Six Sigma with R

1. Six Sigma With R (Springer, 2012) November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e Six Sigma with RFrontmatter Statistical Engineering for ProcessMainmatter ImprovementI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI Control Emilio L. Cano, Javier M. MoguerzaVII Further and BeyondBackmatter and Andr´s Redchuk e November 20, 2012 Facultad de Estudios Estad´ ısticos Universidad Complutense de Madrid Book Presentation UCM 1/59

2. Six Sigma With R (Springer, 2012) Contenido November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e 1 FrontmatterFrontmatterMainmatter 2 MainmatterI BasicsII Deﬁne I BasicsIII MeasureIV AnalyzeV Improve II DeﬁneVI ControlVII Further and Beyond III MeasureBackmatter IV Analyze V Improve VI Control VII Further and Beyond 3 Backmatter Book Presentation UCM 2/59

3. Six Sigma With R (Springer, 2012) Contents November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e 1 FrontmatterFrontmatterMainmatter 2 MainmatterI BasicsII Deﬁne I BasicsIII MeasureIV AnalyzeV Improve II DeﬁneVI ControlVII Further and Beyond III MeasureBackmatter IV Analyze V Improve VI Control VII Further and Beyond 3 Backmatter Book Presentation UCM 3/59

4. Six Sigma With R (Springer, 2012) Publisher November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter http://www.springer.com/statistics/book/978-1-4614-3651-5 Book Presentation UCM 4/59

5. Six Sigma With R (Springer, 2012) Book website November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter http://www.sixsigmawithr.com/ Book Presentation UCM 5/59

6. Six Sigma With R (Springer, 2012) R Package November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter http://cran.r-project.org/web/packages/SixSigma/index.html Book Presentation UCM 6/59

7. Six Sigma With R (Springer, 2012) Frontmatter November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e ForewordFrontmatter PrefaceMainmatter Why Six Sigma with RI BasicsII Deﬁne Who is this book forIII Measure ConventionsIV AnalyzeV Improve ProductionVI ControlVII Further and Beyond ResourcesBackmatter About the Authors Acknowledgements Contents List of Tables and Figures Acronyms http://link.springer.com/book/10.1007/978-1-4614-3652-2//page/1 Book Presentation UCM 7/59

9. Six Sigma With R (Springer, 2012) 1. Six Sigma in a Nutshell November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter Herbert SpencerMainmatterI Basics “Science is organised knowledge”II DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 9/59

10. Six Sigma With R (Springer, 2012) The DMAIC Cycle November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 10/59

11. Six Sigma With R (Springer, 2012) Six Sigma Roles November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter In Six Sigma, everyone in the organization hasMainmatterI Basics a role in the project. Six Sigma methodologyII DeﬁneIII MeasureIV Analyze uses an intuitive categorization of these roles.V ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 11/59

12. Six Sigma With R (Springer, 2012) Six Sigma Roles November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter In Six Sigma, everyone in the organization hasMainmatterI Basics a role in the project. Six Sigma methodologyII DeﬁneIII MeasureIV Analyze uses an intuitive categorization of these roles.V ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 11/59

13. Six Sigma With R (Springer, 2012) 2. R from the Beginning November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter Linus TorvaldsMainmatterI Basics “Software is like sex; it’s better when it’s free”II DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 12/59

14. Six Sigma With R (Springer, 2012) The R Project November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter http://www.r-project.org Book Presentation UCM 13/59

15. Six Sigma With R (Springer, 2012) The R Environment November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 14/59

16. Six Sigma With R (Springer, 2012) 3. Process Mapping with R November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter Charles Franklin KetteringMainmatterI Basics “A problem well stated is a problem halfII DeﬁneIII Measure solved”IV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 15/59

