Digital Twin - The new reality in product lifecycle management

A digital twin can be an incredibly effective tool for the product lifecycle management of complex assets, all the way from the first concept through design, manufacturing/ construction and commissioning, and all the way until the end of life and decommissioning of the asset.

Even the operation itself can be managed through or with the help of an advanced operational digital twin, making use of the digital thread and all the data that has been generated to that point.

Are you ready to take the step into the future of product management?

In this slideshare presentation, I provide a deep dive into the Digital Twin subject and explain the opportunities, success factors, elements to control etc.

Check out my blog for similar articles: https://www.hengsttechconsult.com/blog

1. Digital
Twin
THE NEW REALITYOF
PRODUCTLIFECYCLE
MANAGEMENT

2. Definitions
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3. Definitions
Digital Twin vs (predictive)Asset HealthManagement
Digital twins and predictive asset health management are closely related concepts, but they have some key differences:
•Digital Twin:
A digital twin is a virtual, interactive representation of a physical asset, process, or system. It uses real-time data
from sensors, historical data, and simulations to create an accurate, up-to-date replica of its physical counterpart.
Digital twins allow for real-time monitoring, simulation, and optimization of the asset or system.
Applied to e.g.the railway sector, a digital twin could cover the entire infrastructure –from stations, rolling stock,
switches and signals to supporting IT systems.
•(Predictive)Asset Health Management:
Predictive asset health management is a proactive maintenance approach that uses data analysis, machine
learning, and predictive modeling to identify potential issues or failures in assets before they occur. By analyzing
data from sensors, maintenance records, and other sources, predictive maintenance models can forecast when an
asset is likely to fail, enabling timely maintenance interventions
Digital twins and predictive asset health management can be combined to optimize the performance and maintenance
of trains and infrastructure. By combining digital twins and predictive asset health management, operators can enhance
the reliability, availability, and performance of their assets, ultimately improving safety, reducing costs, and increasing
customer satisfaction.
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4. Elements of a Digital Twin
•Physical equipment is the actual equipment that we are interested in creating a twin for.
•Twin Model -Comprises of hierarchy of systems, sub-assemblies, and components that describe the twinand its
characteristics enriched by asset, operational, historical, and context data.
•Knowledge -Data sources that feed the twin with operational settings, domain expertise, historical data, and
industry best practices.
•Analytics –Model gets empowered by physics-based models, statistical models, and machine learning/AI models to
help describe, predict and prescribe the behavior (current and future) of the asset, system, or process
Bi-directional link
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5. How can your product Benefit from a Digital Twin ?
The digital twin atthe heart of the product management lifecycle
How can a digital twin benefit the train development?
•Infrastructure managers, suppliers as well as remote repair crews
and station staff all stand to benefit from having a digital twin of a
complete railway infrastructure and rolling stock.
•With a digital twin infrastructure managers can formulate and
evaluate precise requirements for their new system and avoid
costly mistakes and ensure the right system is built in the first
place.
•Suppliers can use the Digital Twin as input to the detailed design,
using automation tools for code generation, test and verification,
further shortening project schedules and reducing costs.
•With access to a real-time 3D representation of the entire railway
infrastructure –maintenance, repairs and upgrades can be
performed faster.
•The proactive decisions possible with a digital twin, can prevent
safety hazards and costly mistakes while improving overall
efficiency.
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6. The benfits of a Digital Twin workflow
The digital twin captures all aspects of theProduct Development Process
The aim: capture reality to serve as the digital context for design and engineering, construction, and operations workflows.
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7. Types of
digital twins
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8. Different objectives of Digital Twins
You must chose well what you want to achieve, as the corresponding efforts vary significantly
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9. Coverages of Digital Twins
•Digital Twin Prototype [Design Phase]
This type of Digital Twin is basically a prototypeof a physical object before it is actually produced. It incorporates all the
data related to the design and production of an object. The information the DTP stores includes:
–its requirements;
–an annotated 3D model;
–a Bill of Materials;
–a Bill of Processes.
•Digital Twin Instance [Late Design& prototyping phase, evolving to product in service and until end of life]
An Instance represents a physical product that a Digital Twin replicates. It contains an annotated 3D model with general
dimensioning and tolerances; it describes the geometry of a physical product and its components. Apart from that, it has:
–a Bill of Materials;
–a Bill of Processes;
–a long record of IoT sensor readouts
–a Service Record;
–The Operational States.
•Digital Twin Aggregate(s) on different levels (single customer, all customers)[product in service, until end of life]
This is a combination of all Digital Twin Instances. Unlike the DTI, a Digital Twin Aggregate is not an independent structure.
The Digital Twin Aggregate uses the data incorporated in the Digital Twin Instances to study the physical product and
identify and learn patterns, as well as make predictions.
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10. Digital Twin Prototype
As Delivered
Digital Twin Instance
Digital Twin Aggregate(s)
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11. Digital Twins
for different lifecycle stages
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12. Digital Twins evolving over the lifecycle
An optimized DT naturally evolvesfrom Design to Commissioning to an Operational Twin
(Image:CourtesyIBM)
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13. 1
3
Digital Twin
for Concept and Engineering
Model-based development
to enable early-stage, full system
performance assessment
When a digital thread is integrated into the digital twin
process, stakeholders can trace design changes directly
from a customer to the engineering team and onto the
production floor. Using the digital thread to trace a line
among more than individual pieces of information, and
insteadusing it to improve a digital twin enables an
engineering team to get real-time customer input and a
corresponding model to trace how that input will affect the
design of a product… and how modifications will affect the
product in cost, performance, availability etc.
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14. 1. Ideation and Conceptual Design
•Virtual Prototyping: A digital twin enables rapid creation and iteration of virtual prototypes, allowing designers to explore and
evaluate multiple concepts without the need for physical models.
•Stakeholder Collaboration: Early-stage visualizations and simulations can be shared with stakeholders to gather feedback and
align on the vision. This enhances communication and reduces the risk of misunderstandings.
2. Feasibility Studies and Analysis
•Scenario Simulation: The digital twin allows for the simulation of various scenarios to assess the feasibility of different concepts.
This includes evaluating performance, cost, and potential risks.
•Integration with Simulation Tools: By integrating with simulation tools (e.g., computational fluid dynamics, finite element
analysis), the digital twin provides insights into the behavior and performance of the concept under different conditions.
3. Requirements Definition
•Requirements Management: The digital twin can be linked with requirements management tools to ensure that all design
concepts meet specified requirements and constraints.
