Lean Six Sigma Practitioners Black Belt - week 1 Training in Denmark

This course forms the first week of the ten-day Lean Six Sigma Black Belt programme, delivered as two separate five-day sessions. It is designed for experienced Green Belts who want to develop the analytical and project leadership capability required to manage larger and more complex process improvement initiatives.

Lean and Six Sigma are established improvement approaches focused on reducing process variation and waste in order to increase value for customers, employees, and stakeholders.

This practical programme develops the technical and analytical skills required at Black Belt level. Theory, examples, and hands-on activities are combined so that participants can apply advanced statistical tools with greater confidence in complex improvement projects.

Week 1 concentrates mainly on the Define, Measure, and Analyse phases of DMAIC. Participants learn how to clarify customer requirements, assess data collection and measurement systems, evaluate process performance, and carry out statistical analysis using Minitab.

Participants are expected to meet the following requirements:

• Completion of a Lean Six Sigma Green Belt course with QA or another recognised provider,
• Ideally, completion of at least one Green Belt project using the DMAIC framework,
• Identification of a second improvement project to complete after the training; this is required for accreditation,
• Access to Minitab for the analytical activities used throughout the course.

Use of a personal device is strongly recommended.

Suggested Pre-Reading

• Lean Six Sigma and Minitab – Quentin Brook
• Statistics Without Tears – Derek Rowntree

This course is particularly suitable for:

• Experienced Green Belts,
• Practitioners who want to lead larger and more complex improvement initiatives,
• Professionals seeking to develop and be recognised as Lean Six Sigma Black Belts.

By the end of Week 1 of the ten-day programme, participants will be able to:

• Explain the key principles and phases of the Six Sigma DMAIC methodology,
• Apply advanced problem-solving tools to identify, analyse, and improve organisational processes,
• Use Voice of the Customer (VOC) and Critical-to-Quality (CTQ) analysis to define customer requirements,
• Apply Data Collection Planning and Measurement System Analysis to assess the reliability of process inputs,
• Evaluate process performance using Process Capability studies and other statistical tools,
• Perform basic and advanced statistical analysis with Minitab,
• Contribute more effectively to the leadership of cross-functional Lean Six Sigma projects,
• Apply the Define and Measure phases of DMAIC in practical project settings using suitable tools and documentation.

Define

Quality Function Deployment

• Structuring customer requirements
• Analysing Voice of the Customer information
• Identifying Critical-to-Quality requirements
• Linking customer expectations with process requirements
• Using Quality Function Deployment within improvement projects

Measure

Balanced Scorecards

• Evaluating organisational and process performance from different perspectives
• Structuring performance measures
• Connecting measurement approaches with project objectives

Introduction to Minitab

• Exploring the Minitab interface
• Organising data in Minitab
• Preparing information for analysis
• Introduction to interpreting statistical output

Basic Statistical Concepts

• Core statistical terminology
• Measures of central tendency and variation
• Understanding data distributions
• Population and sample concepts

Central Limit Theorem

• Understanding the Central Limit Theorem
• Behaviour of sampling distributions
• Its role in statistical inference

Measurement Systems Analysis (MSA)

• Introduction to Measurement System Analysis
• Assessing the reliability of measurement systems
• Understanding the impact of measurement error on process analysis

Continuous Data MSA

• Measurement System Analysis for continuous data
• Evaluating measurement system variation
• Interpreting MSA results

Discrete Data MSA

• Measurement System Analysis for discrete data
• Evaluating the reliability of categorical measurements
• Interpreting measurement system performance

Standardised Normal Distribution

• Standard Normal Distribution
• Z-scores
• Using the normal distribution for probability assessment
• Standardising process data

Process Capability – Cp and Cpk

• Understanding Process Capability
• Calculating and interpreting Cp
• Calculating and interpreting Cpk
• Evaluating process spread and centring
• Assessing whether a process can meet specification requirements

Process Efficiency Calculations

• Measuring process efficiency
• Evaluating value-added and non-value-added activity
• Analysing process performance numerically

Analyse

Graphs Using Minitab

• Visualising process data
• Identifying patterns and trends
• Reviewing data distributions
• Using graphical analysis before statistical testing

Statistical Tests Using Minitab

• Introduction to Statistical Tests
• Interpreting test results
• Understanding p-values and decision rules
• Statistical significance

Hypothesis Testing for Continuous Data

• Defining hypotheses for continuous data
• Null and Alternative Hypotheses
• Selecting an appropriate statistical test
• Interpreting results in a project context

One-Sample t-Test

• Comparing a sample mean with a target value
• Reviewing test assumptions
• Interpreting Minitab output

Two-Sample t-Test

• Comparing the means of two groups
• Evaluating whether differences are statistically significant
• Interpreting the business meaning of the results

Homogeneity of Variance Test

• Comparing variation between groups
• Assessing equality of variances
• Determining suitability for further analysis

Balanced ANOVA

• Comparing the means of multiple groups
• Assessing the effect of factors on process outputs
• Interpreting ANOVA results

Hypothesis Testing for Discrete Data

• Statistical testing for discrete and categorical information
• Evaluating hypotheses based on proportions and frequencies

One and Two Proportion Tests

• Comparing a single proportion with a target
• Comparing proportions between two groups
• Assessing the statistical significance of differences

Chi-Squared Test

• Evaluating relationships between categorical variables
• Comparing observed and expected frequencies
• Interpreting results

Non-Normal Data

• Recognising data that does not follow a normal distribution
• Understanding when non-parametric methods are required
• Selecting an appropriate analysis method

Mann-Whitney U-Test

• Comparing two independent groups with non-normal data
• Interpreting results
• Using a non-parametric alternative to standard comparison tests

Mood's Median Test

• Comparing medians across multiple groups
• Working with non-normal data
• Interpreting findings within a process improvement context

Black Belt Application

Throughout the week, participants apply Define, Measure, and Analyse tools to realistic improvement scenarios. Minitab analysis, interpretation of statistical results, and project documentation are combined to prepare participants for the more advanced Improve and Control work covered in Week 2.