Digital Twins – Thematic Intelligence

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Digital twins are digital representations of physical assets, systems, people, or processes. They help detect, prevent, predict, and optimize the physical environment using artificial intelligence (AI), real-time analytics, visualization, and simulation tools. Conceptually, digital twins have been around for decades; a forerunner was used in NASA’s Apollo 13 mission to the moon in 1970. While far from ubiquitous today, adoption is increasing across industries, although challenges around security and interoperability still need to be addressed.

Scope

This report provides an overview of the digital twins theme.

It identifies the key trends impacting growth of the theme over the next 12 to 24 months, split into three categories: technology trends, macroeconomic trends, and regulatory trends.

It includes a comprehensive industry analysis, including use cases for digital twins across various industries, including manufacturing, power, oil and gas, healthcare, construction, automotive, aerospace and defense, government, and sports.

The detailed value chain comprises six layers: a physical layer, a connectivity layer, a data layer, a platform layer, a delivery layer, and a services layer.

Key Highlights

GlobalData forecasts that the global digital twins market will reach $154.3 billion by 2030, driven by advances in underlying technologies such as the Internet of Things (IoT), cloud, AI, and data analytics. The number of use cases for digital twins is increasing and includes remote asset monitoring, 3D design, and modeling of the effects of drugs on human patients.

Interoperability remains a key concern for digital twins. For widespread adoption of digital twins, it is important to ensure they can communicate effectively with each other. This requires standardizing data formats, communication protocols, and interfaces for seamless integration across different platforms, software, and hardware. Efforts are underway to address these challenges; however, achieving full interoperability requires collaboration among industry stakeholders, technology providers, and standardization bodies.

Reasons to Buy

The range of potential use cases for digital twins is extensive. They range from design and architecture to engineering, smart cities, aerospace and defense, power, oil and gas, and, probably the most advanced, a digital twin of the human body. This report tells you everything you need to know about digital twins, including identifying the current leaders in some of the most important segments of the digital twins value chain.

AAC Technologies
Ab Initio
ABB
AccelData
Accenture
Accton
Actifio
Adeptia
Adetiq
Adimec
Advantech
Aeris
AEye
AIMMS
Airbyte
Aize
Akamai
Alation
Alibaba
Alkira
Alphabet
Alps Electric
Altair
Alteryx
Altibase
Amazon
Ambarella
AMD
AMS
Amundsen
Analog Devices
Ansys
Anyscale
Apache Foundation
Apexon
Apple
aPriori
Aptiv
Arista Networks
Arm
Arup
Astronomer
AT&T
Ataccama
AtkinsRéalis
Atlan
Atos
Attivio
Auron Technologies
Autodesk
Avanade
Aviatrix
AxoMem
BAE Systems
Baidu
Basler
Belden
Bentley Systems
Bharti Airtel
Bigeye
Black & Veatch
Blaize
BMC Software
Boeing
Boomi
Bosch
BP
Broadcom
Broadcom (VMware)
BT Group
Cadmatic
Canon
Capgemini
Cato Networks
CData
CEDQ
Celestica
Celigo
Census
CGI
Check Point Software
Chicony
China Mobile
China Telecom
China Unicom
Ciena
Cintoo
Cisco
ClearBlade
ClickHouse
Cloud Software Group
Cloudera
Cognex
Cognite
Cognizant
Cohesity
Colibra
Comcast
Confluent
Continental
Cosmo Tech
Couchbase
CrowdStrike
CyberTwin
Cyclr
Dahua
Damco Group
Darktrace
Dask
Dassault Systèmes
Data Virtuality
data.world
Databricks
DataCaptive
DataHub
Dataiku
DataStax
DataTorrent
DataVirtuality
Dbt Labs
Decimetrix
Dell Technologies
Deloitte
Delphix
Delta Lake
Denodo Technologies
Denso
Deutsche Telekom
DJI
Domo
Dremio
dSPACE
Duality Robotics
Eastern Jin Tech
Elementl
e-Magic
Emerson Electric
EnterpriseDB
Epic Games
Equinix
Ericsson
Esaote
Esri
Eviden
Exasol
Experian
Extreme Networks
Exxon Mobil
EY
F5 Networks
Fastly
Finisar
Fivetran
Flexpoint
Flink
Forcepoint
Fortinet
Fujifilm
Fujitsu
Gafcon
Garmin
GE
Global Laser
Goertek
GoPro
Great Expectations
HCLTech
Helpsystems
Hightouch
Hikvision
Hitachi
Hive
Holitech Technology
HollySys
Hologic
Honeywell
HPE
HTC
Huawei
Humanising Autonomy
IBM
iFlytek
Immuta
Imply
Indie Semiconductor
Infineon
Informatica
Information Builders
Innoviz
Insource
Inspur
Intel
Intenda
InterSystems
Jitterbit
Juniper Networks
Kanematsu
KBR
KDDI
Keboola
Keyence
Knowles
Kongsberg
Konica Minolta
KT
Kyndryl
Largan Precision
Lenovo
LG Electronics
LG Innotek
Libelium
Link Labs
Lite-On
Lockheed Martin
LookML
Lumen Technologies
Lumentum
Luminar Technologies
Magna
Make
MariaDB
MarkLogic
Matterport
MediaTek
Microchip
Microsoft
Microvision
Mitsubishi Electric
Mobileye
MongoDB
Monte Carlo
Mosimtec
Mott MacDonald
Murata
NavVis
NEC
NetApp
Netgear
Nikon
Nippon Ceramic
Nokia
Northrop Grumman
NTT
NTT Data
NuoDB
Nutanix
Nvidia
NXP
OEM Automatic
Okta
Omron
OpteamX
Oracle
Orange
Ordr
Ovaledge
Palantir
Palo Alto Networks
Panasonic
Percona
Philips
Pluto7
PostgreSQL
PTC
Pure Storage
Qorvo
Q-Tech
Qualcomm
Quanergy
Quanta Cloud Technology
Quectel
Rackspace Technologies
Rakuten
RapidMiner
Redis Labs
Renesas
Ricoh
Rockwell Automation
Rohm
Salesforce
Samsung Electro-Mechanics
Samsung Electronics
SAP
SAS
Saviant Consulting
Schneider Electric
ScienceSoft
Sensata
SenseTime
Shimadzu
Siemens
SiLC
SingTel
SK Telekom
Skyworks
Slingshot Simulations
Snowflake
Softbank
Software AG
Sony
Stemmer Imaging
STMicroelectronics
Stone Bond Technologies
Sunny Optical
Talend
Tata Communications
TCS
TDK
TE Connectivity
Teledyne
Telefónica
Telenor Group
Telstra
Tencent
Teradata
Tesla
Texas Instruments
Thales Group
Toshiba
Trend Micro
Tsinghua Unigroup
Tung Thih
TuSimple
TWI
Ubiquiti
Unity Technologies
Unlearn AI
Varjo
Velodyne
Veoneer
Veritas
Verizon
Visionaize
Visteon
Vodafone
Voyant Photonics
Vuzix
Willow
Winniio
Wipro
Xenonstack
Xiaomi
XMPro
ZF
ZTE

Table of Contents

Executive Summary

Players

Technology Briefing

Trends

Industry Analysis

Signals

Value Chain

Companies

Sector Scorecards

Glossary

Further Reading

Thematic Research Methodology

Frequently asked questions

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