•Traceability: Changes and decisions made during the concept phase are documented within the digital twin, providing traceability
and a clear rationale for design choices.
Digital Twin for Concept and Design Phase
Concept Phase
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15. 1. Detailed Design and Development
•3D Design Integration: The digital twin integrates seamlessly with 3D design tools (e.g., CAD software) to create detailed models of the
product. This ensures that the virtual representation is always up-to-date with the latest design changes.
•Multi-disciplinary Collaboration: The digital twin facilitates collaboration between different engineering disciplines (e.g., mechanical,
electrical, software) by providing a unified platform for sharing and integrating their designs.
2. Simulation and Testing
•Virtual Testing: Engineers can perform virtual testing and validation of the product using the digital twin, reducing the need for
physical testing. This includes stress tests, thermal analysis, and other performance evaluations.
•Single source of truth: The digital twin integrates with the PLM system managing the entire lifecycle of a product from inception to
disposal to ensure that all data, including test results and design iterations, are stored and managed effectively
3. Design Optimization
•Real-time Feedback: The digital twin provides real-time feedback on design changes, enabling continuous optimization and
refinement of the product.
•Predictive Analytics: Using data from previous projects and simulations, the digital twin can predict potential issues and suggest
improvements, leading to a more robust and optimized design.
4. Documentation and Compliance
•Automatic Documentation: The digital twin, especially when paired with generative AI, can generate detailed documentation
automatically, ensuring that all design changes and test results are recorded accurately. Automatic checks for compliance included.
Digital Twin for Concept and Design Phase
Engineering Phase
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16. 1. PLM (Product Lifecycle Management)
•Data Integration: The digital twin is integrated with PLM systems to provide a single source of truth for all product-
related data. This includes design files, test results, and documentation.
•Lifecycle Management: The digital twin supports the entire product lifecycle, from concept through to
decommissioning, by providing continuous data updates and insights.
2. 3D Design Tools
•Model Synchronization: The digital twin synchronizes with 3D design tools to ensure that the virtual model
accurately reflects the current design.
•Collaborative Design: Designers and engineers can work on the same model concurrently, with changes updated in
real-time within the digital twin.
3. Simulation Tools
•The digital twin integrates with various simulation tools to perform comprehensive analyses, including structural,
thermal, and fluid dynamics simulations.
•Real-time Data Feedback: Results from simulations are fed back into the digital twin, providing real-time insights
and enabling iterative improvements.
Digital Twin for Concept and Design Phase
Connection to other IT tools used
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17. •Improved Collaboration: Enhanced communication and collaboration across all stakeholders and
disciplines.
•Faster Time-to-Market: Accelerated design and development processes through virtual prototyping
and testing.
•Cost Savings: Reduced need for physical prototypes and tests, leading to lower development costs.
•Enhanced Quality: Continuous monitoring and optimization lead to higher quality and more reliable
products as outcome of the Design
•Lifecycle Management: Effective management of the product lifecycle, ensuring that the product
evolves with changing requirements and conditions.
•Automatic Documentation: The digital twin can generate detailed documentation automatically,
ensuring that all design changes and test results are recorded accurately.
•Regulatory Compliance: By integrating with compliance management tools, the digital twin ensures
that the design meets all relevant standards and regulations.
Digital Twin for Concept and Design Phase
Benefits of Using a Digital Twin in Design
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18. •In signaling design automation projects, we start by developing a digital twin of the existing railway system, future systemsand
conceptual systems. The digital twin is developed using formal methods that utilize automated simulation and can test and
validate every step of the rail control project.
•When creating a digital twin of the rail control system, the first step is to specify the requirements for the function and safety of
the system. These requirements are then used to define the design specification for the implementation.
•An Object Model is defined, serving as a common interface for the test, safety and design specifications, so that these can be
developed independently. The digital twin is used in an iterative process to validate and refine the requirements.
•To ensure that the specifications for the system are high-quality, they can be validated through simulation and formal
verification. This is done with formal methods and the digital twin of the existing and future system.
•By using Signaling Design Automation, a set of automation tools and processes based on formal methods can be used to develop
software for rail control systems. Code generation tools then convert your design into software code to be compiled, configured
and installed on the computing platform of the rail control system.
Digital Twin for signaling systems development
Example: Using a Digital Twin for railway signaling systems development through SDA
•Automate the development process
•Efficiently handle change requests and upgrades after
commissioning
•Validate that the delivered system meets requirements and
expectations
•Automatically prove safety
•Procure the best solution and service for the best price
•Simplify maintenance and upgrades after commissioning
•Minimize risk for project delays
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19. 1
9
Digital Twin
for Construction
and Commissioning
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20. A digital twin can be an incredibly effective tool during the construction and commissioning phases by providing a
virtual representation that helps manage and optimize these processes. The digital twin, in combination with IoT and
general Digitalization (e.g.BIM), reduces the go-to-market time and minimizesdesign risk, if combined with the right
digital tools to manage building construction and equipment commissioning.
1. Design Validation and Optimization
2. Construction Planning and Management
3. Quality Control and Compliance
4. Commissioning and Handover
5. Data Collection and Analysis
6. Stakeholder Collaboration
Summary:
A digital twin utilizing IoT Sensor data and Digitalization can create value along the entire lifecycle of a building.
It can significantly enhance the efficiency, quality, and safety of the construction and commissioning phases, leading
to a better, safer and more sustainable technical product.
Digital Twin for Construction and Commissioning
Overview
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21. A digital twin can be an incredibly effective tool during the construction and commissioning phases by providing a virtual
representation that helps manage and optimize these processes. As such, it can significantly enhance the efficiency, quality,and safety
of the construction and commissioning phases, leading to a better technical product.
1. Design Validation and Optimization
•Simulation and Analysis: Before actual construction begins, the digital twin can simulate different design scenarios to identify potential issues and
optimize the design for performance, cost, and safety.
•Virtual Prototyping: Engineers can use the digital twin to create virtual prototypes, reducing the need for physical prototypes, which can be time-
consuming and expensive.
2. Construction Planning and Management
•Construction Sequencing: The digital twin can help plan the sequence of construction activities to ensure efficient use of resources and time. It can
simulate the construction process to identify potential bottlenecks or clashes.
•Resource Allocation: By integrating the digital twin with project management tools, project managers can allocate resources more effectively andensure
that the right materials and equipment are available when needed.
