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| موضوع: كتاب Six Sigma - A Case Study Approach Using Minitab الأحد 24 مارس 2024, 1:47 am | |
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أخواني في الله أحضرت لكم كتاب Six Sigma - A Case Study Approach Using Minitab Timothy D. Blackburn
و المحتوى كما يلي :
Contentsx 4.3.3 Using Minitab to Calculate Ppk for Continuous Data: Non-normal 69 4.3.4 Discrete Data Process Capability 71 4.3.5 Calculating Ppk for Discrete Data in Minitab 73 4.4 Measurement System Analysis (with Gage R&R, Attribute Agreement Analysis): Minitab Methods and Analysis Detail 75 4.4.1 Introduction to Gage R&R and Attribute Agreement Analysis . 75 4.4.2 GAGE R&R: Overview . 76 4.4.3 Designing and Analyzing the GAGE R&R Study in Minitab 77 4.4.4 Attribute Agreement Analysis in Minitab . 86 4.5 Pareto Analysis: Minitab Methods and Analysis Detail 95 4.5.1 Creating a Pareto Chart . 95 4.5.2 Constructing a Pareto Chart in Minitab . 97 4.6 Test of Proportions: Minitab Methods and Analysis Detail 98 4.6.1 Introduction to Test of Proportions 98 4.6.2 Test of Two Proportions in Minitab 99 4.6.3 Chi-Square Test of Multiple Proportions in Minitab . 101 References 105 5 The Analyze Phase with Minitab Tools 107 5.1 The Analyze Phase: An Overview . 107 5.1.1 Introduction 107 5.1.2 Cause and Effect Analysis . 109 5.1.3 Verifying or Discarding Root Causes: Process Analysis 113 5.1.4 Verifying or Discarding Root Causes: Data Analysis 115 5.1.5 Piloting 116 5.1.6 Data Analysis Example: Regression . 116 5.1.7 Data Analysis Example: Two Sample T Test . 119 5.1.8 Data Analysis Example: Paired T Test 120 5.1.9 Data Analysis Example: ANOVA, ANOM 121 5.1.10 Data Analysis Example: Design of Experiment (DOE) . 123 5.1.11 Summary Root Cause Tables . 125 5.2 Regression Analysis: Minitab Methods and Analysis Detail . 127 5.2.1 Regression Overview . 127 5.2.2 Assumptions for Linear Regression and Key Data Interpretations 128 5.2.3 Single Linear Regression 129 5.2.4 Multiple Linear Regression 134 5.2.5 Regression Issues 140 5.2.6 Correlation and Visualization in Minitab 141 5.2.7 Other Regression Tools (Introduction) 143 Contentsxi 5.3 Two Sample T Test, Mann-Whitney: Minitab Methods and Analysis Detail 143 5.3.1 Two Sample T Test Overview 143 5.3.2 Two Sample T Test in Minitab 145 5.3.3 Mann-Whitney Test in Minitab . 149 5.3.4 Data Transformations in Minitab 154 5.3.5 Sample Size Determination in Minitab . 158 5.4 Paired T Test: Minitab Methods and Analysis Detail 160 5.4.1 Paired T Test Overview . 160 5.4.2 Paired T Test in Minitab . 161 5.4.3 One Sample Wilcoxon in Minitab (Nonparametric Alternative to a Paired T Test) 165 5.5 ANOVA, ANOM: Minitab Methods and Analysis Detail . 167 5.5.1 ANOVA and ANOM Overview . 167 5.5.2 ANOVA in Minitab 168 5.5.3 ANOM in Minitab . 173 5.5.4 Kruskal-Wallis in Minitab (Nonparametric Alternative to ANOVA) 176 5.6 Design of Experiment: Minitab Analysis and Methods Detail . 180 5.6.1 DOE Overview 180 5.6.2 Full Factorial DOE Example in Minitab (No Interactions Between X’s) . 181 5.6.3 Full Factorial DOE Example in Minitab (with Interactions). 193 5.6.4 Introduction to Screening Designs and Reduced Factorials . 196 5.6.5 Other DOE Concepts and Methods 200 References 201 6 The Improve Phase 203 6.1 Introduction 203 6.2 Implementation Plans . 204 6.3 Arriving at Solutions . 206 6.4 Cost-Benefit Analysis . 208 6.5 Risk Analysis . 210 6.6 Piloting 213 References 213 7 The Control Phase . 215 7.1 Introduction 215 7.2 Confirming Objectives Were Achieved . 216 7.3 Monitoring and Control Strategy 220 7.4 Standardization . 222 7.5 Project Closure and Hand-off 223 References 224 Contentsxii 8 Storyboards 225 8.1 Define Phase Storyboard Recommended Contents 225 8.2 Measure Phase Storyboard Recommended Contents 226 8.3 Analyze Phase Storyboard Recommended Contents . 226 8.4 Improve Phase Storyboard Recommended Contents . 226 8.5 Control Phase Storyboard Recommended Contents . 226 Appendixes . 227 Epilogue . 251 References . 