Master’s in Factory and Supply Chain Digital Twins
Online master's programme of 1,900 hours in factory and supply chain digital twins. 12 modules covering manufacturing system modelling, discrete-event simulation, plant connectivity, virtual commissioning, production planning, supply chain networks, resilience, AI analytics and sustainability, with ISO 23247, IEC 63278 (Asset Administration Shell) and IEC 62264 (ISA-95), and an assigned technical tutor.
Master’s in Factory and Supply Chain Digital Twins — studying from the United Kingdom
Data science, artificial intelligence and simulation are central to engineering practice across the United Kingdom, with strong hubs in London, Cambridge, Oxford, Bristol and Edinburgh. Universities, research institutes such as the Alan Turing Institute and industrial users in aerospace, automotive, energy and finance demand skills in modelling, machine learning and computational analysis. Engineering simulation tools are widely used for design validation and digital twins. Organisations seek professionals who can turn large datasets into engineering decisions while meeting data protection and governance requirements.
Fees for the United Kingdom: £8,280 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Which UK bodies recognise data and AI qualifications?
In the UK, the British Computer Society (BCS), the Royal Statistical Society (RSS) and the Institute of Analytics are relevant professional bodies, and the IET covers many engineering-related AI and data areas. SEIUM's qualifications are private and not accredited by these bodies. Check directly with BCS or RSS whether your study would be considered towards their membership or certification.
Do UK employers in data and AI value private online qualifications?
UK employers in finance, retail, health, technology and public sector analytics typically assess candidates on technical skills, portfolio work and experience with real data. A private qualification may help demonstrate specialist learning, but its value depends on each employer's judgement. Publishing projects and showing practical results will usually matter most.
Can I study this online from the UK alongside work, and what English level is required?
Yes, the programmes are fully online and can be studied from anywhere in the UK without a student visa. Teaching is in English, so you need a solid working level of technical English. An assigned technical tutor will support you. Check the programme hours against your working commitments and ask about instalment payments or employer sponsorship.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Design digital twins of manufacturing and logistics systems.
Model and simulate plants using discrete events.
Integrate the twin with real-time plant data.
Carry out virtual commissioning of automated installations.
Optimise production planning and the supply network.
Evaluate the resilience and sustainability of operations.
Plan the implementation of digital twins across the company.
Who it's for
- Digital twin engineers building models that stay synchronised with the plant.
- Process simulation engineers moving from offline studies to connected twins.
- Advanced production planners using simulation to sequence and schedule.
- Supply chain analysts who need what-if and stress-testing capabilities.
- Industrialisation engineers commissioning new lines and robotic cells.
- Industry 4.0 consultants leading twin programmes for manufacturers.
- Engineering or science graduates looking to enter the automotive sector.
What you'll work with
- ISO 23247 (digital twin framework for manufacturing)
- IEC 63278 (Asset Administration Shell for industrial applications)
- IEC 62264 (ISA-95 — enterprise-control system integration)
- IEC 62541 (OPC UA unified architecture)
- ISO 22301 (business continuity management)
- IEC 62443 (security for industrial automation and control systems)
- DIN SPEC 91345 (RAMI 4.0 reference architecture model)
- Siemens Plant Simulation
- AnyLogic
- FlexSim
- Siemens Process Simulate
- NVIDIA Omniverse
12 modules
1.1Digital model, digital shadow and digital twin1.2Twin maturity levels1.3Product, process and system twins1.4Industry 4.0 and RAMI 4.01.5Business value and use cases1.6ISO 23247 reference architecture1.7Asset Administration Shell1.8Integration and data challenges1.9Common mistakes in twin projects1.10Case study: digital maturity of an assembly plant
