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Measurement Systems Analysis: What is it and why should I care? Dec. 11, 2012 Barry Kulback Global Lean Six Sigma Leader Trane and Thermo King, brands.

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Presentation on theme: "Measurement Systems Analysis: What is it and why should I care? Dec. 11, 2012 Barry Kulback Global Lean Six Sigma Leader Trane and Thermo King, brands."— Presentation transcript:

1 Measurement Systems Analysis: What is it and why should I care? Dec. 11, 2012 Barry Kulback Global Lean Six Sigma Leader Trane and Thermo King, brands of Ingersoll Rand

2 2 Agenda About Ingersoll Rand Measurement systems and Measurement System Error What is Measurement System Analysis Types of Measurement Systems Analysis and examples Lessons Learned Feel free to ask questions at any time!

3 3 About Ingersoll Rand A $14 billion diversified industrial company Publicly-held; NYSE:IR Operations in every major geographic region Strategic brands are #1 or #2 in their markets Products and services for commercial, industrial and residential markets

4 4 What do you think about my measurement system?

5 5 We do a lot of measuring in our businesses and in any process improvement methodology… Running the business  Monitor process performance Improving the business  Baseline  Set the levels of the adjustments  Getting better or worse  Validate improvement results … and many of the measurement systems we use for this have similar problems as the one shown!

6 6 When not measuring well… Current process performance may be misjudged Process improvement results may be misinterpreted Missed opportunities Wasted effort, $

7 7 5 years of college in physics & chemistry labs…. Not once was it ever discussed… how does your ability to measure influence the results you think you are seeing?

8 8 DMAIC process improvement methodology Define Measure Analyze Improve Control My ‘aha’ moment came in my Six Sigma Black Belt Training Measurement System Analysis Collect data Six Sigma practitioners are known as Green Belt, Black Belts and Master Black Belts

9 9 Premise for Six Sigma Methods Sources of variation can be –Identified –Quantified –Eliminated by control or prevention Y = f(x) Data driven decisions with a known level of confidence… … we do a lot of measuring in Six Sigma

10 10 What is an MSA? When measuring there is always Measurement System Error An MSA is a procedure to assess a Measurement System –Quantifies the Measurement System Error –Acceptable? Yes or no If ‘no’  improve the measurement system –MSA output can tell you where to look

11 11 For 2 of the 3 types of MSA’s we’ll cover today Guidelines –Trained Operator(s) –Proper Method –Representative Samples Generally two to three operators Each unit is measured or assessed 2-3 times by each operator Results are then analyzed Often with statistical software like Minitab Analytical and graphical outputs explain the results Conducting an MSA

12 12 Types of Data Continuous Data (Quantitative) –Decimal subdivisions are meaningful –Time (seconds) –Pressure (psi) –Conveyor Speed (ft/min) –Rate (inches) –Temperature (degrees) Attribute Data (Qualitative) –Categories –Good / Bad –Inventory Classification Code A, B or C –Shift number –Counted things (# receipt errors, # units shipped, etc.)

13 13 Types of Measurement Systems Analysis Continuous Data  Gage R&R Attribute Data  Attribute Agreement Analysis Data Scrub MSA

14 14 … but first just a wee bit of ‘technical’

15 15 so… ==

16 16 Types of Measurement Systems Analysis Continuous Data  Gage R&R Attribute Data  Attribute Agreement Analysis Data Scrub MSA

17 17 How does measurement system error appear? LSLUSL Actual process variation - No measurement error Observed process variation - With measurement error LSLUSL

18 18 Can’t really understand the true variation present! And if it isn’t understood, it can’t be fixed. Why do we care? LSLUSL Observed process variation - With measurement error xxxx A ‘bad’ part can measure good A ‘good’ part can measure bad

19 19 Possible Sources of Process Variation Observed Process Variation Actual Process Variation Long TermShort TermWithin Sample Measurement Variation Due to instrument RepeatabilityCalibrationStabilityLinearity Due to operators

20 20 Possible Sources of Process Variation Observed Process Variation Actual Process Variation Long TermShort TermWithin Sample Measurement Variation Due to instrument RepeatabilityCalibrationStabilityLinearity Due to operators

21 21 Observed Process Variation Actual Process Variation Long TermShort TermWithin Sample Measurement Variation Due to instrument RepeatabilityCalibrationStabilityLinearity Due to operators Possible Sources of Process Variation

22 22 Observed Process Variation Actual Process Variation Long TermShort TermWithin Sample Measurement Variation Due to instrument RepeatabilityCalibrationStabilityLinearity Due to operators Possible Sources of Process Variation ‘Repeatability’ and ‘Reproducibility’ are the two main contributors to Measurement System Error – hence ‘Gage R&R’

23 23 If Measurement System Error always exists, when should we be concerned with it? -- When it it too large. Too large compared to what? -- That depends on what you are using the measurement system for!

