Shainin: A concept for problem solving

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1 Shainin: A concept for problem solving Lecture at the Shainin conference Amelior 11 December 2009 Willy Vandenbrande 1

2 Dorian Shainin ( ) Aeronautical engineer (MIT 1936) Design Engineer for United Aircraft Corporations Mentored by his friend Joseph M. Juran Reliability consultant for Grumman Aerospace (Lunar Excursion Module) Reliability consultant for Pratt&Whitney (RL-10 rocket engine) Developed over 20 statistical engineering techniques for problem solving and reliability Started Shainin Consultants in 1984, his son Peter is current CEO. 2

3 Dorian Shainin and ASQ 15th ASQ Honorary Member (1996) First person to win all four major ASQ medals In 2004 ASQ created the Dorian Shainin Medal For outstanding use of unique or creative applications of statistical techniques in the solving of problems related to the quality of a product or service. 3

4 Dorian Shainin Not very well known outside USA (compared to Deming, Juran) 1991: Publication of first edition of World Class Quality by Keki Bothe 2000: Second edition (Keki and Adi Bothe) Books brought attention to Shainin methods, but are very biased. 4

5 Problem Solving Focus is on variation reduction LSL USL After Before LSL = Lower Specification Limit USL = Upper Specification Limit 5

6 Problem Solving But also LSL After Before 6

7 Basic Shainin assumption The pareto principle of vital few and trivial many. Only a few input variables are responsible for a large part of the output behavior. Red X TM Pink X TM Pale Pink X TM Problem solving becomes the hunt for the Red X TM 7

8 Shainin tools Recipe like methods / statistics in the background Comparing extremes allows easier detection of causes BOB Best of Best WOW Worst of Worse Non parametrics with ranking tests in stead of calculations with hypothesis tests Graphical Methods Working with small sample sizes The truth is in the parts, not in the drawing: let the parts talk! 8

9 Preliminary activities Define the critical output variable(s) to be improved (called problem Green Y ) Determine the quality of the Measurement System used to evaluate the Green Y A bad measurement system can in itself be responsible for excessive variation Improvements can only be seen if they can be measured 9

10 variables Clue generating Components Search Multi-Vari chart Paired Comparisons Product / Process Search Variables Search Full Factorials 5 20 variables 4 or less variables Formal Doe tools Validation B vs C No interactions Interactions Optimization Scatter Plots RSM methods Control Assurance Positrol Process Certification Ongoing control Precontrol Overview of Shainin tools 10 Source: World Class Quality 2nd edition

11 General comments Gradually narrowing down the search Clear logic Analyzing Improving Controlling Not all tools are Shainin tools What s in a name? Positrol versus Control Plan Process Certification versus Process Audit 11

12 Tool details Overview of methods More info on B vs C TM and Scatter Plots in workshops Some more detail on Multi-Vari chart Paired Comparison TM and Product/Process Search Pre Control 12

13 Clue Generating / Multi-Vari Chart Objective Application Understand the pattern of variation Define areas where not to look for problems Allow a more specific brainstorm Problem type: excess variation Wide applicability Principles Sample Size Divide total variation in categories Search for causes of variation in the biggest category first Samples taken in production on current process Could be a big measurement investment Comments Very useful tool and best applied before brainstorming causes on excess variation 13

14 Multi-Vari Chart Breakdown of variation in 3 families: Positional (within piece, between cavities, ) Cyclical (consecutive units, batch-to-batch, lot-tolot) Temporal (hour-to-hour, shift-to-shift, ) 14

15 Multi-vari Chart If one family of variation contains a large part of total variation, we can concentrate on investigating variables related to this family of variation. 15

16 Clue Generating / Component Search TM Objective Find the component(s) of an assembly that is (are) responsible for bad behavior Application Principles Sample Size Problem type: assembly does not perform to spec Limitation: Disassembly / Reassembly must be possible without product change Select BOB and WOW unit Exchange components and observe behavior. Components that change behavior are Red X comp 2 = 1 BOB and 1 WOW Comments Disassembly / reassembly requirement limits application. 16

17 Clue Generating / Paired Comparison TM Objective Find directions for further investigation Application Problem type: occasional problems in production flow Principles Sample Size Select pairs of BOB and WOW units Look for differences Consistent differences to be investigated further 5 to 6 pairs of 1 BOB and 1 WOW Comments Practical application of let the parts talk 17

18 Paired Comparisons TM : method Step 1: take 1 good and 1 bad unit As close as possible in time Aim for BOB and WOW units Step 2: note the differences between these units (visual, dimensional, mechanical, chemical, ). Let the parts talk! Step 3: take a second pair of good and bad units. Repeat step 2 18

19 Paired Comparisons TM : method Step 4: repeat this process with third, fourth, fith, pair until a pattern of differences becomes apparent. Step 5: don t take inconsistent differences into account. Generally after the fith or sixth pair the consistent differences that cause the variation become clear. 19

