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Master'sRobotics, Automation & Control Applications open

Master’s in Lean Six Sigma for Automated Lines Engineering

Online master's programme of 1,900 hours in Lean Six Sigma engineering for automated lines. 12 modules covering lean manufacturing in automation, Six Sigma and DMAIC, the Measure, Analyse, Improve and Control phases, design of experiments, flow management, automated quality inspection, digital Lean Six Sigma and organisational deployment, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Apply the DMAIC method to automated lines.

02

Measure process capability and the reliability of measurement systems.

03

Analyse root causes using statistical tools.

04

Design experiments to optimise process parameters.

05

Implement lean tools such as SMED, poka-yoke and kanban.

06

Establish real-time statistical control systems.

07

Deploy continuous improvement programmes in industrial organisations.

Profile

Who it's for

  • Lean Six Sigma Black Belts.
  • Process engineers.
  • Continuous improvement leads.
  • Quality engineers.
  • Production engineers.
  • Operational excellence consultants.
  • Engineering or science graduates seeking to enter the automotive sector.
Standards & tools

What you'll work with

  • ISO 13053-1 (quantitative methods in process improvement: DMAIC methodology)
  • ISO 13053-2 (quantitative methods in process improvement: tools and techniques)
  • ISO 7870-2 (Shewhart control charts)
  • ISO 22514-2 (process capability and performance)
  • ISO 9001 (quality management systems)
  • IATF 16949 (automotive quality management system)
  • ISO 18404 (competencies for Lean and Six Sigma)
  • ISO 3534-1 (statistics vocabulary and symbols)
  • Minitab
  • JMP
  • Siemens Plant Simulation
  • Python with statsmodels
  • Microsoft Power BI
Syllabus

