GreyCampus Logo

About

Contact


Premium Resources
Training Courses
Free Resources
Open CampusBlogMock ExamsDownloadables

Six Sigma Black Belt Curriculum | Six Sigma Black Belt


Six Sigma Black Belt Curriculum



Six Sigma Black Belt Curriculum


Module 1: Define


Introduction to Six Sigma



  • History of Six Sigma


  • Need for Six Sigma


  • Six Sigma metrics


  • DMAIC (Define, Measure, Analyze, Improve, Control) methodology overview


  • Examples of Six Sigma results
     



Voice of the customer (VOC)

CTx (quality, time, cost)


Converting VOC to CTQs (critical to quality)


SIPOC (supplier, input, process, output, customer)


Pareto analysis


Project charter



  •  Business opportunity


  •   Problem statement  


  •   Objective primary and secondary metrics


  •    Scope


  •    Cost of poor quality (COPQ)


  •     Project teams



Stakeholder analysis


Module 2 : Measure


Process mapping


Fishbone diagram


Graphical tools



  • Histogram


  • Dot plot


  • Boxplot


  • Scatterplot


  • Time series plot


  • Pareto chart



Basic statistics and probability



  •  Types of data


  • Accuracy versus precision


  • Mean, median, mode


  • Range, interquartile range, variance, standard deviation


  • Sample versus population


  • Percentiles


  • Central limit theorem


  • Confidence intervals



Process distributions



  • Normal distribution


  • Exponential, Weibull, lognormal


  • Binomial, Poisson



Lean concepts



  •  Value stream, flow


  • Batch versus single-piece flow


  • Seven forms of waste


  • Push versus pull systems


  • Kanbans, work cells


  • Supply chain, just-in-time


  • 5S (sort, straighten, shine, standardize, sustain) and visual management


  • Standard work


  • OEE (overall equipment effectiveness)



Sampling and data collection



  • Sampling bias


  • Sampling techniques:  random, stratified random, systematic, rational subgrouping


  • Power and sample size calculations



Process capability



  • Process stability


  • Normal capability analysis (Cp, Cpk, CPM, Pp, Ppk)


  • Non-normal capability analysis


  • Binomial and Poisson capability analysis


  • Rolled throughput yield (RTY), defect per unit (DPU), defects per million opportunities (DPMO), Sigma level (including shift)



Measurement system analysis



  • Variable gage R&R


  • Destructive testing


  • Crossed versus nested designs


  • Attribute Gage R&R



Module 3 : Analyze


Failure mode and effects analysis (FMEA)


Multi-vari analysis


Inferential probability distributions



  • Normal


  • Chi-square


  • True, False


  • Binomial, Poisson



Hypothesis testing



  • Anderson-Darling normality test


  • One-sample t-test


  • Two-sample t-test


  • Paired t-test


  • One-way analysis of variance (ANOVA)


  • One-sample test for variation


  • Two-sample test for variation


  • Test for equal variance


  • One-sample sign


  • Mood’s median test


  • One-proportion test


  • Two-proportion test


  • Chi-squared contingency table


  • One-sample Poisson rate


  • Two-sample Poisson rate



General ANOVA


Correlation and regression


Multiple regression


Binary logistic regression


Design of experiments (DOE) strategies


2k full factorial DOE


DOE center points, blocking, covariates


2k fractional factorial DOE


General full factorial DOE


Central composite design


Module 4 : improve


Innovative solutions (brainstorming, etc.)


Selecting a solution (Pugh matrix)


DOE multiple response optimization


Response surface methodology


Evolutionary operation (EVOP)


Lean tools



  • Lean measures of time:  lead time, takt time, completion time, cycle time 

  • Value stream mapping


  • Time value mapping


  • Theory of constraints


  • Load charts/line balancing


  • Spaghetti chart



Queuing theory


Improve techniques



  • Self-inspection


  • Training


  • Checklist


  • Process simplification


  • Mistake proofing



Implementation and verification (piloting, etc.)


Module 5 : Control


Statistical process control



  • I-MR charts

  • Xbar-R charts


  • Xbar-S charts


  • P-charts


  • C-charts


  • U-charts



Control plans



  •  What, who, where, how often, how much


  • Decision criteria



 Action plan



  • Management engagement and handoff


  • Project closure 



Here are some tips which  might help you before taking up the exam :



  • Initially, be familiar with all the definitions and topics of each and every chapter. The below table shows you the percentage of questions from each section




























































 Section Title  % of Exam
 IX  Analyze     15%
 V  Define -Tools  15%
 VI  Measure - Data 12%
 III  Lean - DFSS 10%
 IV  Define - Teams 10%
 VII  Measure - Portability 10%
 VIII  Measure - Capability 8%
 XI  Control Concepts 8%
 X  Improvement 7%
 II  Six Sigma Goals 5%

 



  • Maintain a notepad with you where you can mention your keywords and topics that you can quickly revise before taking up the exam


  • Do answer the sample questions at the end of every chapter. After answering all the questions go through the questions which are incorrect, read the explanation and make a note of it which would be helpful


  • Review each section as many times as possible


  • Start with easy questions. Do not stay on the problem that you are stuck on. If you skip any question make a star mark on it so you can quickly find out.


  • Arrive early to the exam hall



 





GreyCampus Logo

Company
AboutContactTerms of UsePrivacy Policy
Bootcamps
Data Science CoursePower BI CourseApplied Generative AI CourseCertificate Program in Data Science and Machine Learning