Have you ever pondered the process of building a digital twin and the turnaround time for this advanced technology?

The typical digital twin development process takes several weeks to several months, depending on its complexity and intended use.

Recent research indicates that businesses that invest in custom digital twin development can reduce operating expenses by up to 30% and boost productivity by 20%.

However, why is the digital twin in product development taking so long?

Every stage is crucial, from obtaining high-quality data to incorporating cutting-edge technology like IoT and AI.

This blog will explain the stages of digital twin product development, and the factors determining the digital twin technology development process helps in custom digital twin development to streamline the procedure.

Building an extremely realistic and active virtual representation of a real asset, system, or procedure is known as "digital twin development." Using data from sensors, Internet of Things (IoT) devices, and other data sources, this model mimics its physical equivalent in real-time, allowing businesses to continually simulate, evaluate, and improve productivity.

Simply put, a digital twin is a virtual replica of a real thing. It records the information and actions of the physical object, mirroring any alterations that take place. This virtual model makes real-time monitoring, diagnostics, and prognostics possible, offering insightful data that improves decision-making and increases operational effectiveness.

For example, think of digital twins in the automotive sector. An established manufacturer uses digital twin technology to streamline its manufacturing process. The organization can detect possible delays and inefficiencies by simulating various situations using digital representations of its technology and manufacturing processes. This real-time data ensures quicker processes, lowers interruptions and aids with predictive maintenance. The company can observe a notable decrease in production expenses and new model time-to-market. 

Please Read: What are the benefits of digital twins? Here 

1. Planning and Blueprinting 

During this first phase, the goals and parameters of the digital twin are defined. This stage includes determining the real-world assets to be replicated and establishing precise objectives for the digital twin, such as predictive maintenance or performance optimization. 

It takes around 1-2 months on average.

Key Activities and Considerations:

• Defining Objectives

• Scope Identification

• Stakeholder Engagement

• Technical Feasibility

• Blueprint Creation

This phase ensures all stakeholders agree and provide an explicit schedule, setting the foundation for the next development project stage.

2. Data Collection and Integration 

The next stage involves gathering information from multiple sources and combining it into a coherent model. This phase takes 2-3 months and entails installing sensors, combining current data systems, and guaranteeing the data's reliability and precision.

Problems and Solutions:

• Data quality: Guaranteeing the precision and coherence of data gathered from various sources can be difficult. Therefore, procedures for data cleansing and verification are essential.

• Sensor integration: It sets up sensors and Internet of Things devices to gather data in real-time.

• Data compatibility: Integrating data from multiple sources and outdated systems might be difficult. Data transformation and middleware solutions can be beneficial.

• Security concerns: Protecting the privacy and security of data, particularly while working with sensitive data, requires implementing strong encryption techniques and security mechanisms. Thus, you must follow the best practices to ensure digital twin security. 

High-quality data must be gathered and integrated to create an accurate and trustworthy digital twin.

3. Modeling and Simulation 

Advanced simulation technologies are needed to create the digital twin model. This stage typically lasts 3-4 months. It entails creating 3D models, simulating scenarios, and enhancing accuracy with machine learning techniques.

Tools and Techniques Employed:

• 3D modeling:  Producing intricate 3D models of tangible items using CAD software.

• Simulation Software: Use simulation tools such as Ansys, Simulink, or custom-built solutions to run different scenarios and forecast results.

• Machine learning techniques: Machine learning can improve the precision of simulations and yield forecasted information.

• Integration with Real-Time Data: Real-time data integration with digital twins constantly adds to the model so that it accurately reflects the physical asset's actual state.

During this phase, gathered data is transformed into valuable insights that support efficiency and strategic decision-making.

4. Testing and Validation 

Comprehensive testing and validation are essential before implementing the digital twin. This stage includes comparing the digital twin's efficiency with actual data, finding differences, and making sure the model satisfies all criteria.

This step takes 1-2 months.

Guaranteeing Precision and Dependability:

• Benchmarking: It is the process of contrasting the performance of the digital twin with actual data to find differences.

• Iterative Testing: Putting the model through several iterations to enhance its precision.

• User feedback: Feedback is gathered from stakeholders and final consumers to ensure the model satisfies all needs.

• Stress Testing: Stress testing is one way to evaluate how well a model performs in challenging circumstances.

Testing and validation are essential to guarantee that the digital twin is dependable and offers correct insights.

5. Implementation and Upkeep

After it has been certified, the digital twin is set up, tracked, and upgraded continually. This continuous phase entails real-time monitoring, frequent updates, and maintenance to guarantee precision and dependability.

The time frame is continuous.

Constant Updates and Real-Time Surveillance:

• Deployment: Digital twin implementation in the operational environment.

• Real-Time Monitoring: Consistently tracking the physical asset's digital twin using real-time data ensures that the digital twin accurately depicts its present condition.

• Frequent Updates: Adding new information and insights to the model regularly.

• Maintenance: Keeping the model accurate and dependable requires regular maintenance.

The digital twin's long-term worth is ensured via deployment and maintenance, which aids companies in streamlining operations and making wise decisions. 

Suggested Read: Digital twin cost

1. Complexity of the System or Product

The primary determinant of digital twin development time is the complexity of the system or product under consideration. More sophisticated connections and many elements in a system call for more thorough simulation and modeling.

For instance, consider digital twins in manufacturing. Creating a digital twin for an essential piece of machinery can take a few months. Conversely, creating a digital twin for a whole manufacturing facility or significant infrastructure project might take many months or even years.

• Complexities in accurately replicating each component and process increase the development time.

• Ensuring the fidelity of the model and integrating various subsystems adds to the duration.

