BLACK BELT PROGRAM

TRAINING PROGRAM FOR SIX SIGMA BLACK BELT PROGRAM (6 DAY)

Course Objective :-

• Learn how to integrate principles of business, statistics and engineering to achieve tangible results.
• Master the use of Six Sigma to quantify the critical quality issues in your company.
• Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure.
• Six Sigma methods increase productivity and enhance quality.
• As a Six Sigma green belt, you will be equipped to support and champion Six Sigma implementation in your organization.

Who should attend ?

• Managers, supervisors, and employees and who want to apply quality control process, efficiency, and methodologies learned in class to a project within their own business.
• Quality system managers
• Operations manager
• Production and materials managers
• HR managers
• Finance and commercial managers
• High potential employees

What will I learn?

• Upon completion of this course, participant should be able to learn :
• History of Six Sigma
• Problem solving
• Basic statistics and displays of data
• Process mapping and measurement techniques
• Six sigma tools
• DMAIC process improvement roadmap
• How to establish customer requirements
• How to measure and quantify process performance
• Statistical and other analytical methods for identifying and understanding sources of variation

Day 1

  • Session 1

    • What is Six sigma?
    • History of Six sigma?
    • What is Sigma and why is it Six sigma?

Day 1

  • Session 2

    • How does Six sigma DMAIC process works?.
    • Six sigma Roles and responsibilities. .
    • Six sigma Vs Business Process Re-engineering..

Day 1

  • Session 3

    • What is Statistics?
    • What is Descriptive Statistics?
    • What is Inferential Statistics?
    • Accuracy v/s Precision .

Day 1

  • Session 4

    • Capturing Voice of customer
    • Kano model
    • CTQ drill down tree.

Day 2

  • Session 1

    • Project charter.
    • Team Dynamics.
    • SIPOC.
    • P-Map and Process flow diagram.
    • Cause and Effect matrix

Day 2

  • Session 2

    • 7 types of wastes.
    • Value added and non-value added activities.
    • Concept of Value stream mapping.

Day 2

  • Session 3

    • Identifying project output.
    • Types of data.

Day 2

  • Session 4

    • Data collection strategy -Sampling.
    • Sample size calculation.
    • Measurement system analysis – Continuous data and Discrete data.

Day 3

  • Session 1

    • Understanding process variation.
    • Process capability.

Day 3

  • Session 2

    • Data collection strategy - Sampling.
    • Measurement system analysis – Continuous and Attribute

Day 3

  • Session 3

    • Data analysis (Graphical representation).
    • Box plot , Pareto , Stratification , Time – series plot

Day 4

  • Session 1

    • What is Hypothesis?.
    • Hypothesis Testing Errors.
    • Hypothesis testing (T test and Annova) , Regression analysis , Chi-square analysis.
    • Examples of hypothesis testing.

Day 4

  • Session 2

    • Multi-Vari charts.
    • Non parametric tests.

Day 5

  • Session 1

    • Introduction to DOE .

Day 5

  • Session 2

    • Designing a DOE with Minitab.

Day 5

  • Session 3

    • Example of DOE – Exercise.

Day 5

  • Session 4

    • Solution parameter’s
    • Generate possible solutions
    • Pilot solution implementation.

Day 6

  • Session 1

    • FMEA analysis
    • Mistake proofing (Poka Yoke).

Day 6

  • Session 2

    • Designing a DOE with Minitab.

Day 6

  • Session 3

    • Statistical process control.
    • Types of control charts - Continuous and Attribute.
    • Control plan.

Day 6

  • Session 4

    • Revision and Mock test.