•Progress Monitoring: The digital twin can be updated in real-time to reflect the actual progress of construction, allowing for better monitoring and
management. Any deviations from the plan can be quickly identified and addressed.
3. Quality Control and Compliance
•Standards and Regulations: The digital twin can store and manage information about standards and regulations, ensuring that all aspects of the
construction comply with relevant requirements.
•Inspection and Testing: Virtual inspections and tests can be conducted using the digital twin to identify defects or non-conformities before they become
issues. This helps in maintaining high quality and reducing rework.
Digital Twin for Construction and Commissioning (slide 1 of 2)
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22. 4. Commissioning and Handover
•System Integration Testing: The digital twin allows for the integration and testing of various subsystems in a virtual environment
before physical commissioning. This ensures that all systems work together seamlessly.
•Operational Readiness: Before handing over the product, the digital twin can be used to simulate operational scenarios to ensure
that the product is ready for use. This includes testing under different conditions and stress scenarios.
•Training and Documentation: The digital twin can serve as a training tool for operators and maintenance personnel, providing
them with a realistic environment to learn how to operate and maintain the product. It can also be used to generate
comprehensive documentation for the product.
5. Data Collection and Analysis
•Real-Time Data Integration: During construction and commissioning, real-time data from sensors and other sources can be fed into
the digital twin to provide an accurate and up-to-date representation of the product.
•Performance Monitoring: Continuous monitoring and analysis of data through the digital twin help in assessing the performance
of various components and systems. Any deviations from expected performance can be detected early, allowing for timely
interventions.
6. Stakeholder Collaboration
•Enhanced Communication: The digital twin provides a single source of truth that all stakeholders can access, enhancing
communication and collaboration. It allows for better coordination between different teams and disciplines.
•Stakeholder Engagement: Visualizations and simulations provided by the digital twin can help in engaging stakeholders, including
clients, regulatory bodies, and other partners, by providing a clear understanding of the project status and future plans.
Digital Twin for Construction and Commissioning (slide 2 of 2)
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23. 2
3
Digital Twin
for Operations
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24. Operational Digital Twin
Improvements through a perpetual cycle between DT and physical product
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25. An operational digital twin for a railwayoperator would focus on optimizing the day-to-day operations of the railway
network, including trainscheduling, capacity management, and handling of technical failures or
incidents/disturbances.
Here's how such an operational digital twin (Status / Operational / Simulations Twin) for railwaycould look like:
1.Real-time data integration
2.Trainscheduling, operation and capacity management
3.Incident and failure management
4.Predictive maintenance
5.Performance analysis and optimization
6.Asset Management
By integrating real-time data, simulations, and optimization algorithms, an operational digital twin can provide a
powerful tool for railoperators to manage their networks more efficiently, minimize disruptions, and improve overall
performance. The digital twin can support data-driven decision-making at all levels, from real-time dispatching to
fluent in-service operational changes that have been simulated before, all the way to long-term planning.
This ultimately leads to a safer, more reliable, and more efficient railwaysystem.
The following slides explain each of the bullet points in more detail.
Operational Digital Twin
Optimizing the day-to-day operations of a railway operator
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26. Operational Digital Twin: How to achieve operational excellence
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27. Data from real-time asset monitoring technologies can be analyzed for trends and incorporated into simulations.
Likewise, IoT sensors embedded in the railwaysystem or throughout supply chains can feed operational data directly
into simulations, which enables operators to monitor continuously and in real-time. Thusrailwayoperations can be
run more effectively.
The available data allows to simulate & validate decisions on the DT while having the physical asset in operation.
•Real-time data integration:
The digital twin would integrate real-time data from various sources, such as:
-Train positioning and speed data from GPS and signaling systems
-Passenger count data from ticketing systems and sensors
-Infrastructure status data from sensors monitoring tracks, switches, and signaling equipment
-Weather data from meteorological services
-Maintenance data from asset management systems
•Anomaly Detection:
By continuously comparing real-time data with the digital twin, operators can detect anomalies (e.g., deviations from
normal operation) and respond promptly.
Operational Digital Twin
Real-time data integration for monitoring and control
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28. Trainscheduling and capacity management:
The digital twin would use the real-time data to continuously monitor and optimize train schedules and transport
capacity, by supporting the OCC with data, simulations capability and decision support:
-Providing a comprehensive, real-time view of Trainpositions, speeds, and track status, allowing traffic controllers to
monitor and manage train movements more effectively.
-Simulating and comparing different scheduling scenarios to maximize network capacity and minimize delays
-Adjusting schedules in real-time based on actual train positions, passenger demand, and infrastructure constraints
-Facilitating the dynamic adjustment of traffic / speed / headway based on real-time conditions
-Identifying potential bottlenecks or conflicts in the network before they occur, proposing solutions and ensuring
smooth operations
-Providing data-driven recommendations and decision support to dispatchers and controllers for optimal train
routing and scheduling and managing traffic flow efficiently
Operational Digital Twin
Trainscheduling and capacity management
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29. Incident and failure management:
In case of technical failures or incidences, the digital twin would help in minimizing their impact through enhanced
Incident Detection and Response, more efficient coordination and communication and advanced Post-Incident
Analysis:
-Detecting and localizing failures or incidents in real-time through continuous monitoring of sensor data and other
inputs, and analysis of fault patterns
-Assessing the impact of the disturbance on the overall network using immediate simulations capability
-Evaluating alternative scenarios for rerouting Trains, varying headways or speeds, adjusting schedules, or
deploying maintenance crews
-Providing decision support to dispatchers and maintenance teams for optimal incident response
-Allowing for the simulation of different incident scenarios to understand potential impacts, helping to develop
preventive measures and to prepare effective response strategies.
-Communicating updated schedules and expected delays to passengers and stakeholders
-Using data from the digital twin, it allows to perform detailed root cause analyses of incidents, much faster
Operational Digital Twin
Incident and Failure Management
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30. Condition Monitoring and Preventive Maintenance
The digital twin collects data from various sensors placed on trains and infrastructure, monitoring the condition of
components in real-time. This data helps in predicting potential failures before they occur.