• More detailed and precise digital twins demand longer development procedures.

• Developing a digital twin for a complex system like an aviation engine requires modeling numerous components.

• Each component has unique behaviors and relationships, leading to extended development timelines compared to simpler mechanical devices.

2. Availability and Quality of Data

The growth of digital twins depends on data. The quantity and quality of data significantly influence how long it takes to generate a functional digital twin.

For example, information from occupancy sensors, lighting, HVAC systems, and other sources is needed to create digital twins for smart city development. Optimizing the availability and accuracy of all these data pieces can significantly influence the timeframe.

• High-quality, real-time data from sensors and IoT devices accurately mirrors the activity of the digital twin.

• Low-quality or inconsistent data can lead to errors, requiring additional effort for data cleansing and testing.

• Data collection involves integrating multiple sources, including previous databases, real-time sensor feeds, and manual inputs.

• Synchronizing and ensuring the integration of gathered data can be time-consuming.

• Installing sensors, setting up data collection methods, and ensuring data integrity can add to the timeline, especially when data accessibility is limited.

3. Technological Infrastructure

The time it takes to construct a digital twin also depends mainly on the available technology infrastructure.

For example, a company that relies on outdated technologies that require significant changes cannot generate digital twins as quickly as one that uses a current IoT platform and sophisticated simulation tools.

A knowledgeable team with up-to-date knowledge of digital twin technology can overcome obstacles faster and finish projects sooner. This is where the best digital twin companies play a pivotal role.

Streamlining the Development Process:

• Sophisticated simulation tools and powerful data processing capabilities help accelerate digital twin development.

• Easy integration networks further simplify data gathering, simulation, and integration tasks.

Technology Infrastructure:

• Existing technology infrastructure speeds up development by supporting seamless integration and simulation.

• Insufficient or outdated infrastructure can slow progress due to system upgrades, tool discovery, and compatibility challenges.

Development Team Proficiency:

• The team's expertise and familiarity with the chosen technology significantly influence the development timeline.

• Skilled teams can quickly adapt to tools and processes, ensuring smoother development workflows.

4. Integration with Existing Systems

Integrating a digital twin with current systems, such as ERP or IoT frameworks, is essential in the development phase. However, depending on the integration's complexity, this may significantly affect schedules.

• Compatibility problems: It's common for outdated systems to be unable to integrate with contemporary technologies easily.

• API Standardization: When no standardized APIs exist, system data transfer may be slowed.

• Custom Development Needs: Solutions may need to be tailored, which can take longer to fill in gaps.

Businesses can guarantee a seamless digital twin development process and prevent delays by anticipating integration issues.

5. Regulatory and Compliance Considerations

Industries with strict regulatory systems include healthcare, aviation, and the automotive sector. Complying with these compliance requirements frequently increases complexity and delays the time necessary for creating the digital twin.

Important elements consist of:

• Following rules particular to a given industry, such as the GDPR, FDA regulations, or FAA requirements.

• Ensuring confidentiality and security of information in accordance with international norms.

• Carrying out routine validations and audits to ensure the digital twin complies with the necessary certifications.

By attending to these regulatory expectations early in the development process, businesses can prevent delays and ensure that their digital twin solutions successfully satisfy legal and ethical criteria.

Please Read: Digital twin technology companies

For companies looking to take advantage of this cutting-edge technology, it is essential to comprehend the timelines associated with digital twin development. Every process step, from design and planning to implementation and upkeep, is critical to producing a dependable and efficient digital twin. The system's complexity, data availability and quality, and technological infrastructure are essential variables that affect how long a project takes to develop.

Time spent developing digital twins pays dividends since it facilitates preventative maintenance, lowers interruptions, and improves operations. Compared to conventional approaches, these advantages make digital twins an effective tool for increasing productivity and realizing substantial financial savings.

At Toobler, we recognize that every company faces different demands and difficulties. Our team specializes in offering customized digital twin solutions that faithfully replicate your physical assets or systems and provide insightful data about their behavior and performance. We guarantee a seamless and effective development procedure using the most recent tools and methodologies. This assists you in meeting your business objectives in a reasonable amount of time.

Please speak with our experts to discover how our professional services may revolutionize your company.

1. What factors influence the development time of a digital twin?

A digital twin's time of development is determined by a number of factors, including the

• System complexity that is being duplicated.

• Both the twin's data volume and quality are necessary.

• Combining cutting-edge technology like AI and IoT.

• The accessibility of precise historical data for examination.

• Dynamic functionality and real-time updates are required.

• Requirements for scalability in order to expand in the future.

• Degree of personalization and certain industry requirements.

• Communication and collaboration between interested parties.

2. What are the typical stages involved in digital twin development?

The following are the standard phases of digital twin development:

• Requirement Analysis: It involves determining goals, parameters, and sources of information.

• Data collection: Compiling the twin's historical and current data.

• Modeling is the process of building a digital image of the real system.

• Integration: Linking sensors, data streams, and Internet of Things devices.

• Testing and simulation: Executing scenarios to confirm functionality.

• Deployment: The process of integrating the digital twin into the operational setting.

• Updates and Monitoring: Constantly improving and enhancing the twin.

3. How can the development time of a digital twin be reduced?

By using pre-built templates and libraries, which do not need starting from scratch, a digital twin can be developed faster. Faster access to correct information is ensured by streamlining data collection through the integration of automated technologies.

Model deployment and testing can be completed more quickly by using cloud-based platforms. Cross-functional team collaboration increases productivity, and agile approaches shorten development cycles. Investing in cutting-edge tools and technology, such as AI and IoT, also aids in process automation, reducing delays and guaranteeing a quicker turnaround.