17. Six Sigma With R (Springer, 2012) A Process Map November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e Six Sigma Process MapFrontmatter operators INPUTS toolsMainmatter X raw materialI Basics facilitiesII Deﬁne INSPECTION ASSEMBLY TEST LABELINGIII Measure sheets sheets helicopter helicopterIV Analyze ... INPUTS INPUTS INPUTS INPUTSV ImproveVI ControlVII Further and Beyond Param.(x): width NC Param.(x): operator C Param.(x): operator C Param.(x): operator CBackmatter operator C cut P throw P label P Measure pattern P fix P discard P Featur.(y): label discard P rotor.width C environment N Featur.(y): ok rotor.length C Featur.(y): time paperclip C tape C Featur.(y): weight LEGEND helicopter (C)ontrollable OUTPUTS (Cr)itical (N)oise Y (P)rocedure Paper Helicopter Project Book Presentation UCM 16/59

18. Six Sigma With R (Springer, 2012) 4. Loss Funtion Analysis with R November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter W. Edwards DemingMainmatterI BasicsII Deﬁne Defects are not free. Somebody makes them,III MeasureIV Analyze and gets paid for making themV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 17/59

19. Six Sigma With R (Springer, 2012) A Loss Function Example November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e > ss . lfa ( ss . data . bolts , " diameter " , 0.5 , 10 , 0.001 , lfa . sub = " 10 mm . Bolts Project " ,Frontmatter lfa . size = 100000 , lfa . output = " both " )MainmatterI BasicsII Deﬁne $ lfa . kIII Measure [1] 0.002IV AnalyzeV ImproveVI Control $ lfa . lfVII Further and Beyond expression ( bold ( L == 0.002 %. % ( Y - 10) ^2) )Backmatter $ lfa . MSD [1] 0.03372065 $ lfa . avLoss [1] 6.74413 e -05 $ lfa . Loss [1] 6.74413 Book Presentation UCM 18/59

20. Six Sigma With R (Springer, 2012) A Loss Function Example (cont.) November, 2012 Emilio L. CanoJavier M. Moguerza Loss Function Analysis Andr´s Redchuk eFrontmatter 5e−04 T DataMainmatterI Basics 4e−04 CTQ: diameterII Deﬁne Y0 = 10III Measure Cost of Poor Quality ∆ = 0.5IV Analyze L0 = 0.001 3e−04V Improve Size = 1e+05VI ControlVII Further and Beyond 2e−04Backmatter Mean = 10.0308 k = 0.002 1e−04 MSD = 0.0337 LSL USL Av.Loss = 1e−04 Loss = 6.7441 0e+00 9.6 9.8 10.0 10.2 10.4 Observed Value L = 0.002 ⋅ (Y − 10) 2 10 mm. Bolts Project Book Presentation UCM 19/59

21. Six Sigma With R (Springer, 2012) 5. Measurement System Analysis November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter Lord KelvinMainmatter “If you cannot measure it,I BasicsII Deﬁne you cannot improve it.”III MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 20/59

22. Six Sigma With R (Springer, 2012) Repeatability & Reproducibility November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI Basics Repetible and Reproducible Repetible but non Reproducible Reproducible but non Repetible Non Repetible & Non ReproducibleII Deﬁne q qIII Measure qq q q q q q q qIV Analyze q qV Improve q qq q qqVI ControlVII Further and Beyond q q q q qBackmatter Book Presentation UCM 21/59

23. Six Sigma With R (Springer, 2012) MSA with R November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e Six Sigma Gage R&R Study Components of Variation Var by Part 1.8Frontmatter 80 q q q q q 1.6 60 q q PercentMainmatter 1.4 var 40 q q q q q q 1.2I Basics 20 q q q q q 1.0 q 0II Deﬁne G.R&R Repeat Reprod Part2Part q prot #1 prot #2 prot #3III Measure %Contribution %Study VarIV Analyze R Chart by appraiser Var by appraiser prot #1 prot #2 prot #3V Improve 1.8 q q q op #1 op #2 op #3 0.5 q q q q qVI Control 1.6 0.4 q q qVII Further and Beyond q 1.4 var 0.3 q var q q q q q q q q 0.2 1.2 q q q q qBackmatter 0.1 q q q 1.0 q q q q prot #1 prot #2 prot #3 prot #1 prot #2 prot #3 op #1 op #2 op #3 part x Chart by appraiser Part*appraiser Interaction prot #1 prot #2 prot #3 1.7 q op #1 op #2 op #3 1.7 q 1.6 q 1.6 q q 1.5 var 1.5 1.4 var 1.4 q 1.3 q q 1.3 q 1.2 1.2 q q q 1.1 q q 1.1 q q prot #1 prot #2 prot #3 prot #1 prot #2 prot #3 prot #1 prot #2 prot #3 op #1 op #3 part op #2 Helicopter Project Book Presentation UCM 22/59