20 Clue Generating / Product/Process Search Objective Preselection of variables out of a large group of potential variables Application Problem type: Various types of problems Principles Sample Size Select sets of BOB and WOW units batches -.. Add product data / process parameters and rank Apply Tukey test to determine important parameters 8 BOB and 8 WOW units / batches Comments Tukey test is alternative for t-test Widely applicable method Problem: available data (process parameters) 20

21 Product/Process Search: example Transmission assemblies rejected for noise. Components search shows idler shaft as responsible component One of the parameters of idler shaft is out of round 8 good / 8 bad units selected and measured for out of round 21

22 Product/Process search: example Out of round good units (mm) Out of round bad units (mm)

23 Tukey test procedure Rank individual units by parameter and indicate Good / Bad. Count number of all good or all bad from one side and vice versa from other side. Make sum of both counts. Determine confidence level to evaluate significance. 23

24 Tukey test confidence levels Total end count Confidence 90% 95% 99% 99.9% 24

25 Tukey test: example Good Bad Top end count (all good) 4 Overlap region Bottom end count (all bad) 3 25

26 Tukey test: example Total end count = = 7 95 % confidence that out-of-round idler shaft is important in explaining the difference in noise levels. 26

27 Formal Doe tools / Variables Search Objective Application Principles Sample Size Determine Red X TM, Pink X TM including quantification of their effect Problem type: Various types of problems After clue generating more then 4 potential variables left List variables in order of criticality (process knowledge) and indicate good / bad level. Swap factor settings and observe behavior. Factors that change behavior (and interactions) are red X TM, Pink X TM Number of tests is determined by number of variables and quality of ordering. Comments Alternative to fractional factorials on two levels Method comparable to components search 27

28 Formal Doe tools / Full Factorials Objective Application Determine Red X TM, Pink X TM including quantification of their effect Problem type: Various types of problems After clue generating 4 or less variables left Principles Classical DOE with Full Factorials at two levels Main Effects and interactions are calculated Sample Size Number of tests is determined by number of variables k (2 k test combinations) Comments Well established method 28

29 Formal Doe tools / B(etter) vs C(urrent) TM Objective Application Validation of Red X TM, Pink X TM Problem type: Various types of problems Principles Create new process using optimum settings and compare optimum with current. Sample Size Comments 3 B and 3 C tests (each test can involve several units test of variation reduction) All 3B s must be better than all 3C s Quick validation that works well with big improvements 29

30 Optimization / Scatter Plots Objective Application Principles Fine tune best level and realistic tolerance for Red X TM, Pink X TM if no interactions are present Problem type: Variation Reduction and optimizing signal Do tests around optimum and use graphical regression to set tolerance Sample Size Comments 30 tests for each critical variable Graphical method that could easily be transformed to a statistical method 30

31 Optimization / Response Surface Methods Objective Application Principles Sample Size Comments Fine tune best level and realistic tolerance for Red X TM, Pink X TM if interactions are present Problem type: Variation Reduction and optimizing signal Evolutionary Operation (EVOP) to scan response surface in direction of steepest ascent Depends on variables and surface. Method developed by George Box 31

32 EVOP example 32

33 Control / Positrol Objective Application Principles Assuring that optimum settings are kept Problem type: all types Table of What, How, Who, Where and When control has to be exercised. Sample Size Comments Checking frequency in the When column Can be compared with a Control Plan 33

34 Control / Process Certification Objective Application Principles Eliminating peripheral causes of poor quality Problem type: all types Make overview of things that could influence the process and install inspections, audits, Sample Size Comments Checking frequency to be determined Mix of 5S, Poka-Yoke, instructions, ISO 9000, audits, 34

35 Control / Pre Control Objective Application Continuous checking of the quality of the process output Problem type: control variation and setting of the process Principles Sample Size Divide total tolerance in colored zones and use prescribed sampling and rules to control the process. Checking frequency to be determined Comments Alternative to classical SPC Traffic lights system Very practical method 35

36 Pre-Control: chart construction USL 1/4 TOL 1/4 TOL ½TOL TARGET LSL 36

37 Pre-control: use of chart 1. Start process: five consecutive units in green needed as validation of set-up. 2. If not possible: improve process. 3. In production: 2 consecutive units 4. Frequency: time interval between two stoppages (see action rules) / 6. 37

38 Pre-control: action rules 2 units in same yellow zone 2 units in different yellow zone 1 unit in red zone Result of samples 2 units in green zone 1 unit in green and 1 unit in yellow zone Action Continue Continue Correct Stop and act Stop and act After an intervention: 5 consecutive units in green zone 38

39 Pre-control: example Start Correct Start Time 39

40 QS Consult Willy Vandenbrande, Master TQM ASQ Fellow - Six Sigma Black Belt Montpellier 34 B Brugge België - Belgium Tel + 32 (0) Website Willy Vandenbrande 40

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