Process & Control Engineering vs Six Sigma Brilliant

Process & Control Engineering is a leading publication and have been serving the process industries for 45 years. Our readers are engineers, managers and senior executives who have a purchasing and/or specifying responsibility for control, instrumentation and process products, equipment, systems and services. Our readers work within these industries: Energy & Renewables, Food & Beverage, HVAC, Industrial, Mining, Oil & Gas, Petrochemical & Chemical, Pharmaceutical, Power Generation & Nuclear, Paper & Pulp, Subsea & Offshore, Water & Waste Water We have a print & digitally-enabled, multi-channel distribution network – reaching up to 38,000 monthly readers and followers.

The Lean Six Sigma Coach provides Lean Six Sigma knowledge, the methodology as well as selected tools in English and German. The App is aimed at process managers, project leaders and decision makers that focus on optimizing business processes. It enables the user to access important information quickly and conveniently and thereby organize his project work more efficiently. Six Sigma is a business management strategy, which improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes. In statistical terms, the purpose of Six Sigma is to reduce process variation so that virtually all the products or services provided meet or exceed customer expectations. Some of topics Covered in this application are: 1. SIX SIGMA INTRODUCTION 2. SIX SIGMA DEFINITION 3. THE SIX SIGMA MOVEMENT 4. ROLE OF SIX-SIGMA 5. THE DESIGN FOR SIX-SIGMA 6. HISTOGRAMS IN SIX-SIGMA 7. HISTOGRAMS IN SIX-SIGMA 8. SOME OTHER HISTOGRAMS IN SIX-SIGMA 9. STATISTICAL INTERPRETATION OF SIX-SIGMA 10. PROJECT IMPLEMENTATION 11. SIX SIGMA-SIGMA VALUES 12. PURPOSE OF SIX-SIGMA 13. HISTORY OF SIX SIGMA 14. TOYOTA MANAGEMENT IN SIX-SIGMA 15. LEAN PRODUCTION 16. HISTORY OF DOCUMENTATION 17. HISTORY OF STATISCS AND QUALITY 18. THE CULTURE OF DISCIPLINE 19. QUALITY CONTROL AND SIX SIGMA 20. QUALITY FUNCTION DEVELOPMENT 21. ORGANIZATIONAL ROLES AND METHODS 22. STANDARD OPERATING PROCEDURE 23. ADVANCED STATISTICAL QUALITY CONTROL 24. SQC CASE STUDIES PRINTED CIRCUIT BOARD 25. STATISTICAL QUALITY CONTROL THEORY 26. PROBABILITY THEORY 27. DEFECTS PER MILLION OPPORTUNITIES 28. THE CENTRAL LIMIT THEOREM 29. DISCRETE RANDOM VARIABLES 30. XBAR CHARTS AND AVERAGE RUN LENGTH 31. OC CURVES AND AVERAGE SAMPLE NUMBER 32. DMAIC 33. LEAN SIX SIGMA METRICS 34. LEAN SIX-SIGMA APPROACH 35. LEAN SIX-SIGMA HISTORY 36. LEAN MANUFACTURING 37. LEAN MANUFACTURING WASTE 38. BENEFITS OF LSS 39. TYPES OF LAYOUT OF LSS 40. GROUP TECHNOLOGY 41. TOTAL PRODUCTIVE MAINTENANCE 42. KAIZEN 43. STAGES OF KAIZEN 44. MEASUREMENT SYSTEM ANALYSIS 45. REQUIREMENT OF MSA SYSTEM 46. REPEATEBILITY AND REPRODUCIBILITY 47. ANALYSIS OF VARIANCE METHOD 48. MEASURE PHASE AND STATISTICAL CHARTING 49. STATISTICAL CHARTING 50. GAUGE R AND R 51. STATISTICAL CHART SELECTION 52. P-CHARTING 53. P-CHARTING 54. DEMERIT CHARTING AND U-CHARTING 55. X-BAR AND R-CHARTING 56. ANALYZE PHASE 57. ANALYZE PHASE 58. THE TOYOTA PRODUCTION SYSTEM 59. CAUSES AND EFFECT MATRICES 60. DOE AND REGRESSION 61. PHAESES AND STRATEGY 62. PROJECT CHARTER 63. SNAB TAB PROJECT CHARTER 64. PHASES DIFINING METHODS 65. FORMAL MEETINGS 66. SIGNIFICANT FIGURES 67. THE LAW OF UNCOSCIOUS STATISTICIAN 68. PROCESS CAPABILITY INDICES 69. DESIGN PHASE 70. QUALITY FUNCTION DEVELOPMENT 71. CONTROL OR VERIFY PHASE 72. CONTROL PLANNING 73. ACCEPTANCE SAMPLING 74. DESIGN OF EXPERIMENTS (DOE) AND REGRESSION 75. DOE: SCREENING USING FRACTIONAL FACTORIALS 76. ORIGINS OF THE ARRAYS 77. TIME EXPERIMENTATION 78. DOE: RESPONSE SURFACE METHODS 79. SEQUENTIAL RESPONSE SURFACE METHODS 80. DOE: ROBUST DESIGN 81. REGRESSION 82. THE LEAST SQUARE FORMULA 83. DOE THEORY 84. VARIANCE INFLATION FACTORS AND CORRELATION MATRICES 85. REGRESSION NUMERICALS 86. STATISTICS IN SIX SIGMA 87. AOV BY MULTIPLE T-TESTS 88. NORMAL PROBABILITY PLOTS 89. REGRESSION MODELING FLOWCHART 90. ADVANCED REGRESSION AND ALTERNATIVES 91. KRIGING MODELS 92. NEURAL NETS FOR REGRESSION 93. FITTING LOGIT MODELS 94. LOGISTIC REGRESSION AND DISCRETE CHOICE MODELS 95. CASE STUDY: THE RUBBER MACHINE 96. SYSTEM BOUNDARIES 97. LOW COST RESPONSE SURFACE METHOD 98. PROBABILISTIC MODELS INTRODUCTION 99. DISCRETE PROBABILITY SPACES 100. And More Topics