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Master’s in Digital Twins and Operations Analytics Engineering

Online master's programme of 1,900 hours in digital twin engineering and operations analytics. 12 modules covering digital twin architecture, data integration, physical, data-driven and hybrid modelling, operations KPIs, predictive and prescriptive analytics, platforms, visualisation, cybersecurity and implementation strategy, 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

Design digital twin architectures in line with reference standards.

02

Integrate operational data through industrial information models.

03

Develop physical, data-driven and hybrid models for digital twins.

04

Calculate and analyse key operational indicators.

05

Apply predictive and prescriptive analytics on the twin.

06

Implement twins on commercial and open technology platforms.

07

Plan the implementation and return on investment of digital twins.

Profile

Who it's for

  • Digital twin engineers looking to deepen their expertise in operations analytics.
  • Operations analysts who want to build and exploit digital twins.
  • IIoT architects designing data and model architectures for connected plants.
  • Simulation engineers moving towards live, data-synchronised models.
  • Industrial data scientists applying predictive and prescriptive analytics to operations.
  • Operational excellence managers seeking data-driven decision tools.
  • Engineering or science graduates aiming to enter manufacturing.
Standards & tools

What you'll work with

  • ISO 23247-1 (digital twin framework for manufacturing)
  • ISO 22400-2 (KPIs for manufacturing operations management)
  • IEC 62264-1 (enterprise-control system integration)
  • IEC 63278-1 (Asset Administration Shell for industrial applications)
  • IEC 62443-3-3 (industrial cybersecurity system requirements)
  • IEC 62541 (OPC Unified Architecture)
  • ISO/IEC 30173 (digital twin concepts and terminology)
  • Regulation (EU) 2023/2854 (Data Act)
  • Azure Digital Twins
  • AnyLogic
  • MATLAB Simulink
  • Eclipse Ditto
  • Grafana
Syllabus

