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Master’s in Business Digital Twins Engineering

Online master's programme of 1,900 hours in business digital twin engineering. 12 modules covering digital twin concepts, process modelling and mining, business simulation, data integration, supply chain, manufacturing, financial and customer twins, predictive and prescriptive analytics, visualisation and 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

Distinguish the types and maturity levels of digital twins.

02

Model business processes with BPMN and process mining.

03

Develop discrete-event, agent-based and system dynamics simulation models.

04

Integrate real-time enterprise data with the models.

05

Apply predictive and prescriptive analytics to business scenarios.

06

Assess the economic value of simulated decisions.

07

Plan the deployment and governance of a digital twin at corporate scale.

Profile

Who it's for

  • Digital twin engineers moving from technical twins to business-level models.
  • Business simulation analysts working with discrete-event or agent-based models.
  • Operations consultants who use simulation to support client decisions.
  • Strategic planning managers who need scenario and Monte Carlo tools.
  • Industrial solutions architects integrating ERP, MES and IoT data.
  • Business process analysts adopting process mining.
  • Engineering or science graduates seeking to enter manufacturing.
Standards & tools

What you'll work with

  • ISO/IEC 30173:2023 (digital twin concepts and terminology)
  • ISO 23247-1:2021 (digital twin framework for manufacturing)
  • IEC 62264-1:2013 (enterprise-control system integration, ISA-95)
  • ISO/IEC 19510:2013 (BPMN 2.0)
  • IEC 63278-1:2023 (Asset Administration Shell)
  • DIN SPEC 91345:2016 (Reference Architecture Model Industrie 4.0, RAMI 4.0)
  • ISO 55001:2014 (asset management)
  • AnyLogic
  • Celonis
  • Siemens Plant Simulation
  • NVIDIA Omniverse
  • Power BI
Syllabus