24 24 Gage R&R – case study Process: Compressor Machining Project: Six Sigma Black Belt Project Scrap Reduction $375,000 in scrap / year Capacity bound  lost margins on lost sales Considering $1M CAPX to increase capacity

25 25 LSL USL Actual Observed machined casting Inspection; CMM – measures to.000001 inch assembly into compressor Define Measure Analyze Improve Control Measurement system improved  project complete!

26 26 Types of Measurement Systems Analysis Continuous Data  Gage R&R Attribute Data  Attribute Agreement Analysis Data Scrub MSA

27 27 Attribute Measurement Systems Assessing attribute data often involved judgment … sometimes a little “it’s broke / it isn’t” or “it fits / it doesn’t” … sometimes a lot “ it dented too bad, scrap it”

28 28 It all starts with an Operational Definition: The ‘spec’ Describes what the defects or categories are Describes how to perform the appraisal assessment Used to train those performing the assessment Should be applied with a high degree of consistency Attribute Measurement Systems

29 29 Attribute Agreement Analysis – case study Process: 2” Copper Tube Bending Project: Six Sigma Green Belt Project Scrap Reduction

30 30

31 31 Spec = “No Wrinkles” What’s a wrinkle? How bad is bad?

32 32 How good are our Operators at assessing if a tube is wrinkled and should not be used? Not wrinkled, use it. Slight wrinkle, use it? That’s not a wrinkle, it’s a tool mark! Use it? Not a wrinkle, a stretch mark! Use it? Wrinkled, scrap it.

33 33 An Attribute MSA was conducted: l 30 Samples l 4 Operators l 1 ‘Expert’ l 2 Trials

34 34 TedMichaelJimDannyAlbner 100 90 80 70 Appraiser P e r c e n t Within Appraiser Assessment Agreement Date of study: Reported by: Name of product: Misc: [,]95.0% CI Percent ‘Expert’! want 90% level of agreement or higher

35 35 Spec = “No Wrinkles” – OK! Would useWould not use ?

36 36 Visual Aid Added Prep for future new employees...

37 37 Types of Measurement Systems Analysis Continuous Data  Gage R&R Attribute Data  Attribute Agreement Analysis Data Scrub MSA

38 38 Sometimes we get our data out of the ‘system’ … is it right?

39 39 Data Scrub MSA – case study Process:Cooling the office Project:Six Sigma Green Belt Project Reduce Energy Consumption

40 40 Lessons Learned…

41 41 An expensive gage does not always mean good measurements

42 42 Remember this? Inspection; CMM – measures to.000001 inch  $145,000 used  Must operate in a environmentally controlled room  Strict procedures on part handling, cleanliness, controlling local conditions, controlling part temperature…

43 43 A measurement system is more than a gage or a operational definition (specification)

44 44 A continuous variable measurement system is composed of: the gage / measuring device operator techniques set-up and handling techniques the environment in which the measurements are being done (ex. lighting, access) recording of measurement results

45 45 Operational Definition Training of Operators Application The Attribute Measurement System Problems in any of these areas can lead to too high a degree of inconsistent / incorrect assessments

46 46 Just because a measurement system has been in use ‘forever’ doesn’t mean it is very good

47 47 Attribute MSA – case study Process: Invoicing – Application of appropriate tax Project: Six Sigma Black Belt Project Improve DSR

48 48 What tax should be applied? Sales Tax -- a percentage added to invoice, customer pays Use Tax -- a percentage of the cost of the goods, company pays Non-Tax -- government, hospital, etc., where neither customer or company pays Tax Codes are applied to invoices being sent out to Customers:

49 49 How good are the Accountants at applying the correct tax code? (been doing it for years…)

50 50 An Attribute MSA was conducted: l 10 Samples (more would be better) l 3 Operators -- who do the job every day l 1 ‘Expert’ l 2 Trials

51 51

52 52

53 53 Just because a it came out of the computer doesn’t mean it is accurate

54 54 Data Scrub MSA’s – case studies Focused Management Team monthly published results

55 55 Recap About Ingersoll Rand Measurement systems and Measurement System Error What is Measurement System Analysis Types of Measurement Systems Analysis and examples Lessons Learned

56 56

57 57 Final lesson: You cannot assume you are measuring well

58 58 Measurement error is always present You don’t know if it is small enough to ignore unless you assess it!