24. Six Sigma With R (Springer, 2012) 6. Pareto Analysis with R November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter OvidioMainmatterI Basics Causa latet: vis est notissima. [The cause isII DeﬁneIII Measure hidden, but the result is known.]IV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 23/59

25. Six Sigma With R (Springer, 2012) Pareto Principle (80/20 rule) November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII Deﬁne ExamplesIII MeasureIV AnalyzeV Improve 20 % of customers make 80 % of incomesVI ControlVII Further and Beyond 20 % of students get 80 % of good marksBackmatter 80 % of cost of quality is due to 20 % of the possible causes Book Presentation UCM 24/59

26. Six Sigma With R (Springer, 2012) Cause-and-eﬀect diagram November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e Six Sigma Cause−and−effect DiagramFrontmatterMainmatterI Basics Operator Environment Tools operator #1 height scissorsII Deﬁne operator #2 cleaning tapeIII Measure operator #3IV AnalyzeV ImproveVI ControlVII Further and Beyond Flight TimeBackmatter paperclip model marks rotor.width2 calibrate thickness rotor.length Measure.Tool Raw.Material Design Paper Helicopter Project Book Presentation UCM 25/59

27. Six Sigma With R (Springer, 2012) Pareto Chart November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e Pareto Chart for b.vector qFrontmatter q q q qMainmatter q 80% q 60I Basics Cumulative Percentage qII Deﬁne qIII MeasureIV Analyze Frequency 40V Improve qVI ControlVII Further and BeyondBackmatter 20 q 0 Delays Materials Customer Training Rework Errors Rain Wind Permissions Inadequate Temperature Book Presentation UCM 26/59

28. Six Sigma With R (Springer, 2012) 7. Process Capability Analysis November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter Johann Wolfgang von GoetheMainmatter One cannot develop taste from what is ofI BasicsII DeﬁneIII Measure average quality but only from the very best.IV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 27/59

29. Six Sigma With R (Springer, 2012) Capability Analysis Output November, 2012 Emilio L. CanoJavier M. Moguerza Six Sigma Capability Analysis Study Andr´s Redchuk e Histogram & Density Density Lines LegendFrontmatter Target Density ST Theoretical Dens. STMainmatter Density LT Theoretical Density LTI BasicsII DeﬁneIII Measure LSL USL SpecificationsIV Analyze LSL: 740 Target: 750V Improve USL: 760VI ControlVII Further and BeyondBackmatter Short Term Process Long Term 740 745 750 755 760 Mean: 749.7625 Mean: 753.7239 Check Normality SD: 2.1042 SD: 2.6958 n: 20 n: 40 Shapiro−Wilk Test Zs: 3.14 Zs: 2.33 q p−value: 0.07506 DPMO: 9952.5 q Short Term Indices Long Term qq Lilliefors (K−S) Test Cp: 1.5841 Pp: 1.2365 qq qqq p−value: 0.2291 CI: [1.4,1.7] CI: [1.1,1.3] qq qqq qq qq Cpk: 1.5465 Ppk: 0.7760 q q CI: [1.4,1.7] CI: [0.7,0.8] Normality accepted when p−value > 0.05 Winery Project Book Presentation UCM 28/59

30. Six Sigma With R (Springer, 2012) 8. Charts with R November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e John TukeyFrontmatterMainmatter “The greatest value of a picture is when itI BasicsII Deﬁne forces us to notice what we never expected toIII MeasureIV Analyze see.”V ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 29/59