253 Index 255 ANOVA Analysis of Variance—hypothesis test for three or more sample means CI Confidence Interval Cpk Process capability (short term or within batch) CTQ Critical to Quality DMAIC Define, Measure, Analyze, Improve, and Control DPMO Defects per Million Opportunities FMEA Failure Mode and Effect Analysis HVAC Heating, Ventilation, and Air Conditioning Hurdle Rate Desired rate of return IPO Initial Public Offering IRR Internal Rate of Return LCL Lower Control Limit LSL Lower Specification Limit MR Moving Range MSA Measurement System Analysis NPV Net Present Value OpEx Operational Excellence—a common name for a function responsible for continuous improvement and Six Sigma Ppk Process performance, or long-term process capability R&R Repeatability and Reproducibility SIPOC Supplier, Inputs, Process, Outputs, and Customer SME Subject Matter Expert TPS Toyota Production System UCL Upper Control Limit USL Upper Specification Limit VIF Variance Inflation Factor VOC Voice of the Customer Index A Abraizer machine, 139 Airbag, 126 Airbag seal failure, 23 Airbag seal images, 114 Airbag solutions, 206 Alternative (HA) states, 98 Analysis of means (ANOM), 121–123, 167 in Minitab, 173–175 Analysis of variance (ANOVA), 121–123, 167 dataset, 228–229 in Minitab, 168–173 Analyze phase, 107, 108 ANOM, 121–123 ANOVA, 121–123 cause and effect analysis, 109–113 design of experiment, 123–125 paired T test, 120, 121 piloting, 116, 117 regression, 116–119 root cause tables, 125, 126 two sample T test, 119, 120 verifying/discarding root causes, 113–116 Attribute agreement analysis kappa value, 87 Minitab path, attribute study worksheet, 88 Minitab path to analyze, 89 sample selection, 88 subjective classifications, 86 Attribute agreement dataset, 229–238 B Balance sheet, 5 Baseline, 14, 15, 18, 22 Binary logistic regression (BLR), 143 Box-Cox transformation, 155 Brake and airbag assembly process, 19 Brake caliper, 126 Brake caliper failure, 23 Brake caliper test, 76 Business case, 14 C Caliper mold diameter, 117, 118 Capability analysis binomial and Poisson process capability tools, 75 capable process, 64 discrete data process capability, 71 airbag warranty claims, 72 defect rate example table, 73 defect rate Z value, 72 just capable process, 64 Minitab path for, 66 Ppk calculation for continuous data, 67–69 for continuous data, non-normal, 69 for discrete data in Minitab, 73–75 process not capable, 65 upper CI for Ppk, 67 Causal Tree, 108, 112 Chi-square, 43, 44, 101–103, 105 Chi-square of multiple proportions, 98 Chi-square test of multiple proportions dataset, 238 Common cause variation, 30, 46 Communication plan, 21 Control charts, 30, 33 common cause versus special cause variation, 46 example, 47 I-MR in Minitab, 48, 50 LCL, 46 P-chart for proportional data in Minitab, 49, 50 Run Chart, 60 special cause tests, 47 U-chart, 55, 56 UCL, 46 X-Bar R chart, 53 Control limits, 30, 33, 34, 46, 48, 50, 52, 55, 60, 63, 82 Control phase, 215 confirming objectives, 216–218 KIND Karz summary of objectives, 219 monitoring and control strategy, 220, 221 prior Ppk for KIND Karz, 219 project closure and hand-off, 223, 224 standardization, 222, 223 two sample T test outputs, 219 Cost-benefit analysis, 208–210 Critical to Quality (CTQ) hierarchy, 17 D Data analysis, 115, 116 ANOM, 121–123 ANOVA, 121–123 design of experiment, 123–125 paired T test, 120, 121 regression, 116 two sample T test, 119, 120 Data collection, 26, 28–30 Data collection plan, 28 Datasets, 228–238 chi-square test of multiple proportions, 238 DOE, 243–244 Gage R&R, 244–246 I-MR, 238–239 multiple regression, 248–249 paired T test, 246–247 Pareto summarized data, 247 P-chart, 239–240 process capability, 247–248 run chart, 242–243 single regression, 248 test of two proportions, 249 two sample T test, 249–250 U-chart, 241–242 Xbar-R, 240–241 Data transformations in Minitab, 154–158 Defects, 8 types and counts, 3 Defects per million opportunities (DPMO), 8, 71 Defects per opportunity (DPO), 73 Dependent variable, 127 Design of experiment (DOE), 29, 123–125, 180 concepts and methods, 200, 201 dataset, 243–244 full factorial DOE, 181–195 screening designs and reduced factorials, 196–199 DMAIC phase, 9, 10, 12, 42, 44, 98, 225 F Failure mode and effect analysis (FMEA), 210, 211 Full factorial DOE, 181–195 G Gage R&R, 37, 39–41 analyzing, 78, 83 continuous data, 76 crossed vs. nested designs, 77 dataset, 244–246 designing, 77 Goals, 14, 15 I Implementation plan, 204–206 Improve phase, 203, 204 arriving at solutions, 206, 207 cost-benefit analysis, 208–210 implementation plan, 204–206 piloting, 213 risk analysis, 210, 211, 213 root causes and improvements, 204 Independent variables, 127 Individual moving range control chart (I-MR), 