12 modules · 1900 h

  1. 1.1Definition and levels of a digital twin
  2. 1.2Model, shadow and twin
  3. 1.3Product, process and organisation twins
  4. 1.4From technical twin to business twin
  5. 1.5Use cases and expected return
  6. 1.6The ISO/IEC 30173 framework
  7. 1.7RAMI 4.0 reference architectures
  8. 1.8Organisational maturity
  9. 1.9Common adoption mistakes
  10. 1.10Case study: use cases in a logistics company
  1. 2.1BPMN 2.0 notation
  2. 2.2Process mining
  3. 2.3Discovery from event logs
  4. 2.4Conformance and deviations
  5. 2.5Process indicators
  6. 2.6Decision modelling with DMN
  7. 2.7Variants and bottlenecks
  8. 2.8Integration with ERP systems
  9. 2.9Process-based continuous improvement
  10. 2.10Lab: process mining with Celonis
  1. 3.1Discrete-event simulation
  2. 3.2System dynamics
  3. 3.3Agent-based models
  4. 3.4Hybrid multi-method simulation
  5. 3.5Generating input distributions
  6. 3.6Model validation and verification
  7. 3.7Design of experiments
  8. 3.8Statistical analysis of results
  9. 3.9Simulation-based optimisation
  10. 3.10Practical exercise: warehouse model in AnyLogic
  1. 4.1ERP, MES and CRM data sources
  2. 4.2Levels of the ISA-95 model
  3. 4.3Asset Administration Shell
  4. 4.4IoT and real-time data
  5. 4.5Cloud platforms for digital twins
  6. 4.6Synchronisation between the real system and the model
  7. 4.7Data quality and latency
  8. 4.8Ontologies and knowledge graphs
  9. 4.9Integration security
  10. 4.10Lab: connecting real-time data to a model
  1. 5.1Modelling supply networks
  2. 5.2Integrated demand forecasting
  3. 5.3Inventory and service levels
  4. 5.4Disruption scenarios
  5. 5.5Logistics control towers
  6. 5.6Sales and operations planning
  7. 5.7Resilience and alternative sourcing
  8. 5.8Logistics chain emissions
  9. 5.9Supplier collaboration
  10. 5.10Case study: supply disruption scenario
  1. 6.1The ISO 23247 framework for manufacturing
  2. 6.2Production planning and sequencing
  3. 6.3Overall equipment effectiveness
  4. 6.4Capacity and line balancing
  5. 6.5Condition-based maintenance
  6. 6.6Plant energy consumption
  7. 6.7Workforce and shift twins
  8. 6.8Capacity investment scenarios
  9. 6.9Virtual commissioning
  10. 6.10Workshop: capacity analysis of a production line
  1. 7.1Predictive models in digital twins
  2. 7.2Embedded machine learning
  3. 7.3Mathematical optimisation of decisions
  4. 7.4Surrogate models and metamodels
  5. 7.5Reinforcement learning
  6. 7.6Sensitivity analysis
  7. 7.7Uncertainty and risk
  8. 7.8Generating automated recommendations
  9. 7.9Explainability for executives
  10. 7.10Lab: surrogate metamodel of a simulation
  1. 8.1Dynamic financial models
  2. 8.2Profit and loss twin
  3. 8.3Scenario planning
  4. 8.4Investment appraisal with simulation
  5. 8.5The Monte Carlo method
  6. 8.6Financial risk analysis
  7. 8.7Sales capacity planning
  8. 8.8Strategic dashboards
  9. 8.9Board decision-making
  10. 8.10Practical exercise: Monte Carlo simulation of a business plan
  1. 9.1Segmentation and synthetic profiles
  2. 9.2Simulating customer behaviour
  3. 9.3Elasticity and pricing strategies
  4. 9.4Channels and customer journeys
  5. 9.5Product diffusion in the market
  6. 9.6Competition and market response
  7. 9.7Customer data protection
  8. 9.8Testing virtual campaigns
  9. 9.9Validation with real data
  10. 9.10Case study: simulated product launch
  1. 10.1Interfaces for executives
  2. 10.23D visualisation and dashboards
  3. 10.3Augmented and virtual reality
  4. 10.4Data storytelling
  5. 10.5Real-time collaboration
  6. 10.6User experience design
  7. 10.7Accessibility and usability
  8. 10.8Omniverse and similar platforms
  9. 10.9Conversational interfaces with the twin
  10. 10.10Workshop: dashboard prototype for a digital twin
  1. 11.1The digital twin business case
  2. 11.2Implementation roadmap
  3. 11.3Model governance and versioning
  4. 11.4Life cycle and maintenance
  5. 11.5Organisation and roles
  6. 11.6Suppliers and platforms
  7. 11.7Cybersecurity and model ownership
  8. 11.8Measuring the value generated
  9. 11.9Scaling across the enterprise
  10. 11.10Case study: corporate deployment plan
  1. 12.1Defining the use case
  2. 12.2Process and data analysis
  3. 12.3Conceptual design of the twin
  4. 12.4Building the simulation model
  5. 12.5Data integration
  6. 12.6Validation against the real system
  7. 12.7Experiments and scenarios
  8. 12.8Business value assessment
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is delivered 100% online on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standards and applied exercises. The working software is AnyLogic, Celonis, Siemens Plant Simulation, NVIDIA Omniverse and Power BI, in free, academic or evaluation versions. Exercises are based on real procedures: building a supply chain twin with disruption scenarios, running a Monte Carlo simulation of an investment plan and applying process mining to ERP logs. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO/IEC 30173:2023, ISO 23247-1:2021, IEC 62264-1:2013 (ISA-95), ISO/IEC 19510:2013 (BPMN 2.0), IEC 63278-1:2023, DIN SPEC 91345:2016 (RAMI 4.0) and ISO 55001:2014.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO/IEC 30173, ISO 23247-1 and IEC 62264-1 (ISA-95) applied to real cases, not as a theoretical annex.
Industry software:AnyLogic, Celonis, Siemens Plant Simulation, NVIDIA Omniverse and Power BI, in free, academic or evaluation versions.
Master's Thesis with defence:a business digital twin for a real use case, with simulation model, data integration, validation and business value assessment, 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.

Process mining analysis
Discover and analyse a business process from ERP event logs with Celonis.
Warehouse simulation model
Build and validate a discrete-event warehouse model in AnyLogic.
Real-time data connection
Connect real-time enterprise data to the simulation model.
Monte Carlo business plan
Simulate a business plan with Monte Carlo to quantify financial risk.
Master's Thesis
A business digital twin for a real use case, validated against the real system and assessed for business value, defended before a panel.
Career opportunities

Where this takes you

Digital twin engineerBusiness simulation analystOperations consultantStrategic planning managerIndustrial solutions architectBusiness process analyst
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 AnyLogic, Celonis, Siemens Plant Simulation, NVIDIA Omniverse and 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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