12 modules · 1900 h

  1. 1.1Principles of the Toyota Production System
  2. 1.2The seven wastes and their automated version
  3. 1.3Jidoka and autonomation
  4. 1.4Continuous flow and takt time
  5. 1.5Heijunka on automated lines
  6. 1.6Pull, kanban and supermarkets
  7. 1.7Current-state value stream map
  8. 1.8Future-state value stream map
  9. 1.9Lean automation versus rigid automation
  10. 1.10Workshop: VSM of an automated assembly line
  1. 2.1Variation, sigma and defects per million
  2. 2.2Roles: champion, black belt and green belt
  3. 2.3Project selection and project charter
  4. 2.4Voice of the customer and CTQs
  5. 2.5SIPOC diagrams of the automated process
  6. 2.6Stakeholder management
  7. 2.7The DMAIC roadmap
  8. 2.8Requirements of ISO 13053
  9. 2.9Calculating project benefits
  10. 2.10Case study: charter for a rejects project
  1. 3.1Data collection plan
  2. 3.2Data types and scales
  3. 3.3Measurement system analysis (R&R)
  4. 3.4Attributes and agreement studies
  5. 3.5Process capability Cp, Cpk, Pp and Ppk
  6. 3.6Non-normal data and transformations
  7. 3.7Measuring OEE on automated lines
  8. 3.8Automatic data capture from PLC and MES
  9. 3.9Process baseline
  10. 3.10Lab: R&R study of a vision system
  1. 4.1Descriptive statistics and exploratory charts
  2. 4.2Parametric hypothesis tests
  3. 4.3Non-parametric tests
  4. 4.4One- and two-way ANOVA
  5. 4.5Correlation and multiple regression
  6. 4.6Logistic regression for defects
  7. 4.7Multi-vari studies of sources of variation
  8. 4.8Ishikawa and the cause-and-effect matrix
  9. 4.9Validation of root causes
  10. 4.10Practical exercise: cause analysis of minor stoppages
  1. 5.1Full factorial experiments
  2. 5.2Fractional factorials and resolution
  3. 5.3Plackett-Burman screening designs
  4. 5.4Response surface and central composite design
  5. 5.5Taguchi robust designs
  6. 5.6Experiments on automated processes
  7. 5.7Residual and interaction analysis
  8. 5.8Optimisation of machine parameters
  9. 5.9Mixture experiments
  10. 5.10Lab: DoE of robotic welding parameters
  1. 6.1Generating and prioritising solutions
  2. 6.2Poka-yoke on automatic stations
  3. 6.3SMED and quick format changeovers
  4. 6.4Line and station balancing
  5. 6.5Reducing minor stoppages
  6. 6.6Simulating improvements before implementation
  7. 6.7Pilot trials and validation
  8. 6.8FMEA of the proposed solution
  9. 6.9Calculating actual benefits
  10. 6.10Workshop: SMED on a packaging machine
  1. 7.1Process control plan
  2. 7.2Control charts for variables
  3. 7.3Control charts for attributes
  4. 7.4EWMA and CUSUM charts
  5. 7.5Real-time SPC from the PLC
  6. 7.6Standard procedures and training
  7. 7.7Visual management and andon
  8. 7.8Project closure and handover
  9. 7.9Sustainability audits
  10. 7.10Practical exercise: automatic SPC on line data
  1. 8.1Theory of constraints and the bottleneck
  2. 8.2Buffers and station decoupling
  3. 8.3Little's law and WIP
  4. 8.4Analysis of knock-on stoppages
  5. 8.5Discrete-event simulation of lines
  6. 8.6Batch size and product changeovers
  7. 8.7Station reliability and line availability
  8. 8.8Improving the bottleneck station
  9. 8.9Internal logistics and line feeding
  10. 8.10Lab: buffer simulation on a line
  1. 9.1100% inspection versus sampling
  2. 9.2Machine vision systems in quality
  3. 9.3Inline measurement and integrated metrology
  4. 9.4Part traceability and genealogy
  5. 9.5Defect detection with machine learning
  6. 9.6False rejects and inspection system capability
  7. 9.7Management of non-conformities
  8. 9.8Cost of poor quality
  9. 9.9Integration with MES systems
  10. 9.10Case study: reducing false rejects in vision
  1. 10.1Industry 4.0 and continuous improvement
  2. 10.2Process data analytics
  3. 10.3Process mining in operations
  4. 10.4Performance dashboards
  5. 10.5Machine learning for defect prediction
  6. 10.6Process twins for improvement
  7. 10.7Lean in highly automated environments
  8. 10.8Digital kaizen and idea management
  9. 10.9Risks of over-automation
  10. 10.10Workshop: OEE dashboard with PLC data
  1. 11.1Lean Six Sigma deployment strategy
  2. 11.2Hoshin kanri and goal alignment
  3. 11.3Belt training
  4. 11.4Project portfolio management
  5. 11.5Leadership and improvement culture
  6. 11.6Change management
  7. 11.7Lean audits and maturity
  8. 11.8Integration with ISO 9001 and IATF 16949
  9. 11.9Recognition and sustainability
  10. 11.10Case study: deployment plan for a plant
  1. 12.1Selection of the automated line to be studied
  2. 12.2Project charter and scope
  3. 12.3Measurement and baseline
  4. 12.4Statistical analysis of causes
  5. 12.5Design and simulation of improvements
  6. 12.6Control plan
  7. 12.7Economic evaluation of the improvements
  8. 12.8Lessons learned and standardisation
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: you progress at your own pace within the intake, making it compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Minitab, JMP, Siemens Plant Simulation, Python with statsmodels and Microsoft Power BI, in free, academic or evaluation versions. Exercises are based on real procedures for DMAIC projects to reduce rejects on an automated line, design of experiments on machine parameters and bottleneck and buffer analysis. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 13053-1, ISO 13053-2, ISO 7870-2, ISO 22514-2, ISO 9001, IATF 16949, ISO 18404, ISO 3534-1.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 13053-1, ISO 13053-2 and ISO 7870-2 applied to real cases, not as a theoretical annex.
Industry software:Minitab, JMP, Siemens Plant Simulation, Python with statsmodels and Microsoft Power BI, in free, academic or evaluation versions.
Master's Thesis with defence:A complete DMAIC project on a real automated line, with improvement simulation, control plan and economic evaluation, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Define the project
Select the automated line and draw up the project charter and scope.
Measure the baseline
Measure the process and establish its baseline.
Analyse the causes
Carry out the statistical analysis of root causes.
Improve and control
Design and simulate improvements and define the control plan with its economic evaluation.
Master's Thesis
Present the lessons learned and standardisation in a thesis, defended before a panel.
Career opportunities

Where this takes you

Lean Six Sigma Black BeltProcess engineerContinuous improvement leadQuality engineerProduction engineerOperational excellence consultant
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Minitab, JMP, Siemens Plant Simulation, Python with statsmodels and Microsoft Power BI, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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