SixSigma IASSC

Who Should Take This

Aspiring quality professionals, recent graduates, and entry‑level analysts who seek to validate their understanding of DMAIC fundamentals should pursue this certification. It equips them with the analytical tools needed to define problems, measure performance, and control processes, supporting career advancement in continuous‑improvement roles.

What's Covered

1 Define Phase

2 Measure Phase

3 Control Phase

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph

Practice Questions

Lesson Modules

Console Simulator Labs

Exam Tips & Strategy

20 Activity Formats

Course Outline

60 learning goals

1 Define Phase

4 topics

The Basics of Six Sigma

Describe the multiple meanings of Six Sigma (philosophy, metric, methodology) and trace the history of Six Sigma and continuous improvement from Shewhart through modern applications.
Identify the key deliverables of a Lean Six Sigma project including process improvement, defect reduction, cost savings, and customer satisfaction enhancement.
Explain the Y=f(x) problem-solving strategy and describe how identifying and controlling input variables (Xs) drives predictable output performance (Y).
Differentiate between voice of the customer (VOC), voice of the business (VOB), and voice of the employee (VOE) and explain how each perspective informs project selection.
Identify Six Sigma roles and responsibilities including Yellow Belt, Green Belt, Black Belt, Master Black Belt, Champion, and process owner within the organizational structure.
Explain the DMAIC methodology phases and describe the specific purpose, key activities, and deliverables of each phase in a Lean Six Sigma project.
Apply the Y=f(x) framework to a given process scenario to identify the critical input variables that most significantly influence the output quality characteristic.

The Fundamentals of Six Sigma

Explain process definition concepts including process boundaries, inputs, outputs, and the relationship between process steps and customer requirements.
Apply critical-to-quality (CTQ) characteristic identification to translate customer needs into measurable process requirements.
Calculate and interpret cost of poor quality (COPQ) including internal failure costs, external failure costs, appraisal costs, and prevention costs.
Apply Pareto analysis (80/20 principle) to prioritize defect categories and focus improvement efforts on the vital few causes that account for the majority of quality issues.
Calculate and interpret basic Six Sigma metrics including DPU, DPMO, first time yield (FTY), rolled throughput yield (RTY), and cycle time for process performance assessment.
Differentiate between internal and external failure costs within the COPQ framework and explain how shifting investment from failure costs to prevention costs improves overall quality economics.
Explain the relationship between sigma level, DPMO, and process yield and describe how organizations use sigma level as a universal benchmark for process performance.

Selecting Lean Six Sigma Projects

Develop a business case for a Lean Six Sigma project that articulates the problem, expected benefits, resource requirements, and alignment with organizational strategy.
Create a project charter specifying problem statement, project scope, goals, timeline, team members, and key milestones for a Lean Six Sigma initiative.
Develop project metrics that quantify baseline performance and define measurable improvement targets for Lean Six Sigma project objectives.
Perform financial evaluation and benefits capture analysis to estimate the monetary impact of proposed improvements and track realized savings.
Analyze a project charter to evaluate whether the problem statement, scope, goals, and metrics are sufficiently defined to support a successful Lean Six Sigma initiative.
Explain how project selection criteria balance strategic importance, financial impact, customer urgency, and resource availability to build a prioritized improvement project pipeline.

The Lean Enterprise

Describe the principles and historical evolution of Lean methodology and explain how Lean and Six Sigma complement each other in integrated improvement approaches.
Identify the seven elements of waste (overproduction, correction, inventory, motion, overprocessing, conveyance, waiting) and provide examples of each in manufacturing and service contexts.
Analyze a process scenario to identify which of the seven wastes are present, estimate their impact, and prioritize waste elimination opportunities.
Apply the 5S methodology (sort, straighten, shine, standardize, self-discipline) to organize workspaces, reduce waste, and establish visual workplace standards.
Explain the concepts of value-added and non-value-added activities and apply value analysis to classify process steps and identify candidates for elimination or improvement.
Describe the relationship between Lean thinking and customer value, explaining how the five Lean principles (value, value stream, flow, pull, perfection) drive continuous improvement.

2 Measure Phase

4 topics

Process Definition Tools

Construct cause-and-effect (fishbone/Ishikawa) diagrams to systematically organize potential causes of process problems across categories of people, methods, machines, materials, measurements, and environment.
Create detailed process maps and SIPOC diagrams to document process flows, identify process boundaries, and establish the relationship between suppliers, inputs, process steps, outputs, and customers.
Construct value stream maps to visualize material and information flow, identify value-added and non-value-added activities, and quantify process lead times.
Apply X-Y diagrams to identify and prioritize the relationship between process inputs (Xs) and output responses (Ys) for focused data collection and analysis.
Construct a Failure Mode and Effects Analysis (FMEA) by identifying potential failure modes, assessing severity, occurrence, and detection, and calculating risk priority numbers to prioritize actions.
Analyze a value stream map to identify bottlenecks, excessive inventory, long wait times, and opportunities for flow improvement in the current-state process.
Apply process mapping techniques to identify rework loops, handoff points, and decision points that contribute to cycle time and defect generation.