48 dataset, 238–239 Initial Public Offering (IPO), 7 Internal rate of return (IRR) method, 208, 209 J Johnson’s transformation, 158 K Kappa value, 87 KIND Karz, case study, 1, 3 Kruskal-Wallis in Minitab, 176–179 L Leadership team, 20 Lean six sigma, 8, 9 Learnings from other events, 22 Index257 Linear regression assumptions and key data interpretations, 128, 129 Lower control limit (LCL), 34, 46, 48, 63 M Mann-Whitney test in Minitab, 149–154 Mapping, 18 Measurement system analysis (MSA), 36 attribute agreement graph, 41 attribute agreement Minitab output, 40 Gage R&R Minitab output, 40 Gage R&R report graphs, 39 KIND Karz operational definitions, 38 sources of variation, 37 Measure phase capability analysis (see Capability analysis) contents, 26 data collection, 26–28, 30 funneling and stratification airbag vendor defect rates, 43 Chi-sq table for brake defect counts by vehicle, 44 KIND Karz defect categories, 42 Minitab Chi-sq results, 44 Pareto chart, 42 test of two proportions Minitab output, 43 measurement system analysis attribute agreement graph, 41 attribute agreement Minitab output, 40 Gage R&R Minitab output, 40 Gage R&R report graphs, 37, 39 KIND Karz operational definitions, 38 operational definitions, 37 sources of variation, 37 Pareto chart (see Pareto chart) process capability, 33–36 process stability, 30, 31, 33 Minitab ANOM in, 173–175 ANOVA in, 168–173 correlation and visualization in, 141, 142 data transformations in, 154–158 DOE in, 181–195 Kruskal-Wallis in, 176–179 Mann-Whitney test in, 149–154 paired T test in, 161–164 one sample Wilcoxon, 165–167 sample size determination in, 158–160 20 layout, 228 Multiple linear regression, 134–139 Multiple regression datasets, 248–249 N Nominal logistic regression, 143 Nonlinear regression, 143 Null hypothesis (Ho) states, 98 O One sample Wilcoxon, 165–167 Ordinal logistic regression (OLR), 143 P Paired T test, 120, 121, 160, 161 dataset, 246–247 in Minitab, 161–164 one sample Wilcoxon, 165–167 Pareto chart, 42 Minitab path, 97 no apparent Pareto effect, 96 with clear Pareto effect, 96 Pareto summarized data, 247 Partial least squares (PLS), 143 P-chart, 49, 51 dataset, 239–240 Piloting, 116, 117 improve phase, 213 Power and sample size tools, 29 Ppk airbags, 35 brake caliper torsion rods, 35 for warrantees and recalls, 35 Priority matrix, 28 Process analysis, 113–115 Process capability, 33, 34, 36 datasets, 247–248 Process sigma, 73 Process stability, 30, 31, 33 Project charter, 13, 15, 16 Project plan, 16 Q Questions to ask, 8 R Regression, 116–119 Regression analysis, 127, 128 linear regression, assumptions and key data interpretations, 128, 129 Index258 Minitab, correlation and visualization in, 141, 142 multiple linear regression, 134–139 regression issues, 140, 141 single linear regression, 129–134 tools, 143 Regression issues, 140, 141 Residual, 127 Responsibilities, 16 Risk analysis improve phase, 210, 211, 213 Roles, 14, 16 Root cause tables, 125, 126 Run chart, 60 dataset, 242–243 S Sample size determination in Minitab, 158–160 Schedule, 14, 15 Scope, 14, 15, 17, 18, 22 Single linear regression, 129–134 Single regression datasets, 248 SIPOC, 18, 19 Sixpack, 66, 67, 69 Six Sigma, 3, 8, 9, 12, 37 Special cause, 30, 31, 45, 46, 48, 52, 61, 66 Specification limits, 34, 63 Stakeholder analysis, 19–21 Statement of operations, 5 Storyboards, 225 analysis phase storyboard recommended contents, 226 control phase storyboard recommended contents, 226 defining phase storyboard recommended contents, 225 improve phase storyboard recommended contents, 226 measuring phase storyboard recommended contents, 226 phase storyboard recommended contents, 225 Subject matter experts (SMEs), 37, 113 Sum of the least squares, 127 Supplier, Inputs, Process, Outputs, and Customer, see SIPOC Swim lane diagrams, 18 T Team members, 14–16 Test of proportions chi-square test, 101–103 test of two proportions in Minitab, 99 Test of two proportions, 43, 99 datasets, 249 Toyota Production System (TPS), 2, 3 Two sample T test, 119, 120, 143–149 datasets, 249–250 U U-chart, 55 dataset, 241–242 Upper control limit (UCL), 34, 46, 48, 63 V Voice of the Customer (VOC), 17 W Warrantees claims and recalls, 4 X X-Bar R Chart, 53 Xbar-R dataset, 240–2411 #Minitab,#Minitab,#مينى,#تاب,#مينى_تاب,#ميني,#تاب,#ميني_تاب,#,#مينيتاب ,,,
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