31. Six Sigma With R (Springer, 2012) Multi-vari chart November, 2012 Emilio L. Cano Multi−vari chart for Volume by color and operatorJavier M. Moguerza Andr´s Redchuk e batch 1 q 2 q 3 q 4 q 1 2 3Frontmatter 3 3Mainmatter B CI Basics 18II Deﬁne 17III Measure 16 q q q q q q q q qIV Analyze 15 q q q q q q q q q q q q q qV Improve 14VI Control 2 2VII Further and Beyond B C q q 18 qBackmatter Volume q q 17 q q q q q q q 16 q q q q q q q q 15 q q q 14 1 1 B C q 18 q q q 17 q q q q q q 16 q q q q q q q 15 q q q q 14 q 1 2 3 Filler Book Presentation UCM 30/59

32. Six Sigma With R (Springer, 2012) 9. Statistics and Probability with R November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatter Aaron LevensteinI BasicsII Deﬁne “Statistics are like bikinis. What they reveal isIII MeasureIV AnalyzeV Improve suggestive, but what they conceal is vital.”VI ControlVII Further and BeyondBackmatter Book Presentation UCM 31/59

33. Six Sigma With R (Springer, 2012) Distributions November, 2012 Emilio L. Cano Hypergeometric Geometric Negative Binomial PoisonJavier M. Moguerza Probability Probability Probability Probability 0.06 0.15 0.00 0.10 Andr´s Redchuk e 0.3 0.00 0.00 0.0Frontmatter 0 1 2 3 4 0 10 30 10 20 30 40 0 5 10 20 Elements in class A Items extracted until first success Number of trials until 3 events Number of successful experiments per unitMainmatterI Basics Exponential Lognormal Uniform Gamma Probability Density Probability Density Probability Density Probability DensityII Deﬁne 0.0 0.4 0.8 0.0 0.6 1.2 0.0 0.2 0.4III Measure 0.6IV Analyze 0.0V Improve 0 1 2 3 4 5 0 2 4 6 −0.5 0.5 1.5 0 2 4 6 8VI Control Random Variable X Random Variable X>0 Random Variable X Random Variable XVII Further and BeyondBackmatter Beta Weibul t−Student Chi−squared Probability Density Probability Density Probability Density Probability Density 0.0 1.0 2.0 0.0 0.3 0.6 0.06 0.3 95% 5% 95% 5% 0.00 0.0 1.73 30.14 0.0 0.4 0.8 0 2 4 6 −4 0 2 4 10 30 50 Random Variable X Random Variable X Random Variable X Random Variable X F Probability Density 0.6 95% 5% 0.0 2.34 0 1 2 3 4 Random Variable X Book Presentation UCM 32/59

34. Six Sigma With R (Springer, 2012) 10. Statistical Inference with R November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e George E.P. BoxFrontmatterMainmatter “All models are wrong; some models areI BasicsII Deﬁne useful.”III MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 33/59

35. Six Sigma With R (Springer, 2012) Conﬁdence Interval Example November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e Confidence Interval for the MeanFrontmatter Mean: 950.016 95% CI: [949.967, 950.064]Mainmatter StdDev: 0.267 P−Var: unknownI Basics n: 120 t: 1.98II Deﬁne Missing: 0III MeasureIV AnalyzeV ImproveVI Control Histogram & Density Plot Shapiro−Wilks 1.5VII Further and Beyond Normality TestBackmatter 0.985 p−value: 0.19 1.0 density Normal q−q Plot 0.5 q qq q qq qq qq q qq q qq qq qq qq qq qq qq qq qq qq qq qq qq qq q q qq qq qq qq qq qq qq q 0.0 qq qq qqqq qq qq qq q qq qq qq qq q qq qq qq 949.0 949.5 950.0 950.5 q q q q Value of len q qq Book Presentation UCM 34/59

36. Six Sigma With R (Springer, 2012) 11. Design of Experiments with R November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter R.A. FisherMainmatter “Sometimes the only thing you canI BasicsII Deﬁne do with a poorly designedIII MeasureIV AnalyzeV Improve experiment is to try to ﬁnd out whatVI ControlVII Further and Beyond it died of”Backmatter Book Presentation UCM 35/59

