Physical Meets Digital – How Digital Twins Help Future-proof Sectors

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Digital twins are increasingly gaining traction across a broad spectrum of business applications, given they can seamlessly connect data across physical and digital realms. They are virtual representations intended to accurately represent physical items such as assets, systems, and processes. They employ artificial intelligence (AI), real-time analytics, visualization, and simulation tools to identify, prevent, forecast, and optimize the physical environment. Its key benefits include increased inter-team collaboration, superior risk evaluation and production schedule, real-time remote monitoring​, improved financial decision-making, and predictive analysis.

The digital twins thematic research report presents real-world innovation use cases and examples related to the implementation of digital twins by companies and startups across multiple sectors. It casts light on how tech-enabled innovations transform every segment of cross-sector use case applications.

Digital Twins – Classification

The main types of digital twins are component or part twins, asset twins, system or unit twins and process twins.

Component twins: These are the fundamental unit of a digital twin, representing the smallest example of a functional component.

Asset twins: Asset twins allow investigating the interplay of those components, resulting in a plethora of performance data that can be analyzed and transformed into meaningful insights.​

System or unit twins​: System twins give visibility into asset interactions and can identify the scope for performance improvements.

Process twins: They show how systems interact to produce a complete manufacturing facility. It assists in determining the specific timing schemes that impact overall effectiveness.​

Digital Twins – Key Applications by Sector

Digital twins, paired with AI and machine learning (ML) capabilities, are increasingly becoming a powerful technology for businesses in various sectors to increase efficiency, mitigate risk, and enhance customer experience.​ Digital twins have applications in multiple sectors such as aerospace & defense, agriculture & forestry​, automotive​, construction & real estate, consumer, healthcare, industrial goods & machinery, oil & gas, power & utilities, retail, telecom, and transportation, infrastructure & logistics, among others.

Aerospace & Defense: Digital twins are used to scale earth and other celestial bodies, train personnel in operations and maintenance, simulate, weapons, analyze space weather, and inspect parts and structures.​

Agriculture & Forestry: Digital twins are used to sustainably cultivate crops underwater, scale farmland and equipment, and help improve farming practices.

Automotive: Launch products in a decentralized environment, scale technologies for smart manufacturing, chassis diagnostics, and autonomous vehicles.​

Leading Companies in the Digital Twins Theme

Some of the leading companies making their mark in the digital theme are Aforza, AgentRelay, Agronomeye, Blackshark, Cupix, Google, Hitachi Energy, Matterport, Mendix, Nokia, and Siemens among others.

For more insights on digital twins theme, download a free report sample

Digital Twins Overview

Key Classification Component or Parts Twins, Asset Twins, System or Unit Twins and Process Twins
Key Application by Sectors Aerospace & Defense, Agriculture & Forestry, Automotive, Construction & Real Estate, Consumer, Healthcare, Industrial Goods & Machinery, Oil & Gas, Power & Utilities, Retail, Telecom, and Transportation, Infrastructure & Logistics, and Others
Leading Companies Aforza, AgentRelay, Agronomeye, Blackshark, Cupix, Google, Hitachi Energy, Matterport, Mendix, Nokia, and Siemens


  • Innovation Insights: innovation examples by each use cases segment of various sectors to present key trends.
  • Vendor Map: represents a sample list of vendors in each use case highlighted in the report.

Reasons to Buy

  • No surprise that technology has been a driving force in business transformation for years, but the term ‘emerging technologies’ has all of the sudden become the key catalyst to drive the next wave of innovation across sectors.
  • The sense of urgency weighs different across different sectors, where the direct customer-facing sectors are at the forefront compared to other capital-intensive sectors. Companies in one sector can take cues from successful innovations in other sectors to either draw analogies with existing products, services, and processes or transfer strategic approaches for a revolutionary transformation.
  • Against this backdrop, enterprises need to understand which emerging technologies are impacting their sector and how various companies are implementing them to meet various challenges.
  • The innovation landscape report on digital twins published by GlobalData covers some of the key trends, use cases, and real-world examples related to the implementation of emerging technologies across several cross-sector applications.





Alder Hey

Amaury Sport Organisation

Amazon Web Services








Bentley Systems




China Mobile







Energy Market Authority








Hitachi Energy

Indian Oil Corporation Limited



Kraft Heinz





Manufacturing x Digital






Nemo’s Garden




Nippon Telegraph and Telephone Corporation






Schneider Electric

Sharper Shape




Slingshot Aerospace

SP Group





Tour de France

Transport for London

Twin Health


US Air Force Research Laboratory





Table of Contents

Table of Contents

1. What are digital twins and what are their advantages?

2. Classification of digital twins

3. What is the sector impact of digital twins?

4. Key applications by sector

4.1. Aerospace & Defense

4.2. Agriculture & Forestry

4.3. Automotive

4.4. Construction & Real Estate

4.5. Consumer

4.6. Healthcare

4.7. Industrial Goods & Machinery

4.8. Oil & Gas

4.9 Oil & Gas

4.10 Retail

4.11 Telecom

4.12 Transportation, Infrastructure & Logistics

4.13 Others

5. Major vendors by sectors

6. What are the implementation challenges of digital twins?

7. Methodology

Frequently asked questions

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