = 16%

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Case 2b: Weird Stuff I passed my GR&R but Im still getting weird results. P/T = 70% P/TV = 40% P/T = 10% P/TV = 6%

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Case 2b: Weird Stuff I passed my GR&R but Im still getting weird results. So… what caused this? Camera Lens Ring Light Pin Tip Position

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Case 2c: Time Effects The speed of Information is finite. Information can come from different distances.

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Case 2c: Time Effects The speed of Information is finite. Information can come from different distances. Moon: 1.2 light-seconds awaySun: 8 light-minutes awayMars: 12.5 light-minutes awayPluto: 5.5 light-hours awayProxima Centauri: 4.2 light-years away

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Case 2c: Time Effects The speed of Information is finite. Information can come from different distances. Just because you observe (measure / see) several events at the same time, doesnt mean they all occur(red) at the same time.

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Case 2c: Time Effects Arranged by order of occurrence Arranged by order of observation

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Case 2c: Time Effects What can you do? Collect the data as close as possible to the origin of the event you are observing. Traceability of the events you are observing. De-convolution of the data. What can you do? Collect the data as close as possible to the origin of the event you are observing. Traceability of the events you are observing. De-convolution of the data. In mathematics, de-convolution is an algorithm-based process used to reverse the effects of convolution on recorded data

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Blind reliance on some index value (C pk, C p, P/T, P/TV,…) to tell you what is going on might get you in trouble. Always: -Make sure you understand how the index is calculated -Use the approach fully, not half-way -Verify that all assumptions were met Data stratification opportunities abound. Identify them early on in your project. A few simple rules of thumb will quickly help you determine if you have a chance of having a good measurement system. Blind reliance on some index value (C pk, C p, P/T, P/TV,…) to tell you what is going on might get you in trouble. Always: -Make sure you understand how the index is calculated -Use the approach fully, not half-way -Verify that all assumptions were met Data stratification opportunities abound. Identify them early on in your project. A few simple rules of thumb will quickly help you determine if you have a chance of having a good measurement system. So… What did we learn?

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Further analysis of the Gage R&R data can provide you with some great insights into and improvement opportunities for your measurement process. Data has a finite speed. Being aware of this and planning for it during your measure phase will help keep you on the right track. Further analysis of the Gage R&R data can provide you with some great insights into and improvement opportunities for your measurement process. Data has a finite speed. Being aware of this and planning for it during your measure phase will help keep you on the right track. So… What did we learn?

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Parting Thoughts My organization doesnt use Six Sigma, do these insights benefit me as well?

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Thank You Questions?

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