Lean Six Sigma Black Belt Training Course

Duration 10 days 16 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Continuous Improvement Manager, Quality Manager and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-QP-029

Before you join: Participants should have practical exposure to operational processes, quality improvement, project work or performance reporting.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Lean Six Sigma Black Belts are expected to solve costly, persistent process problems that cross departments and resist local fixes. Typical assignments include reducing payment-processing errors in a bank, shortening procurement lead times in a public agency, improving laboratory turnaround times, cutting manufacturing scrap, or increasing first-pass approval rates in an insurance operation. These projects require more than problem-solving tools: they need credible data, financial discipline, sponsor alignment, controlled implementation and sustained controls.

This course develops the analytical and leadership capability to lead DMAIC improvement projects from charter through control. Participants learn to define customer-critical requirements, map value streams, establish valid measurement systems, quantify baseline performance, test root causes, model process relationships and select improvements. The course covers Lean flow methods, statistical hypothesis testing, regression, design of experiments, FMEA, control charts, control plans and change implementation. It also addresses project governance, benefit validation, executive communication and Black Belt coaching responsibilities.

Instructor-led delivery combines focused briefings with cases, group analysis, simulations and application planning. Participants work with realistic operational datasets and build project materials progressively during the course. The 10-day format provides about 65 contact hours, delivered in a classroom in 50+ cities or live online. Participants leave with a completed DMAIC project package for a selected operational problem, including a charter, analysis evidence, improvement plan, FMEA, control plan and executive presentation. A certificate of completion is issued to participants who complete the course.

The course suits experienced improvement practitioners, operational managers and technical specialists moving into cross-functional quality and productivity leadership. It is particularly relevant where performance depends on repeatable processes, reliable data and sustained adherence to operating standards.

What you will be able to do

  • Define a DMAIC project charter with measurable goals, scope, benefits and governance
  • Translate voice-of-customer evidence into CTQs, operational definitions and process measures
  • Evaluate measurement systems using Gage R&R, attribute agreement and data integrity checks
  • Analyse process performance using capability indices, defect metrics and statistical tests
  • Verify root causes using stratification, Pareto analysis, hypothesis testing and regression
  • Design Lean and Six Sigma improvements using flow analysis, FMEA and mistake-proofing
  • Create control plans using SPC charts, reaction plans, audits and accountable ownership
  • Present a data-supported DMAIC project package with validated benefits and implementation actions

Benefits of attending

For you

  • Lead high-value DMAIC projects with credible evidence and sponsor-ready decisions
  • Apply advanced statistical methods without relying on intuition or anecdotal causes
  • Build a portfolio-quality project package for quality and operational excellence roles
  • Strengthen credibility with finance, technical teams and senior operational leaders
  • Coach Green Belts and project teams through structured tollgates and analysis reviews

For your organisation

  • Reduces recurring defects, rework, delays and avoidable operating costs
  • Improves project selection through quantified benefits, scope and data readiness
  • Creates repeatable controls that protect gains after improvement teams disband
  • Raises internal capability to solve cross-functional process performance problems
  • Provides leaders with traceable evidence for investment, risk and resource decisions

Course modules

16 modules over 10 days

Participants can position a Black Belt project within business priorities, governance and ethical responsibilities.

  • Lean Six Sigma roles, governance and Black Belt project accountabilities
  • DMAIC tollgates, sponsor decisions and required project evidence
  • Strategic objectives translated into measurable quality, cost, delivery and safety gaps
  • Cost of poor quality categories: scrap, rework, warranty and complaints
  • Project selection matrix scoring impact, feasibility, urgency and data availability
  • Ethics, data integrity and escalation responsibilities within improvement governance

Participants can convert customer needs into measurable requirements and defined process boundaries.

  • Voice of customer collection using interviews, complaints, surveys and observation
  • Kano analysis separates basic, performance and attractive customer requirements
  • CTQ trees convert customer needs into measurable process specifications
  • Operational definitions align defect classification, counting rules and inspection decisions
  • SIPOC diagram sets boundaries, suppliers, inputs, outputs and customers
  • Requirements traceability links CTQs to metrics, owners and collection methods

Participants can map current performance and design a future-state process based on flow evidence.

  • Swimlane maps expose handoffs, approvals, queues and rework loops
  • Value stream maps distinguish processing time, waiting time and inventory
  • Documenting current-state workflows with standard symbols and decision logic
  • Process walk observations compare documented procedures with actual work
  • Identify bottlenecks through cycle-time, queue-length and capacity-demand analysis
  • Future-state design removes non-value-added steps and clarifies operating standards

Participants can establish a governed project with clear scope, ownership, benefits and stakeholder actions.

