設計與工程軟體實施：2026年

這份 150 頁的報告重點在於設計和工程軟體市場，著重介紹了 MBSE、左移、雲端、人工智慧和數位線程的採用情況。

本報告解答的主要問題

• 什麼是設計與工程（D&E）？它涵蓋哪些軟體類別（MCAD/ECAD、模擬/CAE、PLM、SE/ALM、雲端平台）？
• D&E為何以及如何改變以滿足新的客戶需求？
• 什麼是系統工程？它面臨哪些挑戰？ MBSE 如何解決這些挑戰？
• 什麼是左移工程？它的實施趨勢、優勢和挑戰是什麼？
• 發展與就業（D&E）領域的雲端採用進展如何？哪些工作負載正在遷移到雲端，哪些將繼續保留在本地？
• 人工智慧在設計與工程領域扮演著怎樣的角色？包括生成式人工智慧和基於代理的人工智慧？此外，工程領域對人工智慧應用的信心還有多大的延遲？
• 哪些數位線程趨勢正在影響設計與工程（D&E）？整合進展到什麼程度？還存在哪些挑戰？
• 在設計與工程的各個類別中，哪些供應商處於領先地位？他們佔怎樣的市場地位？他們最近的發展情況如何？
• 採用率如何因產業和公司規模而異？
• 本調查的結果是基於哪些調查對象和調查方法？
• 到 2027 年，D&E 領域需要關注的五個關鍵點是什麼？

上市公司

以下是本報告中提及的部分公司。

• AWS
• Accenture
• Altair
• Ansys
• Atlassian
• Autodesk
• Cadence
• Dassault Systemes
• GitHub
• Google
• Hexagon
• IBM
• MathWorks
• Microsoft
• NVIDIA
• Oracle
• PTC
• SAP
• Siemens
• Synopsys

關於本報告

產品設計和工程流程正從線性工作流程轉向整合系統模型架構。隨著硬體產品的數位化、持續的售後軟體更新以及嵌入式智慧的普及，製造商被迫在整個產品生命週期中整合機械、電氣和軟體領域。

本報告對整個產品開發生命週期中的軟體採用進行了系統分析。報告檢視了五個關鍵軟體類別的市場狀況：電腦輔助設計 (CAD)、電腦輔助工程 (CAE)、產品生命週期管理 (PLM)、軟體開發生命週期 (SDLC)/應用生命週期檢驗(ALM) 以及設計與工程雲端平台。

報告摘要

• 實施狀態報告：這份 148 頁的報告詳細介紹了 MBSE、左移工程、雲端平台、AI數位線程的實施狀態。
• 相關利益者洞察：資料來自 16 個國家的 120 家硬體製造商，包括工程決策者、研發 (R&D) 領導者和 IT 架構師。
• 市場預測：本報告概述了 CAD、CAE、PLM 和雲端平台服務的市場規模和競爭領導地位數據，直至 2032 年（SDLC 的市場規模未進行定量計算）。
• 策略展望：已確定到 2027 年工程軟體發展的五個關鍵重點領域。

目錄

設計與工程軟體實施報告：2026

第1章摘要整理

第2章：引言

第3章 背景：設計與工程軟體市場及競爭格局

• 背景：設計與工程軟體市場及競爭格局：章節概述
• 市場定義與細分
• D&E軟體市場預測：依軟體類別分類
• 按類別分類的D&E軟體市場佔有率
• 競爭趨勢：近年來，產業整合不斷推進，同時湧現許多人工智慧原生新興企業。
• 西門子在設計與工程領域的關注點
• 達梭系統專注於 D&E 領域
• 使用佔有率：受訪企業所使用的供應商。
• 使用率佔有率：高使用率供應商的優勢
• 設計軟體公司簡介1：達梭系統
• 設計軟體公司簡介 2：歐特克
• 設計軟體公司簡介 3：西門子
• 設計軟體公司簡介 4：PTC
• 模擬軟體公司簡介 1：Ansys（已被 Synopsys 收購）
• 模擬軟體公司簡介 2：MathWorks
• 模擬軟體公司簡介 3：西門子
• 模擬軟體公司簡介 4：Altair（已被西門子收購）
• PLM軟體公司簡介1：PTC
• PLM軟體公司簡介2：西門子
• PLM軟體公司簡介3：SAP
• PLM 軟體公司簡介 4：達梭系統
• PLM軟體公司簡介5：歐特克
• 軟體開發生命週期 (SDLC) 平台公司簡介 1：Atlassian
• 軟體開發生命週期 (SDLC) 平台公司簡介 2：GitHub
• 軟體開發生命週期 (SDLC) 平台公司簡介 3：GitLab
• 雲端平台公司簡介 1：微軟
• 雲端平台公司簡介 2：AWS
• 雲端平台公司簡介 3：Google雲

