Apply now
Postgraduate DiplomaData, AI, Simulation & Analytics Applications open

Diploma in OEE/WIP Integration and Twin Orchestration

Online postgraduate diploma of 800 hours in OEE/WIP integration and digital twin orchestration: 8 modules covering manufacturing KPIs under ISO 22400-2, plant data acquisition, digital twin modelling with ISO 23247 and the Asset Administration Shell, real-time OEE and WIP, twin orchestration, analytics and industrial deployment, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
Enrol now View syllabus Ask an advisor
Virtual lab · try it before you enrol

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.

SIM · LIVE ●
Learning outcomes

What you will be able to do

01

Calculate OEE, WIP and standardised manufacturing KPIs correctly.

02

Design plant data acquisition using industrial standards.

03

Model digital twins using reference frameworks and the AAS.

04

Implement real-time calculation of indicators.

05

Orchestrate composite twins integrating simulation and live data.

06

Analyse losses and propose data-driven improvements.

07

Plan the industrial deployment of digital twin solutions.

Profile

Who it's for

  • Digital twin engineers building plant and line models.
  • MES engineers connecting production data to performance indicators.
  • Lean process engineers who want reliable, real-time OEE and WIP data.
  • Industry 4.0 architects designing integrated shop-floor platforms.
  • Automation engineers working with PLC, SCADA and OPC UA.
  • Industrial data analysts turning machine data into improvement actions.
  • Engineering or science graduates looking to move into the automotive sector.
Standards & tools

What you'll work with

  • ISO 22400-2:2014 (key performance indicators for manufacturing operations management)
  • ISO 23247-1:2021 (digital twin framework for manufacturing)
  • IEC 63278-1:2023 (Asset Administration Shell) (structure of the Asset Administration Shell for industrial applications)
  • IEC 62541 (OPC UA) (OPC Unified Architecture)
  • IEC 62264-1:2013 (ISA-95) (enterprise-control system integration)
  • ISO/IEC 30173:2023 (digital twin concepts and terminology)
  • IEC 62443-3-3:2013 (system security requirements for industrial automation and control systems)
  • ISO/IEC 20922:2016 (MQTT) (MQTT messaging protocol)
  • Eclipse BaSyx
  • Grafana
  • InfluxDB
  • Node-RED
  • Siemens Opcenter
Syllabus

8 modules

  1. 1.1OEE: availability, performance and quality
  2. 1.2KPIs of ISO 22400-2
  3. 1.3WIP, throughput and lead time
  4. 1.4Little's law on the shop floor
  5. 1.5The six big losses
  6. 1.6TEEP and utilisation
  7. 1.7Flow and lean indicators
  8. 1.8Common errors when calculating OEE
  9. 1.9Hierarchy of indicators
  10. 1.10Workshop: OEE calculation with data from a production line
  1. 2.1The ISA-95 pyramid and automation levels
  2. 2.2PLC, SCADA and MES
  3. 2.3OPC UA and information models
  4. 2.4MQTT and Sparkplug B
  5. 2.5Capturing machine states and stoppages
  6. 2.6Part and quality counters
  7. 2.7Edge computing on the shop floor
  8. 2.8Data quality and context
  9. 2.9Cybersecurity according to IEC 62443
  10. 2.10Lab: OPC UA server and state capture
  1. 3.1The ISO 23247 reference framework
  2. 3.2Concepts of ISO/IEC 30173
  3. 3.3Asset Administration Shell according to IEC 63278
  4. 3.4Submodels and semantics
  5. 3.5Machine, line and plant twins
  6. 3.6Asset data models
  7. 3.7Fidelity and granularity
  8. 3.8Synchronisation with the real state
  9. 3.9Twin lifecycle
  10. 3.10Practical: AAS of a machine with Eclipse BaSyx
  1. 4.1Streaming architecture for indicators
  2. 4.2Aggregation by shift and order
  3. 4.3Automatic classification of stoppages
  4. 4.4WIP tracking by station
  5. 4.5Time-series databases for indicators
  6. 4.6Dashboards in Grafana
  7. 4.7Alerts and incident escalation
  8. 4.8Reconciliation with ERP
  9. 4.9Validation of indicators
  10. 4.10Lab: real-time OEE with InfluxDB and Grafana
  1. 5.1Composite twin architectures
  2. 5.2Orchestration of services and events
  3. 5.3Integration of simulation and live data
  4. 5.4Communication between twins
  5. 5.5Digital twin platforms
  6. 5.6Twin version management
  7. 5.7Scalability across multiple plants
  8. 5.8Microservices and containers
  9. 5.9Industrial data governance
  10. 5.10Practical: orchestration of cell and line twins
  1. 6.1Root-cause analysis of losses
  2. 6.2Data-driven Pareto and Ishikawa diagrams
  3. 6.3Stoppage prediction with machine learning
  4. 6.4What-if simulation connected to the twin
  5. 6.5Line balancing and target WIP
  6. 6.6CONWIP and digital kanban
  7. 6.7Integrated predictive maintenance
  8. 6.8Daily performance meetings
  9. 6.9Continuous improvement culture
  10. 6.10Case study: reducing micro-stoppages on a packaging line
  1. 7.1Phased implementation strategy
  2. 7.2Integration with existing systems
  3. 7.3Change management on the shop floor
  4. 7.4Operator training
  5. 7.5OT network security
  6. 7.6Return on investment
  7. 7.7Scaling to other plants
  8. 7.8Maintenance of the solution
  9. 7.9Lessons learned from implementation
  10. 7.10Case study: deployment of digital twins in an automotive plant
  1. 8.1Plant diagnosis
  2. 8.2Data acquisition architecture
  3. 8.3Twin model with AAS
  4. 8.4Real-time OEE and WIP calculation
  5. 8.5Twin orchestration
  6. 8.6Loss analysis and improvements
  7. 8.7Deployment plan
  8. 8.8Assessment of the benefits achieved
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
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 Eclipse BaSyx, Grafana, InfluxDB, Node-RED and Siemens Opcenter, in free, academic or evaluation versions. Practical work is based on real procedures for real-time OEE calculation on production lines, asset modelling with the Asset Administration Shell, and orchestration of cell and line twins. Assessment is continuous by module, plus the final project.

