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製造業向けデジタルツイン技術の世界市場 (2025-2032年)

Global Digital Twin Technology in Manufacturing Market- 2025-2032

出版日
ページ情報
英文 180 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=144.06円
製造業向けデジタルツイン技術の世界市場 (2025-2032年)
出版日: 2025年04月10日
発行: DataM Intelligence
ページ情報: 英文 180 Pages
納期: 即日から翌営業日
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  • 概要
  • 目次
概要

世界の製造業向けデジタルツイン技術の市場規模は、2024年の164億5,000万米ドルから、予測期間中はCAGR 60.20%で推移し、2032年には7,136億1,000万米ドルに達すると予測されています。

製造業向けデジタルツイン技術の世界市場は、インダストリー4.0、IoT、AI技術の採用増加により、急速な成長を遂げています。デジタルツインは、リアルタイムのモニタリング、予知保全、意思決定の改善を可能にし、業務効率を高め、ダウンタイムを削減します。製造業者はこの技術を活用して、生産プロセスのシミュレーション、最適化、合理化を図っています。

世界の製造業向けデジタルツイン技術の動向

AIやIoTとの統合は、製造業向けデジタルツイン技術の成長を促進する重要な動向です。AIは、過去のデータとリアルタイムのデータから学習することで、予測分析とリアルタイムの意思決定を可能にします。IoTデバイスは継続的なセンサーデータをデジタルツインに送り込み、精度と応答性を高めます。

市場力学

インダストリー4.0とスマートマニュファクチャリングの採用

インダストリー4.0およびスマートマニュファクチャリングの導入が、製造業におけるデジタルツイン技術の世界市場の成長を大きく後押ししています。工場がより接続され、インテリジェントになる中で、デジタルツインは生産プロセスのリアルタイム監視、シミュレーション、最適化を可能にします。9th Annual Rockwell State of Smart Manufacturing Reportでは、業界全体の大きなシフトが示されており、参加者の95％がスマートマニュファクチャリング技術の導入を計画していると報告されています。

これは、デジタルツインのようなデジタルツールに対する効率性やレジリエンス（回復力）向上への期待が高まっていることを反映しています。このトレンドは、よりスマートでデータ駆動型の生産環境への加速的な変革を示しています。IoTデバイスの導入拡大や高度な分析技術の進展により、デジタルツインは予知保全や柔軟な運用のためのデータ基盤を提供します。これらの機能は、スマートマニュファクチャリングが目指す自動化、カスタマイズ、生産効率の向上といった目標と完全に一致しています。

当レポートでは、世界の製造業向けデジタルツイン技術の市場を調査し、市場概要、市場成長への各種影響因子の分析、各種区分・地域/主要国別の詳細分析、主要企業のプロファイルなどをまとめています。

目次

第1章 調査手法・調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
    • 抑制要因
    • 機会
    • 影響分析

第5章 業界分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制およびコンプライアンス分析
  • 持続可能性分析
  • 技術進歩分析
  • DMIオピニオン

第6章 タイプ別

  • 製品デジタルツイン
  • プロセスデジタルツイン
  • システムデジタルツイン

第7章 企業規模別

  • 中小企業
  • 大企業

第8章 用途別

  • 予知保全
  • パフォーマンス監視
  • 製品設計・開発
  • ビジネス最適化
  • その他

第9章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他
  • 南米
    • ブラジル
    • アルゼンチン
    • その他
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他
  • 中東・アフリカ

第10章 企業プロファイル

  • Dassault Systemes SE
  • TIBCO Software Inc.
  • Siemens AG
  • Microsoft Corporation
  • Autodesk Inc.
  • Hexagon AB
  • Oracle Corporation
  • Altair Engineering Inc.
  • IBM Corp.
  • aPriori Technologies, Inc.

第11章 付録

目次
Product Code: ICT9472

Global digital twin technology in manufacturing market reached US$ 16.45 billion in 2024 and is expected to reach US$ 713.61 billion by 2032, growing with a CAGR of 60.20% during the forecast period 2025-2032.

The global digital twin technology in manufacturing market is experiencing rapid growth, driven by the increasing adoption of Industry 4.0, IoT, and AI technologies. Digital twins enable real-time monitoring, predictive maintenance, and improved decision-making, enhancing operational efficiency and reducing downtime. Manufacturers are leveraging this technology to simulate, optimize, and streamline production processes.

