Six Sigma Green Belt Certification and Training

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Who should go for this course?

The course is designed for professionals who want to learn Process improvement techniques and wish to apply it on Operational metrics & performance data e.g. : ASA,AHT, MTFR, MTTR, MTTC, MTIA, FDR, CSAT, TSAT etc.

Best Suited for
1. Delivery leaders
2. Business Analyst/ Data analyst
3. Project Managers
4. Process Improvement Experts
5. Change management teams
6. Program Managers

Pre-requisites
1. Basic project management knowledge
2. Basic knowledge on statistics (or) & improvement techniques
3. Ability to drive change & leadership skills
4. Effective communication & Presentation skills
5. Knowledge on MS office applications (MS Excel & MS PowerPoint)
Why Learn Six Sigma?
Six sigma is a set of techniques that follows a methodological approach for bringing process improvements by aligning organizational goals. Organizations worldwide seek experts who understand the strategic objectives of businesses, critical requirements and operational goals to drive improvement.

Every service industry/organization seeks for process experts with Six Sigma techniques to be a leader in the competitive market.

1. 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
2. MEASURE
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.
3. ANALYZE
Learning Objectives: 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. 
4. IMPROVE
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
5. CONTROL
Learning Objectives: 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

Jonathon Smith

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