Standards covered: ISO 22400-2:2014, ISO 23247-1:2021, IEC 63278-1:2023 (Asset Administration Shell), IEC 62541 (OPC UA), IEC 62264-1:2013 (ISA-95), ISO/IEC 30173:2023, IEC 62443-3-3:2013, ISO/IEC 20922:2016 (MQTT).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 22400-2, ISO 23247 and IEC 63278 (AAS) applied to real cases, not as a theoretical annex.
Industry software:Eclipse BaSyx, Grafana, InfluxDB, Node-RED and Siemens Opcenter, in free, academic or evaluation versions.
Final project:a real-time OEE and WIP solution with orchestrated digital twins for a production plant, reviewed by your tutor.
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.

Plant diagnosis
Diagnose the plant and design the data acquisition architecture.
AAS twin model
Model the plant assets as digital twins with the Asset Administration Shell.
Real-time indicators
Implement real-time OEE and WIP calculation with dashboards.
Orchestration and improvement
Orchestrate the twins and analyse losses to propose improvements.
Final project
Present the deployment plan and benefits assessment, reviewed by your tutor.
Career opportunities

Where this takes you

Digital twin engineerMES engineerLean process engineerIndustry 4.0 architectAutomation engineerIndustrial data analyst
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • An 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 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Eclipse BaSyx, Grafana, InfluxDB, Node-RED and Siemens Opcenter, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme 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.

Keep exploring

Related programmes

See all in Data, AI, Simulation & Analytics
Postgraduate Diploma2,000 $

Diploma in Time Series Models for Engineering (ARIMA→DL)

Online postgraduate diploma of 800 hours in time series models for engineering, from ARIMA to deep learning. 8 modules covering ARIMA and state-space models, machine learning…

800 hours · 6–8 monthsOnlineData, AI, Simulation & Analytics
Postgraduate Diploma2,000 $

Diploma in Anomaly and Attack Detection in Graphs

Online postgraduate diploma of 800 hours in anomaly and attack detection on graphs, with 8 modules covering anomalous behaviour in graphs, classical and deep learning detection…

6–8 monthsOnlineData, AI, Simulation & Analytics
Postgraduate Diploma1,220 $

Diploma in Vision-Assisted GD&T and Dimensional Control

Online postgraduate diploma of 800 hours in vision-assisted GD&T and dimensional inspection. 8 modules covering the GD&T language, machine vision for metrology, geometric tolerance evaluation, in-line…

800 hours · 6–8 monthsOnlineData, AI, Simulation & Analytics
Diploma in OEE/WIP Integration and Twin OrchestrationEnrol
Related programmes

More in Data, AI, Simulation & Analytics

Diploma in Geo-Temporal Predictive ModelsView programme →Diploma in Differential Privacy and Applied CryptoView programme →Diploma in Evaluation, Security and Observability in NLPView programme →Diploma in Multimodal Model Evaluation and RobustnessView programme →Diploma in Demand Models and Control StrategiesView programme →Diploma in 1D/3D Coupling and Co-Sim OrchestrationView programme →

All Data, AI, Simulation & Analytics programmes → · How to apply · Fees & financing · Scholarships

Scroll to Top
SEIUM – Institute of Advanced Engineering
Powered by  GDPR Cookie Compliance
Privacy Overview

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.