Digital Twin Market Size, Analysis & Global Industry Expected to Grow at a CAGR of 24.7% during the Forecast Period 2017-2023

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Globalization has made companies to focus more on digitalization both in the business and consumer market places. Over the past few years, IoT has been reaching milestones in the world of technology and it has paved the path for the evolution of various new technologies for enterprises. Digital twin is one such technology which is changing the industry dynamics. Digital twin refers to simulation modules of a physical object.

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This technology is capable of supporting conceptualization, comparison, and collaboration for high-end innovations and problem-solving. Further, the technology provides real-time status and working conditions of the physical objects by accessing data from the sensors installed in the objects. Industry players benefit from this technology in terms of efficiency, higher productivity, lean manufacturing, and cost-effectiveness.

Digital twin works on a simulation platform connected to a predictive analytics platform and it integrates several manufacturing assets, underlying parts, and system processes in real-time with the help of sensors and communicates to the operator in a simulation module. The system senses data from a wide range of devices/machines and analyzes the work. Digital twin is currently used for reducing operational loss, designing and repairing of airplanes and turbines, controlling supply chain loss in a manufacturing plant, and others.

Market Revenue and Segmentation Analysis:
The digital twin market is expected to grow at a CAGR of 24.7% during the forecast period 2017-2023. The market is analyzed based on digital twin types, verticals, deployment type, applications, and regions. The digital twin types considered are product twin, part twin, process twin, and system twin, wherein the process and systems twins are at a high demand in the end-user industries owing to its wide functionalities. The deployment type segment includes, cloud-based, on-premises, and hybrid. Currently, cloud deployment is expected to contribute a major share followed by the on-premises and hybrid during the forecast period 2017-2023.

The applications covered are business optimization, operation optimization, asset performance management, edge computing, cyber security, and others. The business and process optimizations are set to bring the innovations in the digital twin technology as they control supply chain loss and offer better lean manufacturing capabilities and data driven solutions to improve the productivity.

Regions and Vertical Analysis:
The regions covered in report are Americas, Europe, Asia Pacific, and ROW. Among these, Americas is expected to grow at a higher CAGR during the forecast period 2017-2023. The US, Canada, the UK, Germany, and Japan are expected to adopt more digital twin technologies, and Brazil, Switzerland, and Poland are the emerging countries for the digital twin technology adoption owing to the policy changes for the development of their manufacturing capabilities and the rising trend for spending on industrial digital technologies.

Digital twin is used across industries, such as manufacturing, power, oil & gas, BFSI, healthcare, aviation, smart cities, and others. The manufacturing industry could receive a wide range of benefits from digital twin, from product designing to monitoring the plant operations, load forecasting, fleet dispatch, monitoring production losses, and expected delays in the production and others. In aviation and power industries, digital twin is used to monitor the working of machines such as propellers, turbines, and others. Predictive diagnostics, asset condition monitoring, and finding new revenue streams are the popular functions of digital twin in the end-user industries.

Benefits and Vendors
The report contains an in-depth analysis of vendor profiles, which include financial health, business units, key business priorities, SWOT, strategy, and views; and competitive landscape. Digital twin technology is expected to change the way industries work by 2030 with its wide range of capabilities. The report covers an in-depth analysis of Infosys, General Electric, Siemens, AT & T, IBM, and others.
The study offers a comprehensive analysis of the “Global Digital Twin Market”. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.

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Some points from TOC:

1 Industry Outlook 9
1.1 Industry Overview 9
1.2 Industry Trends 10
1.3 PEST Analysis 11

2 Report Outline 12
2.1 Report Scope 12
2.2 Report Summary 13
2.3 Research Methodology 14
2.4 Report Assumptions 15

3 Market Snapshot 16
3.1 Total Addressable Market 16
3.2 Segmented Addressable Market 17

4 Market Outlook 18
4.1 Overview 18
4.2 Porter 5 (Five) Forces 19

5 Market Characteristics 20
5.1 Evolution 20
5.2 Ecosystem 21
5.3 Market Segmentation 22
5.4 Market Dynamics 23
5.4.1 5.4.1 Drivers 24
5.4.1.1 Digitalization- a strategy to enhance productivity 24
5.4.1.2 Early warning, prediction, and optimization 24
5.4.1.3 Virtual marketing promotions 24
5.4.2 5.4.2 Restraints 24
5.4.2.1 Delay in implementing 3D design technologies 24
5.4.2.2 Interoperability and security 24
5.4.3 5.4.3 Opportunities 24
5.4.3.1 Cognitive digital twin 24
5.4.3.2 Expanding the boundaries of product design technologies 25
5.5 DRO – Impact Analysis 26
5.5.1 5.6 Market Trends 27

6 Deployment Type: Market Size and Analysis 28
6.1 Overview 28
6.2 Cloud 28
6.3 On-premises 29
6.4 Hybrid 30

7 Digital twin type: Market Size and Analysis 31
7.1 Overview 31
7.2 Part twin 31
7.3 Product twin 32
7.4 Process twin 32
7.5 System twin 33

8 Applications: Market Size and Analysis 34
8.1 Overview 34
8.2 Business optimization 35
8.3 Operations Optimization 35
8.4 Asset Performance Management 36
8.5 Edge Computing 37
8.6 Cyber Security 38
8.7 Others 38

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