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Market Research Report

Digital Twins in IoT: Market Strategies, Challenges & Future Outlook 2019-2023

Published by Juniper Research Ltd Product code 821209
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Digital Twins in IoT: Market Strategies, Challenges & Future Outlook 2019-2023
Published: April 9, 2019 Content info:


Juniper Research's ‘Digital Twins in IoT’ research provides a comprehensive evaluation of the origin, challenges, advantages, key drivers and future outlook of Digital Twins. It offers an in-depth assessment of select key players and industries which are offering and utilising Digital Twins respectively.

This research provides crucial insights for industries, particularly in manufacturing, transport and energy and utilities, among others seeking to deepen their understanding of the different strengths, opportunities and weaknesses of Digital Twin modelling solutions presented by key players:

  • General Electric
  • Oracle
  • Dassault Systèmes
  • Siemens
  • IBM
  • Bosch
  • SAP
  • PTC
  • Hitachi
  • Microsoft

It explores different regions and key markets championing the use of digital twins, and subsequently driving adoption of this technology, to enhance efficiency of their physical assets and processes.

This research suite includes:

  • Market Trends & Vendor Strategies (PDF)
  • 5-year Market Sizing & Forecast Spreadsheet (Excel)
  • 12 months' access to harvest online data platform

Key Features

  • Competitive Landscape: In-depth analysis of key players offering Digital Twins; Juniper explores:
    • The strengths of these players' Digital Twin offering business models
    • Industry experiences and assets, that are collectively supporting adoption of their Digital Twin modelling solutions by different industries, markets and/or regions
  • Market Dynamics: Strategic analysis of the major drivers, challenges and innovations shaping the adoption and development of Digital Twins including:
    • Enhancing the value of Digital Twins through technological innovation
    • The future strategic direction and market outlook for Digital Twins
    • Key issues organisations must address to implement Digital Twins effectively
  • Interviews: With significant players in the Digital Twins IoT ecosystem, including:
    • Siemens
    • Oracle
    • T-Systems
  • Sector Analysis: Analysis of Digital Twins revenues over 2018-2023, split by industry sectors:
    • Manufacturing
    • Energy & Utilities
    • Others (including Transport, Retail, Healthcare, Consumer Goods, etc)
  • Benchmark Industry Forecasts: Key forecasts for size and growth of the market, including:
    • Digital Twin Revenues, Split by Manufacturing, Energy & Utilities, and Others
    • Number of Industrial Companies deploying Digital Twins
    • Number of Manufacturing Companies Using Digital Twins
    • Number of Energy & Utility Companies Deploying Digital Twins
  • Juniper Leaderboard: 11 players offering Digital Twins compared, scored and positioned on the Juniper Leaderboard.

Key Questions

  • 1. Which industries and markets have the greatest adoption of Digital Twins in their IoT operations and why?
  • 2. Which verticals will generate the greatest revenues from Digital Twins by 2023?
  • 3. Which key players are offering Digital Twins IoT and what are the strengths and strategic opportunities of these players in this field?
  • 4. What are the challenges that industries must take into account when utilising Digital Twins?
  • 5. What will the value of the Digital Twin market be by 2023?

