Master’s in Factory and Supply Chain Digital Twins Engineering — Discrete-Event Simulation, WIP/OEE and What-If
Online master's programme of 1,900 hours in factory and supply chain digital twin engineering, with discrete-event simulation, WIP/OEE and what-if analysis. 12 modules covering discrete-event simulation, factory physics, factory modelling, OEE, factory and supply chain digital twins, what-if analysis, planning and scheduling, intralogistics, AI and sustainability, with an assigned technical tutor.
Master’s in Factory and Supply Chain Digital Twins Engineering — Discrete-Event Simulation, WIP/OEE and What-If — studying from New Zealand
New Zealand's data and artificial intelligence sector is growing, with strong activity in Auckland, Wellington and Christchurch across fintech, agritech, health analytics and government data services. Universities including the University of Auckland, the University of Otago and Victoria University of Wellington run leading research in machine learning and computational modelling. Simulation and analytics skills are widely applied in agriculture, energy and environmental modelling, and employers include government agencies such as Stats NZ as well as technology firms and consultancies.
Fees for New Zealand: NZ$17,700 (New Zealand dollar), with instalments and employer sponsorship available.
Relevant bodies in New Zealand
Typical roles
Questions from New Zealand
Are data and AI engineering roles regulated in New Zealand, and how does that affect a private qualification?
Data and AI are not regulated professional titles in New Zealand, though privacy obligations fall under the Office of the Privacy Commissioner. Engineers who want chartered status can pursue registration through Engineering New Zealand. SEIUM's qualifications are private, not state degrees, and not accredited by a national body, so employers decide how much they value them.
Which New Zealand employers hire data, AI and simulation engineers?
Demand is found in banks and insurers, government agencies, healthcare, energy and utilities, technology firms, and consultancies. Employers usually assess portfolios, practical projects and technical interviews more than credentials. A private online programme can help build relevant skills, but it does not guarantee a position or a specific salary.
Can I complete this online from New Zealand in English while working?
Yes. The programmes are fully online and taught in English, so you can study from New Zealand without a student visa. You will have an assigned technical tutor and complete a final project. Specialist courses are 300 hours, diplomas 800 and master's 1,900. Fees can be paid in instalments, and some employers sponsor study, so ask yours early.
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
Build discrete-event simulation models of factories.
Analyse the relationship between WIP, throughput and cycle time.
Assess OEE and its losses through simulation.
Connect simulation models to plant data.
Model supply chains and their response to disruptions.
Design and analyse what-if studies of capacity and investment.
Propose operational improvements grounded in the digital twin.
Who it's for
- Process simulation engineers who want to move from one-off studies to live digital twins.
- Supply chain analysts modelling networks, inventories and disruptions.
- Digital twin engineers focusing on factories and logistics.
- Production planners looking for simulation-based scheduling tools.
- Logistics engineers designing warehouses, AMR fleets and line feeding.
- Operations consultants who need rigorous what-if analysis for their clients.
- Engineering or science graduates aiming to enter the automotive sector.
