Learning Objective: 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 Objective: 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 Objective:: In this module, you will be introduced to important aspects of CloudFormation. Along with it, you will also learn how to use CloudFormation Templates to model and provision the AWS resources in an automated and secure manner for your application.

Topics

• Introduction to CloudFormation
• CloudFormation Template
• Intrinsic Functions & Conditions
• Stack Creation
• Advanced CloudFormation Concepts - CloudFormation Nesting, CloudFormation Wait Conditions & Wait Condition Handlers, CloudFormation Helper Scripts, CloudFormation Custom Resources
• CloudFormation Stack Updates
• CloudFormation Resource Deletion Policy
• CloudFormation Best Practices
• Troubleshooting

Hands-On:

• Creating an S3 Bucket using CloudFormation by Hardcoding the Name
• Creating an S3 Bucket using Intrinsic Function (Join And Ref)
• Creating and Configuring EC2 Instance using Helper Scripts
• Creating a Custom Resource with the help of Lambda Function

Learning Objective: In this module, you will understand the Data driven approach of analyzing data and 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 variation (ANOVA), Analyzing the statistical significance of 2 data sets using Correlation, Usage of Regression models to predict & estimate Y’s with X inputs. Interpreting Chi-Square to determine statistical significance.

Learning Objective: 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 Objective: In this module, you will learn to develop 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