Global Digital Twin Technology in Manufacturing Market Trend

Integration with AI and IoT is a key trend driving the growth of Digital Twin technology in manufacturing. AI enables predictive analytics and real-time decision-making by learning from historical and real-time data. IoT devices feed continuous sensor data into digital twins, enhancing their accuracy and responsiveness.

In October 2024, Ola Electric launched its advanced Ola Digital Twin platform to revolutionize manufacturing and product development processes. Built on Nvidia's Omniverse platform, it leverages AI, simulation, and IoT technologies to create digital replicas of real-world environments. This innovation aims to optimize factory planning and equipment layout and accelerate product development cycles, marking a major step in Ola's tech-driven growth strategy.

Dynamics

Industry 4.0 and Smart Manufacturing Adoption

The adoption of Industry 4.0 and smart manufacturing is significantly driving the growth of the global digital twin technology market in manufacturing. As factories become more connected and intelligent, digital twins enable real-time monitoring, simulation, and optimization of production processes. The 9th Annual Rockwell State of Smart Manufacturing Report highlights a strong industry shift, with 95% of participants planning to adopt smart manufacturing technologies.

This reflects growing confidence in digital tools like digital twins to enhance efficiency and resilience. The trend underscores the accelerating transformation toward smarter, data-driven production environments. With the increased deployment of IoT devices and advanced analytics, digital twins provide the data foundation for predictive maintenance and agile operations. These capabilities align perfectly with the goals of smart manufacturing, such as automation, customization, and efficiency.

Data Privacy and Security Concerns

Data privacy and security concerns are significantly restraining the growth of the global digital twin technology in the manufacturing market. Digital twins rely on real-time data from physical assets, which often includes sensitive operational and proprietary information. Unauthorized access or cyberattacks can lead to data breaches, intellectual property theft, and operational disruptions.

A study by HiddenLayer, AI Threat Landscape Report 2024, reveals that AI security breaches are a growing concern, with 77% of businesses reporting an AI-related breach in the past year. These risks make manufacturers hesitant to adopt the technology. Ensuring end-to-end security in interconnected systems is both technically challenging and costly.

Segment Analysis

The global digital twin technology in manufacturing market is segmented based on type, enterprise size, application and region.

Predictive Maintenance Drives a Significant Share by Enhancing Operational Efficiency and Reducing Downtime

The growing need for predictive maintenance is a major driver of the global digital twin technology in manufacturing. Manufacturers increasingly use digital twins to simulate and monitor the real-time condition of machines and equipment. This enables early detection of wear, anomalies, or failures, allowing proactive maintenance before costly breakdowns occur. For instance, implementing digital twins for predictive maintenance can reduce downtime by up to 30% and extend equipment lifespan, resulting in substantial cost savings for manufacturers.

By reducing unplanned downtime and extending asset life, predictive maintenance directly contributes to higher operational efficiency and cost savings. Digital twins also help in scheduling maintenance activities without disrupting production workflows. The ability to forecast issues based on data-driven insights makes them indispensable in high-value, asset-intensive industries.

Geographical Penetration

High Technology Adoption and Strong Presence of Key Industry Players in North America

North America holds a significant share of the digital twin technology in the manufacturing market due to its strong industrial base and early adoption of advanced technologies. The region is home to major players like General Electric, IBM Corp., and Microsoft Corp., which are actively investing in digital twin innovations. High levels of automation in manufacturing, combined with a robust IT infrastructure, further accelerate the implementation of these solutions.

Government support for smart manufacturing initiatives also boosts growth. In November 2024, Binghamton University joined forces with industry and academic partners in a landmark US$285 million federal initiative to boost US semiconductor manufacturing through digital twin technology. Backed by the US Department of Commerce, the institute will focus on advancing semiconductor design and production. This marks a major step toward strengthening domestic innovation and education in the semiconductor sector.

Technological Advancement Analysis

Digital Twin technology in manufacturing leverages real-time data, IoT, AI, and advanced simulation tools to create virtual replicas of physical assets, enabling enhanced monitoring, diagnostics, and predictive maintenance. The integration of 5G and edge computing boosts data transmission and processing capabilities, making real-time insights more accessible. AI and machine learning algorithms continuously optimize performance by analyzing historical and live data. Cloud platforms support scalability and remote collaboration across global facilities.

Competitive Landscape

The major global players in the market include Dassault Systemes SE, TIBCO Software Inc., Siemens AG, Microsoft Corporation, Autodesk Inc., Hexagon AB, Oracle Corporation, Altair Engineering Inc., IBM Corp., aPriori Technologies, Inc. and others.