The digital twin would integrate predictive maintenance capabilities to minimize disruptions:
-Analyzing sensor data and maintenance records to identify potential failures before they occur
-Learning from experience, supported by AI and ML, which allows for optimized diagnosis and trouble shooting
Maintenance Scheduling
Integration with Computerized Maintenance Management Systems (CMMS) allows for predictive maintenance
scheduling. Maintenance activities can be planned based on the actual condition of assets rather than on fixed
schedules, reducing downtime and maintenance costs
-Optimizing maintenance schedules based on asset condition and criticality
-Simulating the impact of different maintenance strategies on network performance and costs
-Providing decision support for prioritizing and scheduling maintenance activities
Operational Digital Twin
Predictive Maintenance
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31. Predictive Analytics and Alerts:
The digital twin continuously analyzes the railwaynetwork's performance and identify optimization opportunities. By
aggregating data from various sources -including sensors, maintenance records, and operational logs -into a common
data lake acting as Centralized Data Repository and single source of truth.
The DT analyzes operational data to optimize Trainschedules, energy consumption, and resource allocation. It utilizes
machine learning algorithms to predict potential failures, maintenance needs, and operational inefficiencies.
This helps in continuously improving the efficiency of the railwaysystem.
-Calculating key performance indicators (KPIs) such as punctuality, reliability, and capacity utilization
-Continuously assesses the condition of infrastructure and Trains to predict potential failures.
-Comparing actual performance against benchmarks and targets
-Identifying root causes of performance issues and proposing corrective actions
-Generates automated alerts and recommendations for maintenance, speed adjustments, or other interventions
based on predictive analytics
Operational Digital Twin
Performance analysis and optimization
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32. Simulation of Operational Scenarios:
Operators can use the digital twin to simulate various operational scenarios, such as changes in train schedules or the
introduction of new rolling stock, to understand their impact and optimize operations:
-Allowing operators to simulate different operational scenarios, such as rerouting Trains or adjusting schedules, to
assess impacts before implementation and optimize decision-making processes.
-Simulating the impact of different optimization measures on network performance and costs
-Allowing to run in parallel several operational models in simulation, which are fed by real-life data from the
physical assets, and to compare the outcome
-Supporting long-term planning and investment decisions based on performance data and simulations
-Improving the ability to reach or exceed performance targets, ultimately leading to better profitabiity
-Enhancing Emergency Preparedness by simulating emergency situations to prepare and train operators for real-
world incidents.
-Facilitating Anomaly Detection: Identifies anomalies and unusual patterns in data that could indicate underlying
issues, enabling proactive intervention.
Operational Digital Twin
Simulation of Operational Scenarios
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33. 1. Operations Control Center (OCC)
•Real-Time Data Feed: The digital twin receives and processes real-time data from the OCC, ensuring accurate
and up-to-date monitoring of railway operations.
•Align engineering data with reality geospatial, IoT, and other operational datato create a Single View Of The
Truth, ideally using immersive visualization, adapted to the different operators’ specialties and needs.
•Decision Support: The OCC uses the digital twin for decision support, leveraging its real-time insights and
predictive analytics to make informed operational decisions.
2. Computerized Maintenance Management System (CMMS)
•Predictive Maintenance Integration: The digital twin provides condition data to the CMMS, enabling predictive
maintenance strategies. Maintenance tasks are generated based on the actual condition of assets, improving
reliability and efficiency.
•Maintenance History and Analytics: The digital twin and CMMS work together to maintain a comprehensive
history of all maintenance activities, which can be analyzed to identify trends and improve maintenance
practices.
Operational Digital Twin
Connecting with other tools / systems (1 of 2)
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34. 3. Asset Management Systems
•Comprehensive Asset Data: The digital twin ensures that asset data is continuously updated and accurate. It provides
a detailed view of the asset's condition, performance, and lifecycle status.
•Strategic Asset Planning: Integration with asset management systems allows for strategic planning of asset utilization,
upgrades, and replacements based on real-time and historical data.
4. Maintenance Management Tools
•Work Order Management: Maintenance management tools can receive work orders generated by the digital twin
based on predictive analytics and condition monitoring.
•Resource Allocation: These tools use data from the digital twin to allocate resources (personnel, parts, tools) more
efficiently, ensuring that maintenance tasks are completed effectively and on time.
5. Analytics and Reporting Tools
•Data Analysis: The digital twin's data is analyzed using advanced analytics tools to identify patterns, trends, and
insights that can improve operations. It gains insights through artificial intelligence (AI) and machine learning (ML)
•Visualize and track changes, including changes in real-world conditions from IoT-connected devices
•Reporting: Automated reports can be generated to provide stakeholders with up-to-date information on
performance, maintenance activities, and other key metrics.
Operational Digital Twin
Connecting with other tools / systems (2 of 2)
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35. Operational Digital Twin
Value Added Functions for Specialized Operators (1 of 2)
1. TrainDispatchers [Responsible for train scheduling, route management, and ensuring on-time operations]
•Enhanced Scheduling: Real-time data and predictive analytics help in optimizing train schedules and minimizing delays.
•Dynamic Routing: Ability to dynamically reroute trains based on real-time conditions and predictive insights.
•Real-Time Alerts: Generates automated alerts for deviations from the planned schedule or operational anomalies, enabling quick responses
•Incident Response: Better preparedness and quicker response times to incidents with real-time data and simulation capabilities.
2. Signal Operators [Manage signal systems to ensure safe and efficient train movement]
•Improved Signal Management: Real-time visibility into Trainpositions and conditions ensures better management of signal systems.
•Predictive Maintenance: Alerts for potential signal failures allow proactive maintenance and reduce downtime.
3. Infrastructure Managers [Oversee the condition and maintenance of tracks, bridges, tunnels, and other infrastructure]
•Condition Monitoring: Continuous monitoring of guideway and infrastructure components, predicting wear & tear, scheduling timelymaintenance.
•Resource Optimization: Better allocation of resources for maintenance activities based on real-time and predictive data.
4. Safety Officers [Ensure safety protocols are followed and manage emergency response efforts]
•Risk Assessment: Real-time risk assessments and alerts for potential safety hazards.
•Emergency Simulations: Conduct and evaluate emergency drills using simulated scenarios in the digital twin.
5. Communication Officers [Handle communication with Trains and passengers, providing updates and information]
•Passenger Information: Real-time updates and accurate information dissemination to passengers through integrated communication systems.
•Incident Communication: Effective coordination and communication during incidents or emergencies.
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36. Operational Digital Twin
Value Added Functions for Specialized Operators (2 of 2)
6. Energy Managers [Oversee the condition and maintenance of tracks, bridges, tunnels, and other infrastructure]
•Energy Optimization: Monitoring and optimizing energy consumption of trains and infrastructure.
•Sustainability Metrics: Tracking and reporting on sustainability metrics, such as carbon emissions and energy savings.