59 59 Addendum

60 60 LSLUSL If using the measurement system to see if the item being measured is within the spec, you want the Measurement System Error (MSE) to be small compared to that spec: MSE specification range, or tolerance sample observed measurements This.. should be small when compared to this... The P/T ratio quantifies this... A ratio of MSE to the tolerance range x

61 61 LSLUSL If using the measurement system to analyze the variation present or control the process with Statistical Process Control, you want MSE to be small compared to that variation: MSE sample observed measurements This.. should be small when compared to this... The %R&R quantifies this... A ratio of MSE to process variation x

62 62 Dec. 11, 2012 Professional Development Meeting TOPIC AREA: MEASUREMENT SYSTEMS Measurement System Analysis: What is it and why should I care? SPEAKER – Barry Kulback, Master Black Belt, Six Sigma For our December Professional Development Meeting we are pleased to have Barry Kulback, Master Black Belt. Measurement System Analysis (MSA) is an often overlooked critical step in any process improvement process and is the linchpin of Six Sigma ’ s Measure phase in the DMAIC (Define, Measure, Analyze, Improve, and Control) methodology. This non-technical presentation will review what MSA ’ s are and why they are important. The major components of measurement error will be discussed along with how measurement error may color your perception of process performance. Examples of the three most common types of MSA ’ s will be shared along with some lessons learned. Come find out why you should assess your measurement systems the next time you embark on a process improvement journey.

63 63 Sound interesting? Come join us! We look forward to seeing you! Reserve your place today by clicking here or by emailing programs@apicsnashville.org. DATE: December 11, 2012 PLACE: Holiday Inn Select Opryland Airport/Briley Parkway 2200 Elm Hill Pike Nashville, TN 37214 (615) 883-9770 Click http://www.hinashville.com/directions.html for directions TIME: 5:30 to 8:00 PM COST: $20.00 for Dinner - Free if an unemployed member - bring your resume You do not need to be an APICS member to attend Remember! Attend Four (4) APICS Nashville Professional Development Meetings from September 2012 thru April 2013 (excluding Tours) and attend the 5th PDM free! New members to our chapter attend their first meeting at no charge when they bring their APICS welcome letter! Reserve your place today by clicking here or by emailing programs@apicsnashville.org. Stay Informed! Click to Join our LinkedIn group APICS Middle Tennessee Chapter Nashville

64 64 Speaker Bio: Barry Kulback’s 33 year professional career has been entirely with Trane/American Standard now Ingersoll Rand. He is currently the Global Lean Six Sigma leader for the Climate Solutions Sector of Ingersoll Rand and very engaged in IR’s Lean Transformation. He gained his BS in 1979 at Austin Peay State University majoring in Physics and minoring in Computer Science. After spending 20 years in Information Technology Barry joined Operations as a Six Sigma Black Belt and progressed to a certified Six Sigma Master Black Belt in 2004. He was named Tennessee Academy of Science’s Industrial Scientist of the year in 2006 and is the holder of 4 patents related to algorithms for delivering to customer request by optimizing the match of demand to supply. He maintains his membership in the American Society for Quality as a Senior Member. Reserve your place today by clicking here or by emailing programs@apicsnashville.org. Stay Informed! Click to Join our LinkedIn group APICS Middle Tennessee Chapter Nashville Thank you for your support of APICS.

65 65 Bring 1 copper tube 1 measuring tape PC Remote mouse Backup – on data stick, on disk Pad, pen to take notes re parking lot 45-50 minutes, leaving 10-15 minutes at the end for question/answer

66 66 Title Topic Sub-topic Sub-sub-topic Sub-topic Sub-sub-topic

67 67 SERVICES / CONTROLS TRANSPORT REFRIGERATION Broad and Global Portfolio of HVAC Systems and Services Vision To make building owner and transport customers more profitable and efficient for life through innovative HVACR systems and services Vision To make building owner and transport customers more profitable and efficient for life through innovative HVACR systems and services


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