Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You’ll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.
Become a Six Sigma Green Belt Expert by mastering concepts like Fishbone/Ishikawa diagram, Root Cause analysis, Co-relation & Statistical analysis of data while working on industry-based Use-cases and Projects.
The curriculum of the course is as follows
- Overview & Define
Learning Objectives: In this module, you will learn about overview and history of Six Sigma, benefits of Six Sigma and you will get a kick start on a Six Sigma Project.
Topics: Introduction & overview of six sigma project management, Who are customers, what are the types of customers?, What is Voice Of Customer(VOC), What are Critical To Quality (CTQ ), How do we map CTQs to internal Critical to Business Processes (CBP ), Elements of a project charter- Problem statement, Business case, Goal statement, Project scope & Project team
Learning Objectives: In this module, you will understand baselining the metric of Improvement, the types of data and Data Collection plan.
Topics: Process analysis & mapping, Identifying the detailed AS-IS processes, Understanding the tools to create a process map, Dos & Don’ts of process mapping, SIPOC, Understanding data, Types of data & characteristics, Basic statistics – Mean, Median, Mode, Standard deviation & Variation, Data collection techniques, Understanding data sampling and techniques, Defining a unit and evaluating DPU, DPO, DPMO, Computing process sigma for discrete & continuous data type.
Learning Objectives: In this module, you will understand the Data-driven approach of analyzing data and the use of various statistical techniques to infer results.
Topics: Data analysis techniques, Tools used for data analysis, Pareto Chart, Fishbone, FMEA. Statistical hypothesis to validate the assumption ( Assumption based on P-value), Understanding Type I and Type II error, Analysis of Means using a variation (ANOVA), Analyzing the statistical significance of 2 data sets using Correlation, Usage of Regression models to predict & estimate Y’s with X inputs.
Learning Objectives: Various improvement methodologies to improve the process, prioritizing Root causes and shortlisting solutions.
Topics: Understanding Value, Evaluating Value added & non-value-added activities using Value stream mapping, 5S ( Set, Sort, Straighten, Strengthen, Stabilize), Muda, Driving Kaizen events
Learning Objectives: In this module, you will learn to develop a control mechanism. You will also learn about tools like Control Charts, which will be used depending on the type of data and sustenance measures.
Topics: Establishing Control Plans to sustain gains, Introduction to Statistical Process Control (SPC) Charts, Identifying special and common causes, etc. ,Selection and application of right control charts, Application of the following types of Control charts: X bar R, X bar S, individual and moving range (ImR/ Xmr),NP,P,C and U. Analysis of Control Charts- Interpret control charts results, common & special causes using rules for determining statistical control. Controlling the changes using poka-yoke (mistake proofing), Analyzing new process capability and defining control plans.
For more details and enrolling, click on the link below.