7. Maintenance Coordinators [Oversee the condition and maintenance of tracks, bridges, tunnels, and other infrastructure]
•Proactive Maintenance: Scheduling maintenance activities based on predictive analytics to prevent failures.
•Work Order Management: Integration with CMMS for efficient work order generation and tracking.
8. Traffic Controllers: [Monitor and control train traffic to ensure smooth and efficient operations]
•Live Traffic Visualization: comprehensive, real-time view of Trainpositions, speeds, track status, allowing to manage Trains more effectively
•Dynamic Routing: dynamic adjustment of Traintraffic based on real-time conditions, track availability, maintenance activities, or delays
•Incident Prediction: Uses predictive analytics to foresee potential incidents and suggests proactive measures to mitigate delays.
•Conflict Resolution: Identifies and resolves potential conflicts in train schedules and routes before they occur, ensuring smooth operations.
9. Incident Managers: [Respond to and manage incidents, ensuring minimal disruption and safety]
•Real-Time Incident Detection: Quickly identifies incidents through continuous monitoring of sensor data and other inputs.
•Incident Simulation: Allows for simulation of different incident scenarios to understand potential impacts and prepare effectiveresponse strategies.
•Incident Management: Provides tools for logging, tracking, and managing incidents, ensures all necessary actions are documented and coordinated.
10. Data Analysts: [Analyze operational data to identify trends, optimize performance, and support decision-making]
•Comprehensive Data Integration: Aggregated data from many different sources into a Centralized Data Repository, as Single Sourceof Truth
•Advanced Analytics and Insights: Enables the analysis of current and historical data to identify trends, patterns, and areas forimprovement.
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37. By intelligently integrating a digital twin with the standard operational tools and systems, it creates a
connected and intelligent railwayoperations ecosystem that enhances performance, safety, and reliability,
allows for better reactivity and planning capability, and radically improves decision-making processes.
•Enhanced Safety: Real-time monitoring and predictive maintenance reduce the risk of failures and accidents.
•Increased Efficiency: Optimization of operations and maintenance activities leads to better resource utilization
and reduced downtime.
•Cost Savings: Predictive maintenance and efficient resource allocation lower maintenance costs and extend
asset life.
•Improved Reliability: Continuous monitoring and quick response to anomalies ensure more reliable railway
operations.
•Better Decision-Making: Comprehensive data and advanced analytics provide valuable insights for informed
decision-making.
•Improved Visualization:Democratize and simplify previously siloed data into an intuitive global view of clearer
context for all stakeholders
Operational Digital Twin
Benefits of using an operational digital twin
August 2024DIGITAL TWIN – THE NEW NORMAL IN PRODUCT LIFECYCLE MANAGEMENT 37

38. Examples of Operational Digital Twins
Asset Lifecycle and Digital Twin Information Exchange
Source: ebook-digital-twins-rail-asset-lifecycle-en.pdf (bentley.com)
Digital twins provide opportunities to deliver improved
business outcomes across the entire rail and transit asset
lifecycle, and can help reshape how infrastructure is
planned, designed, built, and operated.
Digital workflows on existing & future networks help
engineers, designers, asset managers, inspectors, and
other specialists do their job better and faster.
As part of this transformation—made possible by
advances in geotechnical engineering, 3D modeling, 4D
planning and visualization, reality modeling, artificial
intelligence, and machine learning—digital twins today
provide an immersive and holistic
view of infrastructure and rolling stock assets above
ground and below ground.
For capital projects, digital twins help drive efficiency and
increase quality within multidiscipline and digitalized
workflows, enabling streamlined collaboration, maximum
productivity, and more informed decisions.
During operations, digital twins help optimize rail
operations and allow for predictive maintenance strategies
to reduce costs, improve safety and reliability.
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39. Examples of Operational Digital Twins
Managing Passenger flow and Customer Experience in stations
See video describing the complete project: https://youtu.be/aorQDMinJzc
ADIF and NTT Data have developed a
project to support the reconfiguration of
one of Madrid’s train stations into an
intermodal transportation hub.
A developed app allows passengers to
plan routes and measure station
occupancy in real time, get way time
estimates, etc. In addition, it facilitates
the recalculation of routes in case of
finding spaces with high occupancy, thus
improving the overall experience at the
station.
For the development of this project, NTT
Data has turned to a variety of
advanced technologies, including cloud
computing, high-resolution cameras,
advanced AI techniques (counting the
number of people in real time). In
addition, for precise indoor positioning,
digital twins have been developed
complemented by an advanced
processing engine and a network of
more than a hundred beacons
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40. Tendency: Digital Twins as OCC Core
DTs are becoming the core of Operational Control and Management
Digital twins are virtual replicas of physical
assets or systems, with a bi-directional
connection to the original assets. In the
context of operating control centers for
transportation, they offer a powerful tool for
managing operations and supporting
decision-making:
1. Real-time Monitoring and Control
(comprehensive visual representation of the
entire system, predictive maintenance
support, remote control of assets)
2. Scenario Planning and Testing
(Testing and simulating different operating
scenarios in reaction to incidents before
applying them, risk assessment, development
of contingency plans)
3. Optimization and Efficiency
(e.g. resource allocation, performance
analytics, efficiency improvements)
4. Training and Education
(e.g. emergency response training, practicing
of certain skills without risking damage)
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41. Tendency: Digital Twins as OCC Core
Digital twins, the airport operations control interface of the future
Information about everything that happens at an airport is
largely available in the more advanced control centers. However,
the various elements are collected / presented independently, so
it can be difficult to see the overall picture, and how all the
elements interact. It can also be hard to generate a historical
view to review how the airport previously handled disruption
caused by bad weather, for example.
SITA works on creating the operations control interface of the
future, using digital twins to bring together everything that’s
happening. It covers arriving and departing aircraft, the number
of passengers involved, queue wait times, escalator operations,
passenger satisfaction with restrooms, traffic flows at drop-off
and pick-up and more.
The result is improved decision-making, based on the holistic
view of the airport operations. As well as showing what’s
happening now, we can also select a moment in history and play
back exactly what happened in the past.
The next stage, which we’re working on now, is to build in a view
of what is going to happen. By feeding in flight information,
weather data and other operational information we can use the
digital twin to predict what will happen at the airport next.
Source: SITA | Digital twins, the airport operations control interface of the future
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42. Tendency: Multi-purpose digital twin
One multi-purpose digital twin
Using a multi-purpose digital twin, companies
can analyze multiple disciplines using the
same simulation model.
