1.1 Research Scope

1.2 Research Methodology

1.3 Explanation of Research Methodology

1.4 Summary of Key Findings

1.4 Summary of Key Findings (continued)

2.1 Digital Twin – Brief Overview

2.2 Evolution of DT

2.3 Segmentation of the Virtual Descriptive World

2.4 Digital Twin: Benefits and Use Case

2.5 Step by Step Transformation of Manufacturing using DT: Product Impact

2.6 Digital Twin in a Production Line

2.7 Factors Driving Adoption

2.7 Factors Driving Adoption (continued)

2.8 Adoption Barriers

2.9 Technology Convergence Leads to Digital Twin

3.1 Three Types of Digital Twins

3.2 ANSYS’ Digital Twin Platform

3.3 Hero MotoCorp Digital Twin Project

3.4 Veerum Digital Twin Solution for the Oil and Gas industry

3.5 Precognize Machine Learning Platforms

3.6 DVN GL Digital Twin for Oil and Gas

3.7 Maplesoft and Rockwell Automation – Project Sherlock AI

3.8 Key Innovations Shaping the Future

3.9 Other Major Players

3.10 Businesses Swiftly Managing What-If Scenarios

4.1 Analyst Viewpoint

4.2 Should You Opt for Digital Change? Is it Right for You?

4.3 What Sort of Skills Companies Need to Acquire to Make the Transition?

4.4 What Should Manufacturers Do for a Implementing a Successful Digitized Infrastructure?

4.5 What Business Value will Digital Twin Bring to Companies?

4.6 Digital Twin is part of the Digital Thread

5.1 Key Patents on AR and Digital Twin

5.2 Key Patents on Machine Learning in Manufacturing Sector

5.2 Key Patents on Machine Learning in Manufacturing Sector (continued)

5.3 Key Contacts

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