37. Six Sigma With R (Springer, 2012) The Importance of Experimenting November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter “An engineer who does not knowMainmatter experimental design is not anI BasicsII DeﬁneIII Measure engineer”IV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Comment made by to one of the authors [of “Statistics for experimenters” by an ] executive of the Toyota Motor Company. Book Presentation UCM 36/59

38. Six Sigma With R (Springer, 2012) 12.Process Control with R November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatter Walter A. ShewhartI BasicsII Deﬁne “Special causes of variation may be found andIII MeasureIV Analyze eliminated.”V ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 37/59

39. Six Sigma With R (Springer, 2012) Control Chart Plotting November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e p Chart for stockouts 0.25Frontmatter q q UCLMainmatter q q 0.20I Basics Group summary statisticsII Deﬁne qIII Measure 0.15 q q q qIV Analyze qV Improve q qVI Control 0.10 q q CL qVII Further and Beyond q qBackmatter q q 0.05 q q q q LCL 0.00 1 3 5 7 9 11 13 15 17 19 21 Group Number of groups = 22 Center = 0.1212294 LCL is variable Number beyond limits = 1 StdDev = 0.3263936 UCL is variable Number violating runs = 0 Book Presentation UCM 38/59

40. Six Sigma With R (Springer, 2012) 13. Other Tools and November, 2012 Emilio L. CanoJavier M. Moguerza Methodologies Andr´s Redchuk eFrontmatterMainmatter Johann Wolfgang von GoetheI BasicsII Deﬁne Instruction does much, but encouragementIII MeasureIV AnalyzeV Improve everything.VI ControlVII Further and BeyondBackmatter Book Presentation UCM 39/59

41. Six Sigma With R (Springer, 2012) Other topics November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI Basics Failure Mode, Eﬀects, and CriticalityII DeﬁneIII Measure AnalysisIV AnalyzeV ImproveVI Control Design for Six SigmaVII Further and BeyondBackmatter Lean Gantt Chart Some Advanced R Topics Book Presentation UCM 40/59

43. Six Sigma With R (Springer, 2012) Case Study November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 42/59

44. Six Sigma With R (Springer, 2012) Helicopter Template November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter > ss . heli () null device 1 > # vignette (" H e l i c o p t e r I n s t r u c t i o n s ") to get instructions Book Presentation UCM 43/59

45. Six Sigma with R | Paper Helicopter template max (9.5cm) std (8cm) min (6.5cm) ← wings length → cut ? pe fold ↑ fold ↓ tacut cut cut ← body length → tape? tape? min (6.5cm) std fold ↓ ↓ fold ↑ ↑ (8cm) clip? max max min ← body width → min max (9.5cm) (6cm) (4cm) (4cm) (6cm)

46. Six Sigma With R (Springer, 2012) Enjoy the Case Study! November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 45/59

47. Six Sigma With R (Springer, 2012) Enjoy the Case Study! November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 45/59

48. Six Sigma With R (Springer, 2012) So what? November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 46/59

49. Six Sigma With R (Springer, 2012) So what? November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e The Scientiﬁc MethodFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 46/59

50. Six Sigma With R (Springer, 2012) The Scientiﬁc Method and Six November, 2012 Emilio L. CanoJavier M. Moguerza Sigma Andr´s Redchuk eFrontmatterMainmatter DMAIC Cycle Scientific MethodI BasicsII Deﬁne Ask a questionIII Measure DefineIV AnalyzeV Improve Do some backgroundVI Control Measure researchVII Further and BeyondBackmatter Construct a hypothesis Analyze Test the hypothesis with an experiment Improve Analyze the data and draw conclusions Control Communicate results Book Presentation UCM 47/59

51. Six Sigma With R (Springer, 2012) The Key to Success November, 2012 Emilio L. CanoJavier M. Moguerza “Six Sigma speaks the language of business” Andr´s Redchuk eFrontmatter ISO 13053-1:2011MainmatterI BasicsII DefineIII MeasureIV Analyze Six Sigma methodology is a quality paradigmV ImproveVI Control that translates the involved scientificVII Further and BeyondBackmatter methodology into a simple way to apply the scientific method within every organization. Book Presentation UCM 48/59