  • Project charters define problem statements, goals, scope, roles and timing
  • SMART problem and goal statements avoid solution-led project definitions
  • Stakeholder analysis maps influence, interest, resistance and communication needs
  • RACI matrix assigns accountable owners for decisions, data and actions
  • Benefit statements quantify savings, cost avoidance, capacity and service gains
  • Gate reviews test evidence quality before advancing DMAIC work

Participants can plan reliable data collection and evaluate the quality of measurement systems.

  • Data collection plans specify sources, sampling, timing, ownership and safeguards
  • Measurement system analysis separates repeatability, reproducibility, bias and stability
  • Gage R&R studies assess measurement error against process variation
  • Attribute agreement analysis tests inspector consistency for categorical decisions
  • Sampling plans address population definition, representativeness and practical constraints
  • Data integrity checks identify missing values, duplicates, outliers and coding errors

Participants can establish a statistically sound baseline and interpret process capability against specifications.

  • Frequency plots, boxplots and run charts reveal distribution and time patterns
  • Descriptive statistics summarise centre, spread, shape and unusual observations
  • Normality assessment informs capability calculations and transformation decisions
  • Process capability indices Cp, Cpk, Pp and Ppk interpreted correctly
  • Defect metrics calculate DPU, DPMO, first-pass yield and rolled throughput yield
  • Baseline performance establishes measurable improvement targets and financial exposure

Participants can generate, prioritise and verify potential root causes using process and data evidence.

  • Stratification separates performance by product, channel, team, shift and supplier
  • Pareto charts focus investigation on dominant defect categories and losses
  • Cause-and-effect diagrams organise potential causes across six process categories
  • Five Whys probes causal chains beyond symptoms and local fixes
  • Is-is not analysis defines problem boundaries and discriminating facts
  • Root-cause verification uses evidence, process observation and controlled testing

Participants can select and interpret statistical tests for process improvement questions.

  • Confidence intervals quantify uncertainty around means, proportions and differences
  • Null and alternative hypotheses frame testable improvement questions
  • t-tests compare means for independent, paired and one-sample data
  • Chi-square tests assess associations within categorical defect data
  • ANOVA compares multiple group means while controlling false positives
  • Nonparametric tests support skewed data, ranks and small samples

Participants can model process drivers and test factor settings for improved performance.

  • Correlation analysis distinguishes association strength from causal evidence
  • Simple and multiple regression model continuous process responses
  • Residual diagnostics test linearity, independence, constant variance and normality
  • Logistic regression estimates defect probability from process factors
  • Factorial designs screen main effects and interactions efficiently
  • Response optimisation identifies factor settings meeting target specifications

Participants can identify flow constraints and apply Lean methods to remove waste.

  • Eight wastes identified through direct observation of work and information flow
  • 5S workplace controls establish visual order, ownership and audit routines
  • Standard work defines sequence, takt time and expected inventory
  • Pull systems use Kanban signals, supermarkets and replenishment rules
  • SMED analysis separates internal and external changeover tasks
  • Poka-yoke methods prevent errors or make defects immediately visible

Participants can assess failure risks and define proportionate prevention and detection controls.

  • Process FMEA ranks failure modes using severity, occurrence and detection
  • Failure mode causes linked to controls, actions and accountable owners
  • Risk priority decisions use action priority rather than scores alone
  • Control selection distinguishes prevention controls from detection controls
  • Mistake-proofing concepts address human error, omission and incorrect sequence
  • FMEA updates reflect implemented actions, residual risk and verification results

Participants can build control systems that distinguish normal variation from actionable signals.

  • Control plans specify CTQs, methods, frequency, reactions and record retention
  • Statistical process control distinguishes common-cause from special-cause variation
  • Xbar-R, I-MR, p and u charts selected by data type
  • Control chart rules trigger investigation without tampering with stable processes
  • Reaction plans assign containment, diagnosis, correction and escalation actions
  • Layered process audits confirm standard work and control-plan adherence

Participants can plan, test and introduce improvements while managing operational adoption risks.

  • Pilot plans define test scope, success criteria, safeguards and review points
  • Kaizen events structure rapid improvement around a bounded process problem
  • Implementation plans sequence actions, dependencies, owners, dates and resources
  • Change impact assessments identify affected roles, procedures, systems and training
  • Communication plans address sponsor updates, frontline adoption and resistance
  • Benefits tracking compares validated results against chartered financial assumptions

Participants can apply DFSS concepts when process improvement requires a new design.