12 modules · 1900 h

  1. 1.1Model, digital shadow and digital twin
  2. 1.2Digital twin maturity levels
  3. 1.3Reference architecture according to ISO 23247
  4. 1.4Components: physical entity, data, models and services
  5. 1.5Synchronisation and fidelity
  6. 1.6Product, process and system twins
  7. 1.7Relationship with IIoT and analytics
  8. 1.8Value cases in operations
  9. 1.9Common mistakes in digital twin projects
  10. 1.10Case study: defining the scope of an operations twin
  1. 2.1Sources: sensors, SCADA, MES and ERP
  2. 2.2OPC UA and MQTT protocols
  3. 2.3Information models and ontologies
  4. 2.4Asset Administration Shell
  5. 2.5DTDL definition language
  6. 2.6Time-series databases
  7. 2.7Contextualisation and asset graphs
  8. 2.8Data quality and latency
  9. 2.9State synchronisation
  10. 2.10Lab: asset model with DTDL
  1. 3.1First-principles physical models
  2. 3.2Data-driven models
  3. 3.3Hybrid physics-statistical models
  4. 3.4Discrete-event simulation
  5. 3.5Reduced-order models
  6. 3.6Calibration with operating data
  7. 3.7Data assimilation
  8. 3.8Estimation of unmeasured states
  9. 3.9Validation and fidelity of the twin
  10. 3.10Practical exercise: state estimator with a Kalman filter
  1. 4.1Operational KPIs according to ISO 22400
  2. 4.2OEE, performance and quality
  3. 4.3Descriptive and diagnostic analytics
  4. 4.4Loss and bottleneck analysis
  5. 4.5Batch process analytics
  6. 4.6Operational process mining
  7. 4.7Statistical process control
  8. 4.8Operational dashboards
  9. 4.9A culture of data-driven decisions
  10. 4.10Workshop: OEE dashboard with plant data
  1. 5.1Demand and load forecasting
  2. 5.2Product quality prediction
  3. 5.3Operational anomaly detection
  4. 5.4Integrated predictive maintenance
  5. 5.5Cycle time forecasting
  6. 5.6Time-series models
  7. 5.7Explainability for operators
  8. 5.8Uncertainty of predictions
  9. 5.9Continuous model updating
  10. 5.10Lab: quality prediction integrated into the twin
  1. 6.1What-if scenarios on the twin
  2. 6.2Setpoint optimisation
  3. 6.3Planning and scheduling with twins
  4. 6.4Simulation-based optimisation
  5. 6.5Reinforcement learning on twins
  6. 6.6Recommendations to operators
  7. 6.7Closed loop versus open loop
  8. 6.8Risk assessment of recommendations
  9. 6.9Measuring the value generated
  10. 6.10Practical exercise: setpoint optimisation with the twin
  1. 7.1Azure Digital Twins
  2. 7.2AWS IoT TwinMaker
  3. 7.3Vendor industrial platforms
  4. 7.4Open source with Eclipse Ditto
  5. 7.5Edge-cloud architectures for twins
  6. 7.6Scalability and multi-site deployment
  7. 7.7Integration with enterprise systems
  8. 7.8Costs and licensing
  9. 7.9Platform selection
  10. 7.10Workshop: cloud twin of a production line
  1. 8.13D visualisation of assets
  2. 8.2glTF and OpenUSD formats
  3. 8.3Augmented reality on the plant floor
  4. 8.4Virtual reality for training
  5. 8.5Operator interfaces
  6. 8.6Alarms and notifications
  7. 8.7Remote collaboration on the twin
  8. 8.8Graphics performance in large scenes
  9. 8.9User-centred design
  10. 8.10Lab: 3D viewer with live data
  1. 9.1Twins in discrete manufacturing
  2. 9.2Process industry
  3. 9.3Buildings and facilities
  4. 9.4Logistics and warehouses
  5. 9.5Civil infrastructure twins
  6. 9.6Energy asset twins
  7. 9.7Healthcare and hospitals
  8. 9.8City digital twins
  9. 9.9Cross-domain lessons
  10. 9.10Case study: twin of a logistics centre
  1. 10.1Attack surface of the twin
  2. 10.2Industrial cybersecurity according to IEC 62443
  3. 10.3Access control and segmentation
  4. 10.4Data and model integrity
  5. 10.5Model governance
  6. 10.6Data ownership and the Data Act
  7. 10.7Digital twin life cycle
  8. 10.8Version management
  9. 10.9Accountability in automated decisions
  10. 10.10Practical exercise: risk analysis of a connected twin
  1. 11.1Business case for the digital twin
  2. 11.2Phased roadmap
  3. 11.3Pilots and scaling
  4. 11.4Organisation and roles
  5. 11.5Change management
  6. 11.6Success indicators
  7. 11.7Procurement and suppliers
  8. 11.8Long-term interoperability
  9. 11.9Return on investment
  10. 11.10Case study: digital twin roadmap for a company
  1. 12.1Selection of the operational system to be twinned
  2. 12.2Data and model architecture
  3. 12.3Development of the twin model
  4. 12.4Calibration and validation
  5. 12.5Predictive and prescriptive analytics
  6. 12.6Visualisation and services
  7. 12.7Value assessment
  8. 12.8Conclusions and scaling of the twin
  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. There are no fixed timetables: you progress at your own pace within the intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Azure Digital Twins, AnyLogic, MATLAB Simulink, Eclipse Ditto and Grafana, in free, academic or evaluation versions. Practical work is based on real procedures: a digital twin of a production line, what-if analytics for setpoint optimisation and real-time OEE dashboards. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 23247-1, ISO 22400-2, IEC 62264-1, IEC 63278-1, IEC 62443-3-3, IEC 62541, ISO/IEC 30173, Regulation (EU) 2023/2854.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 23247-1, ISO 22400-2 and IEC 62264-1 applied to real cases, not as a theoretical annex.
Industry software:Azure Digital Twins, AnyLogic, MATLAB Simulink, Eclipse Ditto and Grafana, in free, academic or evaluation versions.
Master's Thesis with defence:A calibrated and validated digital twin of a real operational system, with predictive and prescriptive analytics, 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.

Scope the twin
Define the operational system to be twinned and the value case it must deliver.
Model assets with DTDL
Build the asset and information model that connects plant data to the twin.
Estimate hidden states
Develop and calibrate the twin model, including a Kalman-filter state estimator.
Optimise setpoints
Run what-if scenarios and prescriptive optimisation on the twin, tracked on an OEE dashboard.
Master's Thesis
A complete digital twin with analytics, visualisation and value assessment, defended before a panel.
Career opportunities

Where this takes you

Digital twin engineerOperations analystIIoT architectSimulation engineerIndustrial data scientistOperational excellence manager
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's 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.

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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 Azure Digital Twins, AnyLogic, MATLAB Simulink, Eclipse Ditto and Grafana, 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 programme's 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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