By using model identification approaches
developed by your team, OEMs and suppliers
can build the technical models of new
products more easily and in a way that is
much more cost-efficient than building from
scratch. This approach helps engineers
achieve a higher level of fidelity, lower
product development costs, increased quality,
and a better driving experience for the end-
user.
Engineering companies often have difficulties
building the models required for a full digital
twin in a way that is both time-and cost-
efficient. Supplier companies typically do not
have the required information in order to
build these models. The cost to OEMs to
build a model for every variation is
prohibitively high.
Specialized companies like the Siemens
SimcenterEngineering team can take any
product and turn it into a high-fidelity multi-
purpose digital twin, used by all stakeholders.

43. Reality: Siemens Mobility –RailigentX
A digital services customer portfolio for rail systems, to support the maintainer, operator and asset owner
Siemens Mobility’s
RailigentX is aiming at
providing a 100%
operational availability
so a value close to
railwayTT’sobjective of
99,5%.
It focuses on
Maintenance
intelligence
applications, Operations
Intelligence applications
and Asset lifecycle
intelligence, providing a
holistic view on the
whole system.
It consists of a suite of
applications creating a
holistic digital twin
nurturing specific
applications and data
services
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44. Reality: Airbus Skywise
One multi-purpose digital twin and analytics environment for aircraft
Skywise, the aviation data platform launched by Airbus in 2017,
provides the key digital infrastructure to the participants, as it
provides a singular access point to data analytics that combine
multiple sources into one secure cloud-based platform, including
work orders, spares consumption, components data,
aircraft/fleet configuration, onboard sensor data and flight
schedules.
The cloud computing infrastructure for the servers utilized by
Skywise are based in Ireland, and today contain a total of 15
petabytes, or 15 million total gigabytes of flight operational
data points about individual in-service Airbus aircraft parts,
systems, and engines
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45. Data Management
Aspects
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46. A digital twin comprises three main elements:
•Past data –historical performance data of individual systems making up the railwaysystem, overall processes involving
the system and its operation, and specific systems, with a focus on those elements which are considered most relevant
(safety-critical, cost-drivers, availability-drivers).
-> This is knowledge brought into the picture
•Present data –real-time data from equipment sensors, outputs from manufacturing platforms and systems, and
outputs from systems throughout the distribution chain. It can also include outputs from systems in other business
units, including customer service and purchasing.
-> This is the enhanced picture of the current reality of the system
•Future data –system predictions based on AI and machine learning, as well as additional inputs from operators, as
well as additional “forecasted” data from external sources that are relating to the operation of the digital twin. In the
case of the railwaySystem, this could be events in the region (affecting traffic around the system or ridership), weather
forecasts (=possible operational impacts), maintenance planning (=no-availabilities, limitations in RUL), etc
-> This is the outlook for the system, and allows to take into accountdifferent scenarios that can be simulated to
determine the best way to operate, depending on the circumstances that will finally happen
Basics of the Digital Twin
Bringing together different views in time
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47. 4
7Digital Twin implementation
It is essential to chose wisely what the Digital Twin shall do and represent, based on YOUR specific business objectives
Recommendations:
•Keep your digital Twin as Simple as Possible, but no Simpler than that (Gartner)
•A High Fidelity only model leaves out many operations due tot he investment needed to develop the model.
Selecting the Fidelity based on simulation needs provides an economical path to a more complete picture of the operation
•Evaluate well the IoT and Digital Twin Technologies, Services and Disciples you want to use (Implementation Architecture)
Marc Halpern in his book “Busting the Myth of Digital Twins and Planning Them Realistically” (2017) points out that the digital twin
doesn’t have to be a “high fidelity physics model” of the asset, butsays “Digital twins should provide you with a view into assets —based
on yourbusiness objectives.
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48. Creating and maintaining an effective digital twin over the lifecycle of a technical development program involves feeding it with relevant
data from various sources. This includes combining disparate engineering data created by diverse design tools into a federated
environment with no disruption to our current tools or processes.Managing this data efficiently is of utmost importance and ensuring
that it integrates seamlessly (even across different data architectures) to form a comprehensive, interconnected digital thread.
In this process, several aspects are of great importance:
•Data Integration and Interoperability
•Data Quality and Governance
•Data Storage and Retrieval
•Real-Time Data Processing
•Data Analytics and Insights
By effectively managing data across its lifecycle and ensuring seamless integration and interoperability, a digital twin can provide a
comprehensive and accurate representation of the technical product, enabling optimized performance, enhanced decision-making, and
continuous improvement. But the right design in line with business objectives is essential.
There is different philosophies for when to start with the digital twin. Ideally the twin grows over time, as described previously. Some DT
companies prefer to start with the DT at a later stage (e.g.commissioning) and instead provide all kinds of data interfaces to load all the
relevant data into the tool at once, in a “big bag” type approach.
Setting up the Digital Twin
Data Management for an effective Digital Thread
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49. Phased approach towards the Operational Digital Twin
Thedigitaltwinneedstobefedcontinuously,from different sources, so itcanrepresent your product
The idea is to digitalize all available, relevant data and workflows throughout design, construction, and in-service operations and to
transform the lifecycle-data into a federated environment for optimized operations, simulations and value-based asset management.
(Image:CourtesyFerrovial)
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50. To create a perfect digital thread, it is crucial to ensure seamless connectivity and integration of data across the
entire lifecycle of the technical development program.
•Continuous Data Flow: Establish continuous data flow mechanisms using APIs, ETL (Extract, Transform, Load)
processes, and integration platforms to ensure that data is consistently updated and flows seamlessly from
one phase to the next.
•Unified Data Model: Develop a unified data model that standardizes data structures and relationships across
all systems and tools, ensuring consistency and coherence in the digital thread.. When faced with different
data architectures needing to come together, decide on a common macro model to ensure compatibility.
•Lifecycle Traceability: Implement traceability mechanisms that link data from different phases, ensuring that
every piece of information is connected and can be traced back to its origin.
•Collaboration and Communication: Foster collaboration and communication among all stakeholders by
providing access to a shared digital twin platform, enabling real-time updates and collaborative decision-
making.
•Feedback Loops: Create feedback loops where data from later phases (e.g., operations and maintenance) is
fed back into earlier phases (e.g., design and engineering) to inform improvements and drive continuous
innovation.