Six Sigma Statistics

Calculate and interpret basic descriptive statistics including mean, median, mode, range, variance, and standard deviation for process data characterization.
Describe the properties of the normal distribution including the empirical rule (68-95-99.7), z-scores, and how normality assumptions affect statistical analysis methods.
Apply normality assessment techniques to determine whether process data follows a normal distribution and identify when data transformations may be needed.
Interpret graphical analysis displays including histograms, box plots, run charts, and scatter diagrams to identify patterns, trends, and outliers in process data.
Differentiate between population parameters and sample statistics and explain why sample-based estimates are used to make inferences about process populations.
Construct and interpret a Pareto chart from defect frequency data to visually identify the vital few defect categories that account for the majority of quality problems.

Measurement System Analysis

Define precision and accuracy in measurement systems and explain how measurement errors can mask or exaggerate true process variation.
Explain bias, linearity, and stability concepts in measurement systems and describe how each property affects the validity of collected process data.
Explain gauge repeatability and reproducibility (GR&R) studies for both variable and attribute measurement systems and interpret study results to assess measurement system adequacy.
Analyze GR&R study results to determine the proportion of observed variation attributable to the measurement system versus actual process variation.

Process Capability

Explain process capability analysis concepts and describe the relationship between process variation, specification limits, and capability indices.
Explain the concept of process stability as a prerequisite for capability analysis and describe how control charts verify that a process is in statistical control.
Describe attribute and discrete process capability assessment methods and explain how they differ from variables capability analysis.
Apply process monitoring techniques to track ongoing process performance and identify when process capability has shifted or degraded.
Analyze process capability results to determine whether a process is capable of meeting customer specifications and identify whether centering or variation reduction is the priority.
Differentiate between common cause and special cause variation and explain how each type of variation affects process stability and capability assessment.

3 Control Phase

2 topics

Lean Controls

Apply 5S control methods to sustain workplace organization improvements and conduct 5S audits to assess adherence to established standards.
Explain kanban systems as pull-based production control mechanisms and describe how kanban signals regulate work-in-process inventory and production flow.
Apply poka-yoke (mistake-proofing) strategies to design error detection and prevention mechanisms that eliminate defects at the source.
Analyze a workplace scenario to recommend specific 5S interventions, kanban implementations, or poka-yoke devices that address identified waste and quality issues.
Explain how Lean control mechanisms integrate with statistical monitoring to provide a comprehensive process control system that addresses both waste and variation.

Six Sigma Control Plans

Perform cost-benefit analysis to evaluate the economic justification for implementing and maintaining process control mechanisms.
Describe the elements of a Six Sigma control plan including what to measure, how to measure, measurement frequency, who is responsible, and control limits.
Develop response plans that specify corrective actions, escalation procedures, and communication protocols when process monitoring detects out-of-control conditions.
Analyze control plan effectiveness by evaluating whether monitoring methods adequately detect process shifts and whether response procedures restore process stability.
Apply documentation and standardization practices to capture improved process methods in standard operating procedures, training materials, and visual work instructions.
Describe the process of transferring project ownership to the process owner and establishing ongoing monitoring responsibilities after DMAIC project closure.

Scope

Included Topics

All sections in the IASSC Lean Six Sigma Yellow Belt Body of Knowledge covering the Define, Measure, and Control (DMC) phases of the DMAIC methodology.
Define phase: Six Sigma basics (history, continuous improvement, Y=f(x), VOC/VOB/VOE, roles and responsibilities), Six Sigma fundamentals (process definition, CTQs, COPQ, Pareto analysis, basic metrics DPU/DPMO/FTY/RTY/cycle time), Lean Six Sigma project selection (business case, project charter, metrics, financial evaluation), and Lean enterprise (Lean principles, history, seven wastes, 5S methodology).
Measure phase: process definition tools (cause-and-effect diagrams, process mapping, SIPOC, value stream mapping, X-Y diagrams, FMEA), Six Sigma statistics (basic and descriptive statistics, normal distributions, normality, graphical analysis), measurement system analysis (precision, accuracy, bias, linearity, stability, GR&R), and process capability (capability analysis, stability, attribute capability, monitoring).
Control phase: Lean controls (5S control methods, kanban systems, poka-yoke), Six Sigma control plans (cost-benefit analysis, control plan elements, response plans).

Not Covered

Analyze and Improve phases of DMAIC covered in Green Belt and Black Belt certifications including hypothesis testing, regression analysis, ANOVA, and Design of Experiments.
Advanced statistical process control charts beyond basic concepts covered in Green Belt certification.
Organizational deployment, strategic planning, and enterprise-level Six Sigma program management.
Software-specific statistical analysis tool implementation.

Official Exam Page

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