52. Six Sigma With R (Springer, 2012) Opportunities November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter For Today’s Graduate, Just One Word: StatisticsMainmatter (The New York Times, August 2009)I BasicsII Deﬁne “I keep saying that the sexy job in theIII MeasureIV Analyze next 10 years will be statisticians”V ImproveVI ControlVII Further and BeyondBackmatter Data Scientist: The Sexiest Job of the 21st Century (Harvard Business Review, October 2012) . . . the “data scientist.” It’s a high-ranking professional with the training and curiosity to make discoveries in the world of big data . . . Book Presentation UCM 49/59

53. Six Sigma With R (Springer, 2012) Opportunities (cont.) November, 2012 Emilio L. Cano Gartner Sees 4.4M Big Data Jobs by 2015Javier M. Moguerza Andr´s Redchuk e (Information Management, October 2012)Frontmatter Lack of data scientists could derail big dataMainmatterI Basics projects: IBM (CIO, October 2012)II DeﬁneIII MeasureIV Analyze Son las matem´ticas, est´pido (El Pa´ a u ıs,V ImproveVI Control Noviembre 2012)VII Further and BeyondBackmatter La econom´ del conocimiento exige ıa una educaci´n sustentada en tres o fundamentos: un nivel avanzado en matem´tica y estad´ a ıstica, una capacidad elevada para escribir un argumento y un nivel avanzado de ingl´s e Book Presentation UCM 50/59

54. Six Sigma With R (Springer, 2012) R Final Remarks November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and Beyond kdnuggets.comBackmatter (2012) link Book Presentation UCM 51/59

55. Six Sigma With R (Springer, 2012) R Final Remarks (cont.) November, 2012 Emilio L. CanoJavier M. Moguerza Community Andr´s Redchuk e 4131 packages at CRAN (18/11/2012)FrontmatterMainmatter Task viewsI BasicsII DeﬁneIII Measure ManualsIV AnalyzeV Improve PublicationsVI ControlVII Further and BeyondBackmatter http: //cran.r-project.org/web/packages/ Book Presentation UCM 52/59

56. Six Sigma With R (Springer, 2012) R Final Remarks (cont.) November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e Customization A company can develop a package that ﬁts itsFrontmatterMainmatter inner procedures and methods.I BasicsII DeﬁneIII MeasureIV AnalyzeV Improve InnovationVI ControlVII Further and Beyond A company can develop and deploy anBackmatter innovative method from its R&D department, or from the result of other published researches. Book Presentation UCM 53/59

57. Six Sigma With R (Springer, 2012) R Final Remarks (cont.) November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatter BusinessMainmatterI Basics http:II DeﬁneIII Measure //www.revolutionanalytics.com/IV AnalyzeV ImproveVI Control http://www.openanalytics.eu/VII Further and BeyondBackmatter http://www.fellstat.com/ http://www.rstudio.com/ide/ http://www.datanalytics.com/ ... Book Presentation UCM 54/59

58. Six Sigma With R (Springer, 2012) R Final Remarks (cont.) November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI Basics GUI, IDEII DeﬁneIII Measure RStudioIV AnalyzeV ImproveVI Control Eclipse + StatETVII Further and BeyondBackmatter EMACS + EES Deducer ... Book Presentation UCM 55/59

59. Six Sigma With R (Springer, 2012) R Final Remarks (cont.) November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter Book Presentation UCM 56/59

60. Six Sigma With R (Springer, 2012) November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter http://r-es.org/ Book Presentation UCM 57/59

61. Six Sigma With R (Springer, 2012) November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk eFrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter http://www.r-project.org/useR-2013 Book Presentation UCM 58/59

62. Six Sigma With R (Springer, 2012) Discussion November, 2012 Emilio L. CanoJavier M. Moguerza Andr´s Redchuk e Thanks !FrontmatterMainmatterI BasicsII DeﬁneIII MeasureIV AnalyzeV ImproveVI ControlVII Further and BeyondBackmatter emilio.lopez@urjc.es @emilopezcano http://www.sixsigmawithr.com Book Presentation UCM 59/59