Setting up the Digital Twin
Creating a Perfect Digital Thread
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51. Types of data feeding into the Digital Twin
Here: example of a DT created only upon commissioning, fed by data fromanother connected ecosystem
August 2024DIGITAL TWIN – THE NEW NORMAL IN PRODUCT LIFECYCLE MANAGEMENT 51

52. Building Information Modeling (BIM) models deliver a wide range of data that is crucial for the design, construction,
and management of building and infrastructure projects. The data provided by BIM models can be categorized into
several types, each serving different purposes throughout the project lifecycle:
•Geometric Data: 3D Geometry (including all structural and architectural elements), Spatial Relationships
•Physical Properties: Material Specifications and Structural Properties
•Functional Data: Usage Information and Performance Data (e.g.energy efficiency and thermal properties, etc).
•Cost Data: Cost Estimates vs Budget Tracking throughout the project lifecycle.
•Scheduling Data: Project Timeline, Task Management
•Lifecycle Data: Maintenance Schedules and Operational Data (to ensure efficient operation of the building, incl. all major systems).
•Environmental Data: Energy Consumption, Sustainability Metrics
•Regulatory Compliance Data: Building Codes and Standards and Data on safety features and compliance with safety regulations
•Collaboration and Coordination Data: Document Management, Version Control
•Asset Management Data: Asset Register of all assets within the building
•Warranties and Manuals: Data on warranties, manuals, and maintenance instructions for various assets.
BIM Data allows for Improved Collaboration, Enhanced Decision-Making, higher Efficiency and advanced Lifecycle Management duringthe
entire lifecycle of the building, from design and construction through to operation and maintenance. This brings significant Cost Savings.
Data feeding into the Digital Twin
BIM Data
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53. The data feeding the digital twin during the early phases of a development project help to validate concepts, perform essential
simulations, create and refine requirements and models.
Generative AI can help to create additional data for advanced learning, to speed up the process.
•Operational Requirements, Use Cases, Safety and regulatory requirements, Environmental and sustainability requirements
•Requirements Documents: Functional and performance requirements in textual or spreadsheet formats.
•CAD Models: 3D design data from CAD (Computer-Aided Design) tools, typically in formats like STEP, IGES, or native CAD
formats (e.g., SolidWorks, CATIA).
•Planned Design parameters (Structural and mechanical design parameters, Electrical and electronic design parameter)
•Software Architecture and interfaces
•BIM Data (Geometric Data, Spatial Relationships, Physical Properties & Functional Data)
•System models and initial architectures (functional and technical)
•Simulation Results (e.g., FEA, CFD) in formats like CSV, HDF5, or specialized simulation software outputs.
•Technical Dossiers: Design descriptions in document or spreadsheet format
•MBSE models and data models
•Risk and Safety Assessments(PHA, etc)
Data feeding into the Digital Twin
Concept and Design Phase
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54. •Electrical Schematics: Diagrams Software Code and Configuration: Design Data
•CAD Models: Detailed 3D CAD models of components, assemblies, and the overall system.
•Design Specifications: Technical specifications and design criteria, including dimensions, materials, and performance requirements.
•Drawings and Schematics: Detailed engineering drawings, electrical schematics, piping and instrumentation diagrams (P&IDs), often in and layouts
in EDA (Electronic Design Automation) tool formats.
•Simulation and Analysis Data
•Finite Element Analysis (FEA): Results from structural, thermal, and other finite element analyses.
•Computational Fluid Dynamics (CFD): Data from fluid dynamics simulations.
•Dynamic Simulations: Results from kinematic and dynamic simulations of system behavior.
•Requirements and Compliance Data
•System Requirements Specification (SyRS): Detailed documentation of functional and non-functional requirements.
•Compliance Documentation: Records showing compliance with industry standards, safety regulations, and certification requirements.
•Traceability Matrices: Documents linking requirements to design elements, tests, and validation activities.
•Configuration Data
•Version and Revision Control: Records of design iterations, version history, and changes made during development.
•Bill of Materials (BOM): Comprehensive list of all components and materials used in the design, including part numbers and quantities,
•Detailed parts lists in formats such as CSV, XML, or integrated directly from PLM (Product Lifecycle Management) systems
Data feeding into the Digital Twin
Engineering and Development Phase (slide 1 of 2)

55. •Prototype and Testing Data:
•Prototype Specifications: Detailed descriptions and specifications of prototype models used during development.
•Test Plans and Procedures: Documentation of testing protocols, procedures, and conditions.
•Test Results and Reports: Data from functional, performance, and durability tests, including pass/fail criteria and any observedissues.
•Validation Reports: Results from system validation activities, ensuring the design meets all specified requirements.
•Software and Control Systems Data
•Source Code: Source code for any embedded software, control algorithms, and firmware.
•Software Specifications: Detailed documentation of software functionality, interfaces, and integration points.
•Simulation Models: Software models used for simulating system behavior and control logic.
•Source code and configuration files, often stored in repositories like Git in formats such as text, JSON, or XML.
•Safety and Risk Analysis Data
•Failure Modes and Effects Analysis (FMEA): Documentation of potential failure modes, their causes, and mitigation measures.
•Hazard Analysis: Identification and assessment of potential hazards, along with mitigation strategies.
•Safety Case: Comprehensive safety documentation demonstrating that the system meets safety requirements.
•Maintenance and Service Data
•Initial Maintenance Schedules: Preliminary maintenance schedules based on design specifications and expected operational conditions.
•Service Instructions: Detailed instructions for maintenance and service activities, including tools and procedures.
Data feeding into the Digital Twin
Engineering and Development Phase (slide 2 of 2)
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56. •Manufacturing Process Data: Sensor Data, e.greal-time data from IoT sensors on the production floor, typically in JSON, XML, or direct
streaming protocols (e.g., MQTT).
•Geometric and Design Data (Final CAD Models -reflecting any modifications or deviations from the original design; Dimensional
Measurements incl Precise measurements and tolerances of components and assemblies)
•Material Data (Material Specifications, Batch and Lot Numbers)
•Manufacturing Process Data (Data on manufacturing process parameters, settings and conditions, such as temperature, pressure,and
machining parameters and Production Records with all relevant details). Data from MES (Manufacturing Execution Systems) and ERP
(Enterprise Resource Planning) systems, typically in database formats (SQL, CSV).
•Quality Control Data (Inspection and Test Results (quality inspections, NDT, dimensional inspections, and functional tests) and Defects
and Rework Data (defects identified during production and the corrective actions taken).
