Learning Lean Sex Sigma and its applications. Its importance in achieving operational excellence across diverse industries. Real-life examples and other modules led learning for better learning.

Learning about different phases of a Lean Six Sigma project. Understanding define phase to find the right problem to address and improve project selection for maximum process improvement.

This module covers the organizational applications and deployments of Lean Six Sigma. Learners will understand how Lea Six Sigma is applied across various industries with successful implementations.

Each module will have its own tests and quiz. After the successful completion and clearing the passing rate, learners will go through a final evaluation. Successful completion of this evaluation will grant the learner the Lean Six Sigma White Belt certification.

Understanding the methodologies used to map a process. This enables one to lay out a clear process roadmap to apply Lean Six Sigma to improve process and operations.

Cause and Effect matrix is an approach that takes major possibilities and projects its outcomes. This enables learners to understand the benefits and implications of any change in a process or business function.

This concept takes the learners to use different modes, projecting failures and worst case scenarios along with its effect on the whole process. With this learners can find solution for the root causes and bring operational excellence.

Each module will have its own tests and quiz. After the successful completion and clearing the passing rate, learners will go through a final evaluation. Successful completion of this evaluation will grant the learner the Lean Six Sigma Yellow Belt certification.

Presentations are very important when trying to obtain support from Upper Management for a project, or when trying to explain the progress of a team’s activity. The information being presented can be shown as a graph or as a Statistical Analysis. This module introduces the student to various Graphs and Statistics.

This module shows the student how to create graphs using the statistical software package, MINITAB or SigmaXL.

This module explains the concepts and types of graphs used to monitor a process. These methods are referred to as Statistical Process Control (SPC).

Verifying the accuracy and precision of data used in an analysis is crucial. Measurement Systems Analysis (MSA) will be used to determine if the data can be trusted.

The ability of a process to consistently produce outcomes that match a customers demands is critical to maintaining a client base. The module will explaining the Capability study that will show how often the customer demands are met.

This module summarizes the material covered in Session 1 of the Green Belt curriculum.

One of the issues with improvement projects is the lack of data. Once the number of potentially critical factors has been narrowed down, the Multi-Vari study provides the means to collect data on these variables.

Data from either historical records or a Multi-Vari study, requires proper analysis. This module provides the student with the background information to understand the results from different statistical analyses.

The first statistical test to be explained uses inputs that are discrete. and continuous output data. The t-Test and ANOVA tests are the proper tools to use with these types of data. These tests are investigating differences between population means.

When the Inputs and Outputs data are both continuous, the correct method of analysis is the Correlation and Regression. This module explains the basic information required to perform these tests.

Often times the easiest analysis might be to simply use input and output data that are both discrete, or attributes. To provide a basic look at this type of data, a Chi-Square test is performed. While the output of this type of analysis should be used with caution, it is useful in the preliminary look at a process. This module covers the basics of this type of test.

While the Multi-Vari module discussed how to obtain data on a passive basis, a Designed Experiment will show how to actively collect data through a systematic change of potentially critical variables.

Often times, reductions in defects can be obtained by implementing simple devices that will provide immediate feedback to the operator at the site of the error. This module discusses one class of these tools referred to as Mistake Proofing.

Once the improvement has been shown to work, a Control Plan is necessary to maintain the process at the new quality levels. This module explains what should be included in the Control documentation.

It is important that processes are documented to maintain a consistent outcome. This module explains what should go into a good documentation system for processes.

This module wraps up the information provided in Session 2 of the Green Belt curriculum.