Lean Six Sigma Green Belt Online Course with Python

Explain six sigma and sigma by linking standard deviation to process variation, using a train arrival example to show how six sigma reduces variability for reliable performance.

Explore why six sigma at 99.9996 percent outperforms 99 percent, measure process performance with the sigma scale, and build a business case to target six sigma for critical processes.

Explore the three interpretations of six sigma: as a performance measure with the sigma scale, as a problem solving tool using dmaic, and as a management philosophy guiding business decisions.

Six Sigma aims to reduce defects, improve yield, and ensure consistent delivery by focusing on data-driven, customer-centric decisions that lower variation to 3.4 defects per million opportunities.

Explore the ClearCalls case study, an end-to-end telecom project to improve installation turnaround time using Six Sigma, with affinity diagrams and data analysis.

Identify and apply Six Sigma terms like CTQ, customer specification limits (USL and LSL), defects, defect opportunities, and defectives, and understand how these metrics drive product quality and customer satisfaction.

Create the project charter and secure sign-off from the champion and stakeholders. Identify the CTQ and map the process to define scope and ensure alignment.

In the analyze phase of DMAIC, we use measure data to establish and validate the relationship between causes and the effect, and quantify its strength and impact on project goals.

Identify all possible solutions in the improve phase, refine by selecting and optimizing the best option to minimize risk, then pilot the solution in a test environment before full deployment.

Explore how to select Six Sigma projects with a structured criteria-based approach, aligning strategy, customer impact, feasibility, and resources, using the 80/20 principle, stakeholder voting, and a particle diagram.

Voice of customers gathers qualitative insights and verbatims to illuminate customer emotions and unarticulated needs, guiding profitable product design and a win-win for customers and the business.

Discover how to capture the voice of customers using active and passive, qualitative and quantitative methods, including surveys, interviews, focus groups, observation posts, and mystery shopping.

Plan, execute, and analyze a voice of customer study by segmenting customers, determining valid sample sizes, selecting VoC methods, and translating verbatims into requirements using affinity diagrams and Kano model.

Discover how to use an affinity diagram to group overwhelming survey feedback, organize data logically, and present it for further analysis across contexts.

Classify customer requirements into must-be, delighters, and one-dimensional using the Kano Model to prioritize features. Explore must-be, delighters, and one-dimensional categories with examples and guidance for prioritization.

Discover how to build a project charter with a clear business case and problem statement. Learn to define CTQs and metrics, and balance primary and secondary metrics for DMAIC projects.

Use the in-frame/out-frame tool to scope projects by framing items inside or outside. Teams silently place items, discuss boundaries, and move items to reach a clear, accountable scope.

Explore advanced process flow diagrams and deployment flow-charts to map complex processes, reveal inefficiencies, and apply process, decision, connector, and terminator symbols for re-engineering.

Enhance deployment flow-charts, provide clarity for stakeholders, manage customer expectations, boost accountability, reveal process complexity, enable level 2 and 3 drill-downs, support modeling for redesign, and enable automation through BPR.

Learn how a cause and effect diagram identifies all possible reasons for repeat customer calls. Use these insights to address the problem and boost team utilization and efficiency.

Learn to construct a fishbone (cause-and-effect) diagram through structured team brainstorming and brain-writing, focusing on root causes in six categories to improve processes.

Apply a cause and effect matrix to prioritize potential causes against CTQs with weighted sums and a simple three-point scale in Six Sigma problem solving.

Explore the difference between precision and accuracy, and learn how repeatability and reproducibility (gauge R&R) assess measurement variation across appraisers and equipment.

Explore attribute data, variable data, and locational data, learn when to use discrete versus continuous data, and understand converting data types and the role of locational data in screening.

Explore sampling concepts and methods, including random, unbiased, and representative samples, and distinguish population sampling from process sampling with practical examples.

Learn how population sampling estimates population characteristics using random sampling and stratified random sampling, with examples from pallet inspection and employee surveys.

Compute the continuous data sample size using n = (Z/delta)^2 * s^2, with Z = 1.96 for 95% confidence and s as the standard deviation.