  • Design for Six Sigma connects customer requirements to design parameters
  • DMADV phases frame define, measure, analyse, design and verify work
  • Quality function deployment translates CTQs into technical design requirements
  • House of Quality compares customer priorities with engineering characteristics
  • Design FMEA anticipates failures before release or process transfer
  • Tolerance analysis balances functional requirements, variation and production cost

Participants can document analysis, validate benefits and communicate project decisions to leaders.

  • Minitab workflows document analyses, graphs, outputs and reproducible project evidence
  • Data visualisation selects displays suited to audience, decision and data type
  • Executive storyboards connect baseline, causes, solutions, controls and benefits
  • Financial validation separates hard savings, cost avoidance and revenue effects
  • Project portfolio reviews prioritise active work against strategic capacity
  • Black Belt coaching supports Green Belts through tollgates and analysis reviews

Participants can build, review and present a complete DMAIC project package for an operational problem.

  • Complete DMAIC project package integrates charter, evidence, solutions and controls
  • Baseline, analysis and improvement claims supported by traceable data
  • Review teams challenge assumptions, calculations, risks and implementation readiness
  • Financial benefits validated with operational owners and finance stakeholders
  • Control plan, FMEA and reaction plan aligned to residual risks
  • Present executive project story using concise visuals and decision requests

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. DMAIC project charter template
  2. Voice-of-customer and CTQ analysis
  3. Measurement system analysis checklist
  4. Root-cause and statistical analysis pack
  5. FMEA and control plan template
  6. Executive DMAIC project presentation

Who should attend

  • Continuous Improvement Manager — leads enterprise improvement portfolios and project governance
  • Quality Manager — needs advanced methods for recurring defects and control systems
  • Operations Manager — owns productivity, service, cost and process performance targets
  • Process Engineer — analyses process variation and designs technical improvements
  • Operational Excellence Specialist — facilitates cross-functional Lean Six Sigma projects
  • Business Analyst — turns operational data into evidence for improvement decisions

Skills and tools covered

DMAIC project leadershipStatistical process analysisRoot-cause verificationLean flow designRisk-based controlsBenefits validation

Tools and standards

DMAIC, SIPOC, FMEA, Minitab

How the course runs

Training methodology

The course uses short instructor briefings followed by case analysis, group exercises, statistical simulations and structured application planning. Participants interpret operational datasets, construct process maps, select tests, assess capability, develop FMEAs and build control plans. Cases reflect service, transaction, manufacturing and public-service processes. Facilitators use tollgate-style reviews to test the logic between problem definition, data, root causes, solutions and controls. Participants progressively assemble materials for a work-relevant DMAIC project and receive feedback on analysis quality and executive communication.

Requirements and prerequisites

Participants should have practical exposure to operational processes, quality improvement, project work or performance reporting. They should be comfortable reading tables and charts, calculating percentages and discussing process measures such as defects, cycle time and cost. Prior Lean Six Sigma Yellow Belt or Green Belt study is useful but not mandatory; the course revisits essential foundations before advancing to Black Belt methods. Advanced mathematics, programming, prior Minitab use and a formal quality qualification are not required. Participants will gain most value by bringing a current or potential improvement problem.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should understand how your operational process works and have some experience with quality, performance reporting, projects or improvement work. Green Belt study is useful but not essential; advanced statistics and formal quality credentials are not required.

Bring a current or potential process problem, along with any non-confidential information on defects, delays, costs, service levels or customer complaints. A laptop is recommended for working with datasets, templates and project materials.

It is designed for professionals who will lead significant improvement projects, rather than only participate in them. Quality, operations, process engineering, continuous improvement and business analysis roles are particularly suitable.

Green Belt training usually focuses on supporting or leading smaller improvement projects with core DMAIC tools. This Black Belt course adds advanced statistical analysis, project governance, financial validation, control-system design and coaching responsibilities.

The tools apply to transactional, service, technical and production processes where defects, delays, variation or rework affect results. Participants build materials around a relevant process problem and can use the templates for future projects.

Each day runs from 08:30 to 16:30, providing about 6.5 contact hours and approximately 65 contact hours overall. The first part establishes project definition, measurement and analysis; the later part focuses on improvement, controls, implementation and the capstone presentation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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