•Assembly Data (Assembly Instructions, Fit and Alignment Data of components and subsystems, Incident reports).
•Calibration and Certification Data (Calibration Records, Certification Documents, Statements of conformity and compliance).
•Configuration Management Data (Version and Revision Control, Records of design and manufacturing changes, Configuration Status,
including any updates or modifications made during production or pre-delivery).
•Supply Chain and Logistics Data (Supplier Information, Delivery and Shipping Records).
•Operational Readiness Data (Results from pre-operational tests and trials, ensuring that the product is ready for service).
•Maintenance and Service Instructions: Initial maintenance schedules and service instructions based on manufacturing data.
Data feeding into the Digital Twin
Manufacturing and Production Phase
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57. Real-Time Operational Sensor Data: Real-time data from the field, including telemetry and performance data, often in
formats like JSON, XML, or streaming data protocols.
•Operational Performance Data
•Equipment status and health metrics
•Real-time usage statistics and performance metrics
•Environmental conditions (temperature, humidity, etc.)
•Process and Control Data
•Control system parameters and logs
•Process variables (pressure, flow rates, etc.)
•System alerts and alarms
Historical Performance Data: from Operational Data Systems, Incident and Fault Reporting Systems, Data Warehouses and
Historical Databases, Performance Management Systems
•Operational History
•Historical usage patterns and performance trends
•Historical environmental data
•Long-term degradation and wear patterns
•Failure and Repair History
•Detailed records of past failures and breakdowns
•Repair procedures and times
•Root cause analysis reports
Data feeding into the Digital Twin
Operations and Maintenance Phase (slide 1 of 4)
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58. Real-Time Sensor Data
•Scheduled Maintenance Data
•Maintenance schedules and logs
•Preventive maintenance tasks and outcomes
•Parts replaced and repairs performed
•Unscheduled Maintenance Data
•Incident reports and troubleshooting steps
•Corrective maintenance actions
•Downtime duration and impact analysis
Predictive and Diagnostic Data
•Predictive Maintenance Algorithms
•Predictive models based on historical and real-time data
•Remaining useful life (RUL) estimates
•Anomaly detection results
•Condition Monitoring Data (from onboard and guideway-based monitoring systems)
•Vibration analysis data
•Oil analysis reports
•Thermal imaging and other non-destructive testing (NDT) results
Maintenance Records:
•Data from CMMS (Computerized Maintenance Management Systems) which tracks all maintenance activities, including scheduled and
unscheduled maintenance, repairs, and part replacements.
•Includes historic data on maintenance history, frequency of interventions, and downtime.
Data feeding into the Digital Twin
Operations and Maintenance Phase (slide 2 of 4)
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59. User and Operator Feedback: Customer feedback and issue reports, typically in textual or spreadsheet formats, often collected
through CRM (Customer Relationship Management) system.
•User Interaction Data
•User interface logs and interactions
•Operator feedback and suggestions
•Training records and competency assessments
•Customer Service Data
•Customer complaints and service requests
•Service response times and resolution effectiveness
•Customer satisfaction metrics
Compliance and Regulatory Data: coming from different sources such as Government Agencies and Regulatory Bodies, Standards
Organizations, internal compliance programs, certification bodies, Project Records, Industry Associations and Consortia
•Regulatory Compliance Records
•Inspection and audit reports
•Certification and compliance documentation
•Environmental impact data
•Safety and Risk Management Data
•Safety incidents and near-miss reports
•Risk assessments and mitigation plans
•Emergency response logs
Data feeding into the Digital Twin
Operations and Maintenance Phase (slide 3 of 4)
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60. Supply Chain Optimization across the complete supply chain can be achieved by bringing together
•the actual failure rates and spares utilization and warranty reports data in comparison with the original
reliability and spares planning data,
•Supply Chain, Supplier and Logistics Performance Data compared against initially estimated metrics and SLAs
•Supplier delivery performance and quality metrics
•Warranty claims and return rates
•Supplier reliability and risk assessments
•Information of Maintenance providers and Depot information (resources, parts, dock places etc)
•Operators planning data (in comparison with past plannings and usage data)
•Inventory Management Data and Real-time availability information of OEM data
•Parts inventory levels and usage rates
•Lead times and supply chain disruptions
•Inventory costs and stock-out incidents
•Production Forecast and planning
•Historical usage patterns and performance trends
Data feeding into the Digital Twin
Operations and Maintenance Phase (slide 4 of 4)
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61. End-of-Life Phase datais retained for Regulatory Compliance, Knowledge Retention and REX and to provide a complete
lifecycle view, from design to decommissioning, to help improve future asset designs and operational strategies.
•Decommissioning Data: Information on decommissioning processes, disposal and materials recycling, often in CSV or database formats.
Execution Logs, Decommissioning Plans and Costs. Needs to be stored compliant with applicable regulations.
•Environmental Impact Data: Environmental Assessments (including emissions, waste generation, and potential contamination),
Documentation of measures taken to mitigate environmental impacts, Certificates and documentation proving compliance with
environmental regulations and standards during the decommissioning process.
•Safety and Risk Management Data: Documentation of risk assessments conducted prior to and during the decommissioning process,
Safety Plans and Procedures during the decommissioning process, Incident Reports of any incidents, accidents, or near-misses occurred
during decommissioning, including root cause analyses and corrective actions taken.
•Asset Disposal Data: Disposal Records including recycling, re-use, or landfill. Recycling and Reuse Documentation verifying the proper
recycling or reuse of asset components. Documentation on the handling, transportation, and disposal of hazardous materials, including
compliance with relevant regulations.
•Regulatory and Legal Compliance Data: Regulatory Filings with regulatory bodies regarding the decommissioning process, Compliance
Audits Results and Copies of any permits or licenses required for the decommissioning activities.
•Historical Performance and Operational Data: Operational Logs including performance data, maintenance records, and incident logs.
Reports analyzing the performance of the asset throughout its lifecycle, including efficiency, reliability, and reasons for decommissioning.
•Historical Data Archives: Long-term storage of all lifecycle data for future reference and analysis, in data lakes or warehousing solutions.
Data feeding into the Digital Twin
End-of-Life Phase
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62. Michael Hengst
Founder & Lead Consultant
HeCoS-Hengst Tech Consulting Services
https://www.hengsttechconsult.com/
Tel: [phone]
[email protected]
August 2024DIGITAL TWIN – THE NEW NORMAL IN PRODUCT LIFECYCLE MANAGEMENT 62

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