Post Graduate Program in Lean Six Sigma Management

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Key Highlights

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Lean Management
Definition of Lean Management
Need of Lean Management in different types of Industries
History of Lean Evolution
Lean Principles and types of wastes
Examples of Wastes in different Industrial scenario
5S (Basic Lean tool )
Value stream Mapping
SMED Kaizen , Kanban
Industrial applications of above tools in Manufacturing , Service , Non-Manufacturing sectors
Case study of one project in Lean Manufacturing
Case study of One project in Non Manufacturing (Oce )
Case study of one project in Service industry
Lead time Concept
Cycle time Concept
Lead time concept
OEE concept and application in Industries
Other miscellaneous concepts
Theory of constraints
Lean Maturity Matrix and conclusions .
Minitab – Statistical Software tool
Introduction to Minitab tool
Basic statistics in Minitab
All Minitab based applications in Six sigma Green and Black Belt
a. Regression Analysis
b. ANova
c. Hypothesis Testing
d. Design Of experiments
Digital Transformation
Digital revolution History
The stages of Digital Maturity
Critical factors to consider
Strategic planning in Digital technology
Challenges in driving digital transformation
Importance of Digital transformation in Industries
Introduction to RPA
Difference between RPA and other tools
Benefits of RPA
Identify opportunities for Automation
Process Re engineering concepts
Best Practices at Enterprise Level
Case study on RPA in industrial oriented

Robotic Process Automation

Cloud and Devops

Digital Marketing and other digital concept

Certifed Lean Six Sigma Green Belt
Basics and History of Six sigma
Need of Six sigma tool to any Industry
Six Sigma and organizational Level Hierarchies, Business targets
DMAIC overview and Lean principles, Calculations of six sigma levels
Difference between Lean & six sigma
Define Phase overview
How to identify the project as Six sigma Green Belt Project
Voice of Customer
Determine CTQ
Develop project charter
Process mapping – SIPOC , COPIS , Flow chart applications
DPMO Calculations and Exercise on Sigma Levels Calculation
COPQ calculations
One Case study on Define phase
Measure phase Overview
Data Types
Basics on Probability and statistics definitions
Population and sampling
Data collection Plan
Gemba Audit
Measurement system Analysis basics
Process capability Indices and calculations on Cp , Cpk.
One case Study on Measure Phase
Analyse Phase overview
Data Analysis
Histogram , Box Plot , Project Examples
Root Cause Analysis -Cause and effect diagram , Why Why Analysis (5 Whys)
Hypothesis testing, Basics on each tool applications
Minitab oriented case study on Hypothesis testing a. One sample T test b. Two sample T test
Improve Phase overview
Generating creative solution ideas concepts
Lean Tools application while selecting solutions
FMEA Concept
Basics on Design of Experiments
Case study on Improve phase
Control Phase overview
Statistical Process Control (SPC)
Control Charts and different types of charts and applications
Applications of Lean tools in control phase
Standardization , Horizontal Deployment concept
Control Plan , Work instructions
Case study on Control Phase
Certifed Lean Six Sigma Black Belt
Pre –Requirements/Requisites for LSSBB Course
Difference Between Green Belt and Black Belt and Master Black Belt
Roles and Responsibilities of Six Sigma Teams in each level
Data Driven Six sigma concepts and traditional Quality concepts How to Select Project as Black Belt project
Define Phase overview for DMAIC , DFSS
Concept of DMAIC , DFSS for any project
VOC , Quality Function deployment
Kano Model
Concept of DMAIC , DFSS Projects and Examples
Case study project on DFSS project
Measure phase Overview
Data Types and Measures of Central tendency
Dispersion concept
Central Limit Theorem
Measurement system Analysis Concept
Gage R&R Concept
Continuous MSA , Attribute MSA
Normal and Non Normal data One case Study on Measure Phase
Analyse Phase overview
Data Analysis
Normal data
Non Normal data
How to check Normality in data through Minitab
Hypothesis Testing Criteria
Null and Alternate Hypothesis
Error Type
Significance Level (α) ,β and Power
P Value, and Acceptance and Rejection Conditions
Sample Size Determination for Tests , Sample z Test ,Test of Equality of Variances
Sample t Test ,Paired T Test
Correlation ,Regression Analysis
Simple/Linear Regression Analysis
Multiple Regression Analysis
Anova -1 way Anova
2 way Anova with replicates
Non Parametric Hypothesis Test a. Mann Whitney Test b. Wilcoxon Test c. Kruskal Wallis d. Mood’s Median
Case study on each tool through Minitab
Improve Phase overview
Solution Generation, Design of Experiments a. Generate solutions , Brainstorming the solutions prioritization b. Out of the box thinking c. Design of experiments
Design of Experiments a. Two Level factorial experiments b. Full factorial experiments c. DOE with Curvature d. Response surface methods i Piloting techniques and FMEA analysis a. Risk Mitigation b. Test solutions c. Pilot solutions d. Refine solutions e . FMEA, Error proving
Measurement System Reanalysis a. Gage R & R b. Cost benefit analysis
Case study on applicable tools through Minitab
Control Phase overview
Statistical Process control a. SPC Chart selection b. SPC Chart analysis
Control Plan
Case study on applicable tools through Minitab
Project Management