Companies Referenced

  • Included in Juniper Leaderboard: ANSYS, Bosch, DS (Dassault Systèmes), GE (General Electric), Hitachi, IBM, Microsoft, Oracle, PTC, SAP, Siemens.
  • Mentioned: 3DSIM, ABB, Accelrys, Accenture, Airbus, Akselos, Alibaba, Amazon, Apple, Aras, Archivideo, Atos, Autodesk, AVIC (Aviation Industry Corporation of China), Axeda, Bausch + Ströbel, Black & Decker, Boeing, BT, Capgemini, Cisco, Coca-Cola, Comau, Concur Technologies, Costco, DAQRI,, Delcross Technologies, Dell, Deloitte, DMDII (Digital Manufacturing and Design Innovation Institute), First Gen Philippines, Flowserve, Fraunhofer, Frustum, Gear Design Solutions, Google, Hausgeräte GmbH, Honeywell, HPE, ICONIC, iFogHorn, Industrial Internet Consortium, Johns Hopkins Hospital, Kaeser Kompresson, KPIT Medini Technologies, LICengineering, Maximo, McDermott International, Meridium, MIT, Mitsubishi, National Infrastructure Commission, National Instruments Corporation, National Research Foundation, Netsuite, Netzsch, Noble, OECD, OPTIS, OSI, OT, Pivotal, Plat.One, Predix, Red Bend Software, Rockwell Automation, RTT (Realtime Technology), Salesforce, Schneider Electric, Seeo, ServiceMax, Servigistics, Shell, Silver Lake, Sim & Cure, SoftBank, SpaceClaim, Standford University, Stara, Telecom Italia, Tesla, Texas Instruments, Three, T-Systems, United Nations, United Technologies, Varian, Vinci, Volkswagen, Vuforia, Vuzix.

Data & Interactive Forecast

Juniper's ‘Digital Twin’ forecast suite includes:

  • 5-year benchmark forecasts with key metrics by 8 key regions
  • Number of manufacturing, energy and utility companies deploying Digital Twins; Total Digital Twin Revenues generated by manufacturing, and energy and utilities, companies; and Average Spend per Company on Digital Twins according to verticals:
    • Manufacturing
    • Energy & Utilities
    • Others
  • Digital Twins Revenue, Average spend per Digital Twin, and number of companies deploying Digital Twins according to:
    • Industry (Manufacturing or Energy & Utilities)
    • Size of company (Large, Medium, Small)
  • Interactive Excel Scenario tool allowing the user to manipulate Juniper's data for 10 different metrics
  • Access to the full set of forecast data of 14 tables and more than 700 datapoints

Juniper Research's highly granular interactive Excels enable clients to manipulate Juniper's forecast data and charts to test their own assumptions using the Interactive Scenario Tool and compare select markets side by side in customised charts and tables. IFxls greatly increase clients' ability to both understand a particular market and to integrate their own views into the model.

Table of Contents

Table of Contents

1. Introduction to Digital Twins

  • 1.1. Introduction
    • Figure 1.1: Main Components of a Digital Twin Model
    • Table 1.2: 5 Main Aspects of Digital Twins in IoT
    • Table 1.3: Overview of Digital Twins Development, 2002-2018
    • Figure 1.4: Overview of Analytics Developments in Industrial IoT

2. Evolution of Digital Twins

  • 2.1. Evolution of Digital Twins
  • 2.2. Replicating the Physical Format of Objects
    • Figure 2.1: Digital Twins IoT: Physical Object vs Virtual Design
  • 2.3. The Value of Using Digital Twins in IoT
    • Figure 2.2: Key Benefits of Using Digital Twins in Manufacturing
  • 2.4. Key Factors Driving the Adoption of Digital Twins
    • Figure 2.3: Average Sensor Prices ($), 2004-2020
    • Table 2.4: Real Life Application Examples of Digital Twins in IoT

3. Challenges of Deploying Digital Twins

  • 3.1. Challenges of Implementing Digital Twins
    • Table 3.1: Major Building Blocks of Industrial IoT Platform Database
    • Table 3.2: Challenges & Solutions Overcome by Engineering Analytics in IoT

4. Future Outlook for Digital Twins

  • 4.1. Future Outlook for Digital Twins
    • 4.1.1. Overview of Recent Movements by Key Players
    • 4.1.2. Digital Twins Gaining Recognition in Small Companies
    • 4.1.3. Key Issues Surrounding Digital Twins
    • 4.1.4. Enhancing the Value of Digital Twins through Technological Innovation
    • 4.1.5. Key Regions Driving Growth of Digital Twin Adoption
      • Table 4.1: Major Countries Leading Digital Twin Modelling

