Short answer
When designing Digital Twin solutions for manufacturing, prioritize adaptability, scalability, and interoperability to ensure long-term value and facilitate integration within complex industrial ecosystems.
- Field
- Modelling
- Source
- Big Data and Cognitive Computing (2023)
- Method
- Mixed-methods research (survey and expert interviews)
- Sample
- 113 (99 survey respondents + 14 interviewees)
- Evidence
- Strong effect
Digital Twins are a critical modelling tool for manufacturing, requiring adaptability, scalability, and interoperability to support assets throughout their lifecycle and deliver ROI within two years. This modelling research insight is drawn from a 2023 study published in Big Data and Cognitive Computing. Using Mixed-methods research (survey and expert interviews) with 113 (99 survey respondents + 14 interviewees), researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing Digital Twin solutions for manufacturing, prioritize adaptability, scalability, and interoperability to ensure long-term value and facilitate integration within complex industrial ecosystems.
Digital Twins: Bridging the Gap Between Industry Needs and Manufacturing Realities
Digital Twins are a critical modelling tool for manufacturing, requiring adaptability, scalability, and interoperability to support assets throughout their lifecycle and deliver ROI within two years.
Big Data and Cognitive Computing · 2023
Key Findings
- 01Digital Twins require adaptability, scalability, and interoperability to support assets across their entire lifecycle.
- 02Completed Digital Twin projects achieve breakeven on average in under two years.
- 03Key motivators for Digital Twin development include autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability.
- 04Major obstacles to Digital Twin adoption are a lack of expertise, funding, and interoperability issues.
- 05Federation of twins and a shift in industrial thinking are essential for future Digital Twin development.
Application
Design takeaway
When designing Digital Twin solutions for manufacturing, prioritize adaptability, scalability, and interoperability to ensure long-term value and facilitate integration within complex industrial ecosystems.
How to apply
When developing or specifying Digital Twin solutions, ensure they are designed with modularity, open standards for data exchange, and the capacity to evolve alongside the physical assets they represent.
Project actions
- 01When creating a Digital Twin model, think about how it will need to change or expand in the future.
- 02Research common data exchange standards to ensure your model can communicate with other software or hardware.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Combines quantitative survey data with qualitative expert interviews for a comprehensive view.
- +Focuses on practical industry needs and challenges, bridging a gap in existing research.
Limitations
The findings are based on a specific industry (defence) in a particular country (UK), so they might not apply everywhere.
Reliability & validity
The study's reliability is supported by a mixed-methods approach, combining a larger survey with in-depth expert interviews. Validity is enhanced by focusing on prominent UK organizations and addressing a specific knowledge gap in industrial practices.
Think critically
Given the identified obstacles of expertise and funding, how can designers and engineers advocate for and implement Digital Twin technology in smaller or less resourced manufacturing settings?
Design Principles
"Design for Lifecycle Interoperability: Digital models should be inherently adaptable and scalable to support assets throughout their entire operational life and integrate seamlessly with diverse systems."
Understanding the practical requirements and challenges of implementing Digital Twins in manufacturing is crucial for designers and engineers. This insight highlights the key characteristics that make DTs effective and the common barriers to adoption, informing the development of more robust and user-friendly digital modelling solutions.
What This Means for Your Design
Digital Twins are like virtual copies of real-world manufacturing processes or products. For them to work well, they need to be flexible, able to grow, and connect with other systems. Companies can get their money back from these investments in less than two years. The main challenges are not having enough experts, money, or systems that can talk to each other.
How to use in your project
- 1.Refer to this study when discussing the importance of digital modelling, the benefits of Digital Twins, and the challenges of implementation in your design project.
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Quick Cite
Paragraph starter
Research indicates that Digital Twins are a vital modelling approach in manufacturing, requiring adaptability, scalability, and interoperability to effectively support assets throughout their lifecycle. Studies show that these digital models can achieve a return on investment within two years, driven by benefits such as enhanced autonomy and optimization. However, adoption is often hindered by a lack of specialized expertise, funding constraints, and challenges in achieving seamless system integration.
Source
Big Data and Cognitive Computing
Industrial Insights on Digital Twins in Manufacturing: Application Landscape, Current Practices, and Future Needs
journal · 2023
View sourceQuestions About This Research
- What does the research say about digital twins: bridging the gap between industry needs and manufacturing realities?
- When designing Digital Twin solutions for manufacturing, prioritize adaptability, scalability, and interoperability to ensure long-term value and facilitate integration within complex industrial ecosystems. Evidence: Big Data and Cognitive Computing (2023).
- Why does "Digital Twins: Bridging the Gap Between Industry Needs and Manufacturing Realities" matter for design?
- Understanding the practical requirements and challenges of implementing Digital Twins in manufacturing is crucial for designers and engineers. This insight highlights the key characteristics that make DTs effective and the common barriers to adoption, informing the development of more robust and user-friendly digital modelling solutions.
- How can designers apply this research?
- When designing Digital Twin solutions for manufacturing, prioritize adaptability, scalability, and interoperability to ensure long-term value and facilitate integration within complex industrial ecosystems.
- What were the main findings?
- Digital Twins require adaptability, scalability, and interoperability to support assets across their entire lifecycle.. Completed Digital Twin projects achieve breakeven on average in under two years.. Key motivators for Digital Twin development include autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability.. Major obstacles to Digital Twin adoption are a lack of expertise, funding, and interoperability issues.
- What research method was used?
- Mixed-methods research (survey and expert interviews) with 113 (99 survey respondents + 14 interviewees).
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2023 journal from Big Data and Cognitive Computing.
- What should I do differently in my next project?
- When developing or specifying Digital Twin solutions, ensure they are designed with modularity, open standards for data exchange, and the capacity to evolve alongside the physical assets they represent.
- What are the limitations?
- The study's focus on the UK defence industry may limit the generalizability of findings to other manufacturing sectors or geographical regions.