Lean Six Sigma Green Belt Online Course with Python

Explore how Six Sigma acts as a highly disciplined management methodology aimed at near-perfection, targeting 3.4 defects per million opportunities to deliver value to customers and shareholders.

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.

Trace lean management from Toyota production systems to JIT, and see how delivering more value with less effort improves quality, reduces waste, and enables flexibility in customized mass production.

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.

Explore the four Six Sigma belt roles—yellow, green, black, and master black belt—and their training, project scope, and responsibilities, plus champion roles that sponsor projects and guide deployment strategy.

Apply a structured six sigma approach to solve business problems by discovering root causes with data and building a data-driven story, using the two-diamond framework to explore and refine solutions.

Explore six sigma problem solving approaches using an opportunity tree, from simple problems to just do it, low-hanging-fruit, dmaic and dfss projects in finance.

Discover the DMAIC five-step framework, define, measure, analyze, improve, and control, for incremental improvements to existing processes in six sigma. Learn when to apply DMAIC, its phases, and associated tools.

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.

Explore the DMAIC measure phase by identifying potential causes, validating the measurement system, collecting data (with sampling if needed), performing basic analysis, and establishing current process capability.

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.

Establish a monitoring plan to sustain improvements, align with the define phase charter, then implement changes, compute benefits, and finalize closure with sign-off on project results.

Design for Six Sigma (DFSS) guides creating new products or services at Six Sigma levels. It uses the IDOV method—identify, define, optimize, validate—to deliver quantum leaps and process reengineering.

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.

Define and classify customers as internal or external, with examples from manufacturing and service, and identify end users, intermediate customers, and regulators.

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.

Use the affinity diagram to organize large amounts of information by logical relationships, group customer feedback into buckets, and assign high-level titles to reveal actionable insights.

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.

Learn to measure customer satisfaction and loyalty with Net Promoter Score, using the 11-point scale, and interpret detractors, passives, and promoters to inform the customer experience.

Learn how a Six Sigma project charter defines business case, problem statement, objectives, scope, CTQ, team roles, and sponsor concurrence, while outlining potential benefits.

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.

Define project scope and boundaries to align duration with the goal, and craft a clear, quantifiable goal statement; engage stakeholders and use frame/out-frame analysis to scope effectively.

Identify the ARMI project team roles—approver, resource, member, and interested parties—and quantify benefits across DMAIC, then implement a translation plan within a 12–16 week duration.

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 process mapping tools in a lean six sigma context, gaining an overview of techniques to map, analyze, and improve processes as part of green belt practice.

Map end-to-end processes on a single page by mastering sipoc. Learn how suppliers, inputs, process, outputs, and customers interact to create value.

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.

Discover how deployment flow-charts organize complex business processes across multiple teams, establish ownership, and streamline training for a new e-channel deployment.

Explore deployment flow-charts, aka swim lanes, for cross-functional process maps that map customer feedback through multiple roles, visualize hand-offs and times, and set inter-departmental SLAs to improve customer experience.

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.

Link outputs to inputs using the Y=f(X) relationship, identify critical X factors like on-time dispatch, address accuracy, landmarks, GPS maps, resources, and traffic, and model their impact to improve performance.

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.

Explore the Ishikawa or fishbone diagram, a cause-and-effect tool by Kaoru Ishikawa, using six categories—man, machine, method, measurement, material, mother nature—to brainstorm all causes.

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.

Apply the 5 why technique to challenge the status quo and uncover root causes by asking why repeatedly, revealing underlying issues and guiding effective problem solving.

Explore the elements of a measurement system: environment, operator, and procedures, and learn how measurement variation adds to process variation to determine total variation.

Explore how resolution, accuracy, and precision define a measurement system, and learn to select gauges and use calibration, stability, drift, and linearity concepts for reliable measurements.

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 four measurement scales: nominal, ordinal, interval, and ratio, and learn how data are classified, ordered, and compared, with examples from service feedback, temperature, and piston diameters.

Explore the data collection roadmap for Six Sigma projects, outlining when, where, and in what format to collect data and the need for a comprehensive plan and template.

Develop a formal data collection plan for DMAIC projects by detailing Y and X measures, data sources, sampling, frequency, operational definitions, and time frame to ensure accurate data.

Create a comprehensive data collection template in a spreadsheet that captures the CTQ (Y) and all X factors, with D/ND and L/H indicators for reliable analysis.

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.

Explore process sampling on a live process by monitoring performance at regular intervals. Learn systematic sampling, pulling every nth data point, and rational sub-grouping to reveal variation.

Learn how to calculate the correct sample size for lean six sigma projects by choosing discrete versus continuous data, applying the appropriate formula, and avoiding sampling bias.

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.