Key Developments

  • In January 2025, Siemens AG unveiled groundbreaking advancements in industrial AI and digital twin technology at CES 2025. Notably, they collaborated with JetZero to simulate aircraft manufacturing operations using comprehensive digital twins. This approach aims to de-risk the manufacturing process and validate methodologies before physical implementation.
  • In January 2025, SPX FLOW announced a collaboration with Siemens to showcase cutting-edge digital twin technology at the MxD (Manufacturing x Digital) Center in Chicago. This partnership aims to drive innovation in manufacturing across various industries, including food and beverage, chemicals, and batteries.

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  • Value of DataM Reports: Our reports offer specialized insights tailored to the latest trends and specific business inquiries. This personalized approach provides a deeper, strategic perspective, ensuring you receive the precise information necessary to make informed decisions. These insights complement and go beyond what is typically available in generic databases.

Target Audience 2024

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

  • 3.1. Snippet by Type
  • 3.2. Snippet by Enterprise Size
  • 3.3. Snippet by Application
  • 3.4. Snippet by Region

4. Dynamics

  • 4.1. Impacting Factors
    • 4.1.1. Drivers
      • 4.1.1.1. Industry 4.0 and Smart Manufacturing Adoption
    • 4.1.2. Restraints
      • 4.1.2.1. Data Privacy and Security Concerns
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory and Compliance Analysis
  • 5.5. Sustainability Analysis
  • 5.6. Technological Advancement Analysis
  • 5.7. DMI Opinion

6. By Type

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 6.1.2. Market Attractiveness Index, By Type
  • 6.2. Product Digital Twin*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Process Digital Twin
  • 6.4. System Digital Twin

7. By Enterprise Size

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
    • 7.1.2. Market Attractiveness Index, By Enterprise Size
  • 7.2. Small & Medium Enterprises (SMEs)*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Large Enterprises

8. By Application

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 8.1.2. Market Attractiveness Index, By Application
  • 8.2. Predictive Maintenance*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Performance Monitoring
  • 8.4. Product Design & Development
  • 8.5. Business Optimization
  • 8.6. Others

9. By Region

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 9.1.2. Market Attractiveness Index, By Region
  • 9.2. North America
    • 9.2.1. Introduction
    • 9.2.2. Key Region-Specific Dynamics
    • 9.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 9.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
    • 9.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 9.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 9.2.6.1. US
      • 9.2.6.2. Canada
      • 9.2.6.3. Mexico
  • 9.3. Europe
    • 9.3.1. Introduction
    • 9.3.2. Key Region-Specific Dynamics
    • 9.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 9.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
    • 9.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 9.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 9.3.6.1. Germany
      • 9.3.6.2. UK
      • 9.3.6.3. France
      • 9.3.6.4. Italy
      • 9.3.6.5. Spain
      • 9.3.6.6. Rest of Europe
  • 9.4. South America
    • 9.4.1. Introduction
    • 9.4.2. Key Region-Specific Dynamics
    • 9.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 9.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
    • 9.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 9.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 9.4.6.1. Brazil
      • 9.4.6.2. Argentina
      • 9.4.6.3. Rest of South America
  • 9.5. Asia-Pacific
    • 9.5.1. Introduction
    • 9.5.2. Key Region-Specific Dynamics
    • 9.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 9.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
    • 9.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 9.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 9.5.6.1. China
      • 9.5.6.2. India
      • 9.5.6.3. Japan
      • 9.5.6.4. Australia
      • 9.5.6.5. Rest of Asia-Pacific
  • 9.6. Middle East and Africa
    • 9.6.1. Introduction
    • 9.6.2. Key Region-Specific Dynamics
    • 9.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Type
    • 9.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
    • 9.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

10. Company Profiles

  • 10.1. Dassault Systemes SE*
    • 10.1.1. Company Overview
    • 10.1.2. Product Portfolio and Description
    • 10.1.3. Financial Overview
    • 10.1.4. Key Developments
  • 10.2. TIBCO Software Inc.
  • 10.3. Siemens AG
  • 10.4. Microsoft Corporation
  • 10.5. Autodesk Inc.
  • 10.6. Hexagon AB
  • 10.7. Oracle Corporation
  • 10.8. Altair Engineering Inc.
  • 10.9. IBM Corp.
  • 10.10. aPriori Technologies, Inc.

LIST NOT EXHAUSTIVE

11. Appendix

  • 11.1. About Us and Services
  • 11.2. Contact Us