What you'll work with
- ISO 22400-2 (KPIs for manufacturing operations management)
- ISO 23247-2 (digital twin framework for manufacturing — reference architecture)
- IEC 62264-3 (enterprise-control integration — manufacturing operations activity models)
- ISO 28000 (supply chain security management)
- ISO 14083 (quantification of greenhouse gas emissions from transport chains)
- ISO 50001 (energy management systems)
- ISO 22301 (business continuity management)
- Siemens Plant Simulation
- AnyLogic
- anyLogistix
- FlexSim
- SimPy
12 modules · 1900 h
1.1Entities, resources, queues and events1.2Time-advance mechanism1.3Random number generation1.4Input distributions and fitting1.5Modelling manufacturing processes1.6Structure of a simulation project1.7Verification and validation1.8Warm-up period and replications1.9Confidence intervals for results1.10Case study: model of a machining cell
2.1Little's Law2.2Relationship between WIP, throughput and cycle time2.3Line performance curves2.4Variability and its propagation2.5Kingman's equation2.6Utilisation and queues2.7Process and transfer batches2.8CONWIP and pull systems2.9Factory Physics principles2.10Lab: WIP-throughput curve of a line
3.1Flow lines and job shops3.2Assembly and sub-assemblies3.3Internal transport and AGVs3.4Warehouses and supermarkets3.5Shifts, breaks and operators3.6Breakdowns and maintenance3.7Changeovers3.8Quality and rework3.9Hierarchical modelling and reuse3.10Practical exercise: model of an assembly plant in Plant Simulation
4.1Definition of OEE according to ISO 224004.2Availability, performance and quality4.3Losses and their classification4.4Line OEE versus machine OEE4.5TEEP and utilisation4.6OEE data capture4.7Simulating the impact on OEE4.8Bottleneck improvement4.9Relationship between OEE and WIP4.10Workshop: simulation of OEE improvements
5.1Connecting the model to MES data5.2Initialisation with the current state5.3Short-term simulation5.4Delivery forecasting5.5Rescheduling with the twin5.6Periodic state synchronisation5.7Continuous validation against reality5.8Software architecture of the twin5.9Interfaces for planners5.10Lab: plant twin fed with MES data
6.1Structure of supply networks6.2Nodes, flows and policies6.3The bullwhip effect and its causes6.4Multi-echelon inventory policies6.5Transport and lead times6.6Agent-based modelling of the chain6.7Hybrid factory and network simulation6.8System dynamics in supply6.9Data for network models6.10Practical exercise: simulation of the bullwhip effect
7.1End-to-end visibility7.2Integration with ERP, TMS and WMS7.3Supply chain control tower7.4Disruption simulation7.5Resilience and time to recover7.6Network capacity planning7.7Dynamic safety stock7.8Supplier risk7.9Service and cost indicators7.10Workshop: network twin with anyLogistix
8.1Design of simulation experiments8.2Demand scenarios8.3Capacity and investment scenarios8.4Sensitivity analysis8.5Statistical comparison of alternatives8.6Simulation-based optimisation8.7Metamodels to speed up scenarios8.8Visualisation of results8.9Communication to management8.10Practical exercise: what-if study of a capacity expansion
9.1Master planning and MRP9.2Finite-capacity scheduling9.3Sequencing heuristics9.4Dispatching rules9.5Optimisation-based scheduling9.6The twin and real-time scheduling9.7Managing rush orders9.8Measuring delivery performance9.9Integration with APS9.10Lab: comparison of dispatching rules
10.1Layout design10.2Automated storage systems10.3Order picking10.4AGV and AMR fleets10.5Line feeding and milk runs10.6Dock sizing10.7Warehouse simulation10.8Warehouse logistics indicators10.9Identification technologies10.10Case study: sizing an AMR fleet
11.1Machine learning with simulation data11.2Reinforcement learning for dispatching11.3Carbon footprint across the chain11.4Energy efficiency in the factory11.5Circular economy and reverse logistics11.6Standardisation of manufacturing twins11.7Change management11.8Business case11.9Scaling challenges11.10Case study: simulated carbon footprint of a network
12.1Definition of the factory-supply chain system12.2Data collection and analysis12.3Conceptual simulation model12.4Model implementation12.5Validation with real data12.6What-if capacity scenarios12.7Recommendations and assessment12.8Conclusions and operational recommendations12.9Master's 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 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 Siemens Plant Simulation, AnyLogic, anyLogistix, FlexSim and SimPy, in free, academic or evaluation versions. Practical work is based on real procedures: discrete-event simulation of a plant, what-if analysis of capacity and WIP, and a supply network twin facing disruptions. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 22400-2, ISO 23247-2, IEC 62264-3, ISO 28000, ISO 14083, ISO 50001, ISO 22301.
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
- 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.
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, anyLogistix, FlexSim and SimPy, 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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