2.1Layouts and material flows2.2Lines, cells and job shops2.3Cycle times and bottlenecks2.4Queueing theory in production2.5Little's Law2.6Variability and buffers2.7OEE and losses2.8Lean and value stream mapping2.9Input data for modelling2.10Practical exercise: VSM of an assembly line
3.1Fundamentals of discrete simulation3.2Random number generation3.3Modelling with Siemens Plant Simulation3.4Modelling with FlexSim and AnyLogic3.5Resources, shifts and breakdowns3.6Intralogistics and AGVs3.7Calibration with real data3.8Experiments and replications3.9Statistical analysis of results3.10Lab: simulating a plant with AnyLogic
4.1Integration with MES and SCADA4.2OPC UA and information models4.3MQTT and the Unified Namespace4.4Process historians4.5Batch and part traceability4.6Production data quality4.7Twin-plant synchronisation4.8Event contextualisation4.9IEC 62443 security4.10Practical exercise: connecting a twin to real-time data
5.1Virtual commissioning5.23D models of machines and robots5.3PLC emulation5.4Robot and cell simulation5.5Validating control logic5.6Reducing start-up times5.7Process Simulate and NX MCD tools5.8Integration with electrical engineering5.9Change management5.10Lab: virtual commissioning of a cell
6.1Master planning and MRP6.2Sequencing and scheduling6.3Simulation-based optimisation6.4Heuristics and metaheuristics6.5Advanced planning and scheduling (APS)6.6Rescheduling after disruptions6.7Line balancing6.8Managing changeovers6.9Schedule adherence indicators6.10Practical exercise: optimal sequencing with simulation
7.1Supply chain structure7.2The bullwhip effect7.3Distribution network design7.4Multi-echelon inventory management7.5Transport and warehouses7.6Supply chain simulation with anyLogistix7.7Suppliers and lead times7.8End-to-end visibility7.9SCOR indicators7.10Lab: twin of a distribution network
8.1Disruptions and supply risks8.2What-if scenarios with twins8.3Supply chain stress testing8.4Supplier diversification8.5Strategic buffers8.6Business continuity under ISO 223018.7Supply chain due diligence8.8Early warnings8.9Response plans8.10Case study: disruption of a critical supplier
9.1Demand forecasting9.2Production anomaly detection9.3Simulation surrogate models9.4Reinforcement learning in planning9.5Prescriptive optimisation9.6Integrated predictive maintenance9.7Predictive quality9.8Explainability for planners9.9Continuous improvement cycle9.10Practical exercise: integrated forecasting in a logistics twin
10.1Plant energy consumption10.2Product carbon footprint10.3Scope 3 emissions10.4Simulating consumption and peaks10.5Circular economy and reverse logistics10.6Digital product passport10.7CSRD Directive and indicators10.8Energy optimisation with the twin10.9Sustainability reporting10.10Workshop: decarbonisation scenario for a plant
11.1Commercial twin platforms11.2Cloud and edge architecture11.3Interoperability and standards11.43D visualisation with Omniverse11.5Industrial data governance11.6Business case and return11.7Change management in operations11.8Phased roadmap11.9Vendors and integrators11.10Case study: multi-plant implementation plan
12.1Selecting the production or logistics system12.2Data gathering12.3Simulation model12.4Connection to real data12.5Scenarios and optimisation12.6Resilience analysis12.7Economic and environmental evaluation12.8Implementation plan12.9Written thesis report12.10Master's Thesis: defence before a panel
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 each intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Siemens Plant Simulation, AnyLogic, FlexSim, Siemens Process Simulate and NVIDIA Omniverse, in free, academic or evaluation versions. Testing is studied through real procedures: what-if simulation of production scenarios, virtual commissioning of robotic cells and supply chain resilience testing. Assessment is continuous for each module, plus the final Master's Thesis.
Standards covered: ISO 23247, IEC 63278 (Asset Administration Shell), IEC 62264 (ISA-95), IEC 62541 (OPC UA), ISO 22301, IEC 62443, DIN SPEC 91345 (RAMI 4.0).
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
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.
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 Siemens Plant Simulation, AnyLogic, FlexSim, Siemens Process Simulate and NVIDIA Omniverse, 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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