5. Competitive Landscape for Digital Twins in the IIoT

  • 5.1. Introduction
    • Table 5.1: IIoT Player Capability Assessment Criteria
    • Table 5.2: Juniper Leaderboard: Industrial IoT Key Players
    • Figure 5.3: Juniper Leaderboard: IIoT companies offering Digital Twins
    • 5.1.1. Vendor Groupings
      • i. Established Leaders
      • ii. Leading Challengers
  • 5.2. Limitations & Interpretations
  • 5.3. Selected Vendor Profiles
    • 5.3.1. GE (General Electric)
      • i. Overview
        • Table 5.4: Select Digital, IoT developments of GE, 1950-2018
        • Table 5.5: GE Software & Data Analytics Developments, 2011 & 2016
      • ii. Deeper into Digital Twins with GE Predix Platform
        • Figure 5.6: Predix Propriety Platform Use of Digital Twin Modelling in IIoT
        • Table 5.7: Advantages of Using Predix Platform in IIoT
      • iii. Industrial Focus on the Cards for GE in 2019
        • Figure 5.8: GE Digital's IIoT solutions,
      • iv. Juniper's View: GE Key Strengths & Strategic Opportunities
    • 5.3.2. PTC
      • i. Overview
        • Table 5.9: PTC Key Acquisitions & Developments, 1996-2018
      • ii. Juniper's View: PTC Strengths & Strategic Opportunities
    • 5.3.3. Siemens
      • i. Overview
      • ii. Launch of MindSphere version 3
        • Figure 5.10: Key elements of MindSphere, 2018
      • iii. Siemens Acquires Mendix to Help Industrial Clients Adopt MindSphere
      • iv. Siemens Utilises Combination of Mendix & MindSphere to Boost Digital Twin Capabilities
      • v. Juniper's View: Siemens Strengths & Strategic Opportunities
    • 5.3.4. (Dassault Systèmes)
      • i.Overview
      • ii. Digital Twins: An Extension of DS' Engineering Strategy
        • Table 5.11: Select Acquisitions of Dassault Systèmes, 2013-
      • ii.Modelling Urban Environments
      • iv. Juniper's View: Dassault Systèmes Strengths & Strategic Opportunities
    • 5.3.5. Microsoft
      • i.Overview
        • Figure 5.12: Microsoft Azure Digital Twins IoT business model
        • Table 5.13: Microsoft's Partners in the Industrial IoT Sector
        • Table 5.14: Features, Issues & Development of HoloLens
      • ii. Juniper's View: Microsoft Strengths & Strategic Opportunities
    • 5.3.6. IBM
      • i. Overview
      • ii. Utilising Augmented Reality with Digital Twins
        • Table 5.15: Key Capabilities of Watson IoT Platform
        • Figure 5.15: IBM Cognitive Digital Twin Model, 2018
      • iii. Juniper's View: IBM Strengths & Strategic Opportunities
    • 5.3.7. SAP
      • i. Overview
        • Table 5.16: Key Developments of SAP Digital IoT Business Model
        • ii.SAP S/4HANA & Leonardo: Foundations of SAP's Digital Twin Model
          • Figure 5.17: SAP Leonardo Make Up, 2018
        • iii. SAP Predictive Engineering Insights
          • Figure 5.18: SAP Predictive Engineering Insights Structure
          • Figure 5.19: SAP Predictive Engineering Insights illustration
          • Figure 5.20: SAP Digital Twins Operating Model
        • iv. Strategic Alliances & Uptake of S/4 HANA
          • Table 5.21: Selected Industries Utilising SAP's Digital Twin Model
        • v. Entry into China
        • vi. Juniper's View: SAP Strengths & Strategic Opportunities
    • 5.3.8. Oracle
      • i. Overview
      • ii. Investing in Cloud Capabilities
        • Table 5.22: Select IoT Oracle Developments
        • Figure 5.23: Oracle's Digital Twin Model
      • iii. Leveraging Augmented Reality & Visual Data
        • Table 5.24: Oracle's Incorporations of AR,VR, Machine Vision & New Data Science
      • iv. Juniper's View: Oracle's Strengths & Strategic Opportunities
    • 5.3.9. Bosch
      • i. Overview
        • Table 5.25: Bosch Key Acquisitions to Boost IoT Capabilities, 2012-2015
      • ii. Eclipse Ditto: The Foundation of Bosch's Digital Twin of Service
        • Table 5.26: Bosch Eclipse Ditto Framework for Digital Twins
        • Figure 5.27: Bosch Eclipse Ditto Framework for Manging Digital Twins
      • iii. Juniper's View: Bosch's Strengths & Strategic Opportunities
    • 5.3.10. ANSYS
      • i. Overview
        • Table 5.28: ANSYS Key Acquisitions, 2001-2018
      • ii. Driving Uptake of ANSYS Digital Twin Simulation with AIM
      • iii. ANSYS Releases 19.1 Software to Enable Digital Twin Build Outs
      • iv. Additive Simulation Increases Usefulness of ANSYS R1
      • v. Juniper's View: ANSYS Strengths & Strategic Opportunities
    • 5.3.11. Hitachi
      • i. Overview
        • Figure 5.29: Hitachi Vantara Business Units, 2019
      • ii. Juniper's View: Hitachi's Strengths & Strategic Opportunities