What is project I Program I Portfolio I Details of PMP exam

Role of PM with respect to Industry I Disciplines I Project I Organization

Develop Project Charter I Develop Project Management Plan I Direct and Manage Project Work I Manage Project Knowledge I Monitor and Control Project Work I Perform Integrated Change Control I Close Project

Collect Requirements  | Define Scope | Create WBS | Validate scope | Control Scope

Plan schedule Management | Define activities  | Sequence Activities | Estimate Activity Durations I Develop Schedule I Control Schedule 

Plan Cost Management I Estimate Costs I Determine Budget I Control Costs

Plan Quality Management I Manage Quality I Control Quality

Plan Resource Management I Estimate Activity Resources I Acquired Resources I Develop Team I Manage Team I Control Resources

Plan Communications Management I Manage Communications I Monitor Communications

Plan Risk Management I Identify Risks I Perform Qualitative Risk Analysis I Perform Quantitative Risk Analysis I Plan Risk Responses I Implement Risk Responses Monitor Risks

Plan Procurement Management I Conduct Procurements I Control Procurements

Identify Stakeholders I Plan Stakeholder Engagement I Manage Stakeholder Engagement I Monitor Stakeholder Engagement

Agile Scrum Master

This will cover introduction to Agile, Agile Mindset , Agile Manifesto and 12 principal on which Agile is based on I This will also cover other Agile Framework

This will cover introduction to Scrum Process Flow I Scrum Framework I Scrum values I Scrum Roles & Responsiblities I Common Artifacts like Product Backlog

This will cover introduction will cover Product Incremental I Sprint Planning Sprint Execution I Daily Scrum I Sprint Review Meeting I Retrospective Meeting I Burndown charts

This will cover introduction will cover Agile Framework I Scrum for complex projects and options for Scaled Agile

Capstone Projects

Capstone Projects

Lean Six Sigma Management

Lean Six Sigma Project in Manufacturing Process

Lean Six Sigma Management

Lean Six sigma Project in Non Manufacturing process

Lean Six Sigma Management

Lean Six sigma project in Service sector

Lean Six Sigma Management

Lean Six sigma Project in Pharmaceutical

Project Management

Create Project Charter

Project Management

Stakeholder Assessment Matrix

Project Management

Procurement- Calculate Cost Payable

Agile Scrum Master

Burndown Chart

Agile Scrum Master

Create Velocity

Agile Scrum Master

Sprint Backlog


On completion of the Post Graduate Program in Lean Six Sigma Management, aspirants will receive an Industry-endorsed Certificate.




B.Tech and MBA ( Finance & Operation ), Ph.D in Data Analysis.


Master degree in Production technology Bachelor degree in Mechanical Engineering, Diploma in Training & Development, currently doing MBA program in Data Analytics and Business Excellence.


Master in computer science



M.Tech (power electronics), MBA-HR, PhD in power quality improvement


M.B.A(Marketing & Finance )

Program Fees

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INR 25,000

*18% GST Extra


usd $600

Our Faculty

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Dinesh Babu R

UrbanPro Excellence Award winner in Data Science Professional
CBAP Certified Professional
Senior Business Analyst in the MNC, Part time, Providing Business Analysis as well as Data Analysis training to both Indian as well as overseas students.
Qualification: P.hd in Data Analysis, MBA (Finance & Operation), B.Tech

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Ganesh Bhure (Management LDP Program, B.Tech/B.E. (Electronics and Telecommunication))

11yrs into training. Working on various training assignments onPython, Machine Learning, Data Analytics, Artificial Intelligence, Docker and Kubernetes, Leadership and Product Management.

Software professional with 16+yrs of experience. Handled innovation strategic roles, Technical Consultant, Technical Manager, Project Lead, Product Development, Project Management, BusinessAnalysis, Technical Design, Python, C, C++, Docker, Kubernetes, Scale Testing, Product Benchmarking, Data Analytics, Machine Learning, Artificial Intelligence, Data Science.

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Suryanarayana Murthy (MCA, B.Sc (Electronics))

9.5 Years IT Experience in Big Data Hadoop and PERL as a Developer.
5.5 Years of comprehensive experience as Big Data Developer, Practical exposure and strong knowledge in Big Data management

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Boddu Lingaiah (M.Tech (CS), B.Tech (CS))

5+ Year teaching experience in Full Stack Web Development.

Boddu Lingaiah has over 10+ Years of experience as Master Trainer at Edunet Foundation & worked as IT Faculty at KL University and Jain University.
Studied M.Tech at SKEC Karepalli as well as worked as Assistant Professior for 1.3 Year.

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Abhishek Srivastava (M.Tech (CS), MCA)

IIT Kanpur Certified Data Science Trainer
Teaching experience in Data science, Machine Learning, Deep Learning, Artificial Intelligence
Data Science Trainer with 5+ Years of experience executing Data Driven solution to increase effeciency, accuracy and utility of internal data processing. Experinced to give training on regression models, using predective data modeling and analysing Data scienceAlgorithm to deliver insights and implement action- oriented to business problems.


  • Introduction to Linux
  • Root
  • Basic commands
  • Editors
  • OS installation
  • Basic system configuration and administration
  • Understanding files and directories
  • Schedulers
  • User administration
  • Software installation