Course Outline

Introduction

  • Advanced overview of Six Sigma
  • Deep dive into each phase of DMAIC
  • Roles and responsibilities of a Black Belt

Define Phase - Project Definition and Management

  • Advanced techniques in defining project scope and goals
  • Stakeholder analysis and communication
  • Developing a strong project charter

Measure Phase - Data Collection and Analysis

  • Designing effective data collection strategies
  • Measurement System Analysis (MSA)
  • Advanced statistical techniques for data analysis

Analyze Phase - Identifying Root Causes

  • Advanced tools for root cause analysis
  • Statistical hypothesis testing
  • Process behavior analysis

Improve Phase - Solution Development and Implementation

  • Design of Experiments (DOE)
  • Solution ideation and selection techniques
  • Risk assessment and mitigation in improvement strategies
  • Pilot testing and full-scale implementation

Control Phase - Sustaining Improvements

  • Developing effective control plans
  • Statistical Process Control (SPC) strategies
  • Ensuring long-term process stability

Advanced Project Management Skills

  • Leading complex projects
  • Managing project teams and dynamics
  • Time and resource management

Leadership and Change Management

  • Leadership skills for Black Belts
  • Organizational change management
  • Training and mentoring Green Belts and team members

Black Belt Certification

  • Preparation for the Black Belt certification exam
  • Tips and best practices

Summary and Next Steps

 

Requirements

  • Green Belt certification
  • Experience in managing or participating in process improvement projects

Audience

  • Senior project managers
  • Six Sigma professionals
 21 Hours

Number of participants



Price per participant

Testimonials (5)

Related Courses

Introduction to Data Visualization with Tidyverse and R

7 Hours

Econometrics: Eviews and Risk Simulator

21 Hours

HR Analytics for Public Organisations

14 Hours

Statistical Analysis using SPSS

21 Hours

Talent Acquisition Analytics

14 Hours

Advanced R

7 Hours

Algorithmic Trading with Python and R

14 Hours

Anomaly Detection with Python and R

14 Hours

Programming with Big Data in R

21 Hours

R Fundamentals

21 Hours

Cluster Analysis with R and SAS

14 Hours

Data and Analytics - from the ground up

42 Hours

Data Analytics With R

21 Hours

Data Mining with R

14 Hours

Deep Learning for Finance (with R)

28 Hours

Related Categories

1