6. Digital Twins IoT Market Forecast Summary

  • 6.1. Introduction
  • 6.2. Methodology & Assumptions
    • 6.2.1. Industrialised Markets & Regions & Key IoT Developments in Vertical Sectors
      • Figure 6.1: Digital Twins Forecasting Methodology
  • 6.3. IoT Connected Devices Summary
    • 6.3.1. Total IoT Connected Devices
      • Figure & Table 6.2: Installed Base IoT Units (m), Split by 8 Key Regions 2018-2023
  • 6.4. Total IoT Software Platform Revenue
    • 6.4.1. Software Revenue
      • Figure & Table 6.3: Total IoT Software Platform Revenue ($bn) Split by 8 Key Regions 2018-2023
  • 6.5. Revenue Summary
    • 6.5.1. IoT Unit Hardware Revenue
      • Figure & Table 6.4: Annual IoT Unit Hardware Revenue ($m), Split by 8 Key Regions 2018-2023
  • 6.6. Digital Twin Revenues by Sector
    • Figure & Table 6.5: Digital Twin Revenues ($m), Split by 3 Key Sectors, 2018-2023
  • 6.7. Total Market Summary
    • 6.7.1. Total Revenues of Industrialised Companies Deploying Digital Twins
      • Figure & Table 6.6: Total Revenues of Industrialised Companies using Digital Twins ($m), Split by 8 Key Regions 2018-2023
    • 6.7.2. Average Spend Per Industrialised Company on deploying Digital Twins
      • Figure& Table 6.7: Average Spend Per Industrialised Company on Digital Twins ($) Split by 8 Key Regions 2018-2023
    • 6.7.3. Total Number of Manufacturing Companies using Digital Twins
      • Figure & Table 6.7: Total Number of Manufacturing Companies Deploying Digital Twins, Split by 8 Key Regions 2018-2023
    • 6.7.4. Total Revenue of Manufacturing Companies using Digital Twins
      • Figure & Table 6.8: Total Revenue by Manufacturing Companies using Digital Twins ($bn), Split by 8 Key Regions 2018-2023
    • 6.7.5. Average Spend Per Manufacturing Company Deploying Digital Twins
      • Figure & Table 6.9: Average Spend per Manufacturing Company Deploying Digital Twins ($),Split by 8 Key Regions 2018-2023
    • 6.7.6. Total Number of Energy & Utility Companies Deploying Digital Twins
      • Figure & Table 6.10: Total number of Energy & Utility Companies Deploying Digital Twins, Split by 8 Key Regions 2018-2023
    • 6.7.7. Total Revenue of Energy & Utility Companies Deploying Digital Twins
      • Figure & Table 6.11: Total Spend by Energy & Utility Companies Deploying Digital Twins ($bn), Split by 8 Key Regions 2018-2023
    • 6.7.8. Average Spend Per Energy & Utility Company Deploying Digital Twins
      • Figure & Table 6.12: Average Spend per Energy & Utility Company Deploying Digital Twins ($), Split by 8 Key Regions 2018-2023
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