第4章：系統工程與模型為基礎的系統工程簡介

• 系統工程與基於模型的系統工程：章節概述
• 軟體工程和模型驅動軟體工程的基礎知識
• MBSE：MBSE 的主要特點
• 軟體和硬體開發之間的協作
• 根本原因是軟硬體協調性差。
• 軟體工程中的其他挑戰
• 任務
• MBSE在製造業的應用案例研究：西門子醫療
• MBSE展望：從人工主導的MBSE轉向AI增強型MBSE的過渡

第5章：左移工程簡介

• 左移工程：章節總結
• 左移工程：概述
• 左移法的介紹
• 左移法的優勢
• 透過左移實現工程資訊的共用
• 左移法的成熟
• 左移法的挑戰
• 供應商和實施公司對左移工程的使用

第6章：雲端採用

• 雲採用：章節概述
• 在D&E工具鏈的每個元件中雲端部署
• D&E 工作負載的雲端採用前景
• 目前D&E工作負載的雲端採用狀況：按地區/產業/公司規模分類
• 案例研究：西門子的雲和SaaS策略
• 遷移到雲端時需要考慮的因素
• 資料安全性和合規性是遷移到雲端時需要考慮的關鍵因素。
• 關於雲端遷移的擔憂
• 雲端遷移可能性較低的工程工作負載

第7章：人工智慧導論

• 人工智慧實施：章節概述
• 人工智慧在設計與工程中的作用
• 人工智慧在設計與工程領域的附加價值
• 對人工智慧工具的信任
• 生成式人工智慧在設計與工程的應用
• 研發供應商的生成式人工智慧實施案例：PTC 與西門子
• D&E 中的基於代理的人工智慧
• 基於代理的AI工具在設計與工程工作流程中的應用：案例研究：Cadence和Ansys
• 基於代理的人工智慧在設計與工程領域的附加價值
• 對基於代理的人工智慧的信任
• 在設計與工程中編配基於代理的人工智慧
• 基於代理的 AI 範例：在整個 D&E 系統中發揮編配作用的 AI 代理。
• D&E作為協調者的切入點：超大規模資料中心業者資料中心營運商

第8章：數位線程簡介

• 數位線程簡介：章節概述
• 什麼是數位線程？
• 數位線程實施現狀
• 引入數位線程的障礙
• 數位線程的關鍵挑戰
• 應對數位線程挑戰的解決方案
• 數位線程相關工具和技術的採用現狀
• 採用數位線程相關工具和技術的障礙
• 在數位線程中引入雲端和 API
• 跨越不同供應商工具的數位線程
• 數位線程產品和服務：案例研究：西門子和AWS

第9章展望

• 設計和工程軟體的未來
• 趨勢

第10章：調查方法

第11章：關於物聯網分析

A 150-page report on the design and engineering software market, with a focus on the adoption of MBSE, shift-left, cloud, AI, and digital threads.

Questions answered

• What is Design & Engineering (D&E), and which software categories does it span (MCAD/ECAD, simulation/CAE, PLM, SE/ALM, cloud platforms)?
• Why and how is D&E changing due to new customer demands?
• What is systems engineering, what challenges are associated with it, and how does MBSE address them?
• What is shift-left engineering, and what are its adoption trends, benefits, and challenges?
• How is cloud being adopted in D&E, which workloads are migrating, and which will stay on-premises?
• What is the role of AI in D&E, including generative and agentic AI? And how far does engineering trust lag adoption?
• What digital thread trends are shaping D&E, what is their level of integration, and what challenges persist?
• Who are the leading vendors across D&E categories, what positions do they hold, and what recent developments have they introduced?
• How does adoption differ by industry and company size?
• What is the survey base and methodology behind the findings?
• What are the 5 things to watch in D&E through 2027?

Companies mentioned

A selection of companies mentioned in the report.

• AWS
• Accenture
• Altair
• Ansys
• Atlassian
• Autodesk
• Cadence
• Dassault Systemes
• GitHub
• Google
• Hexagon
• IBM
• MathWorks
• Microsoft
• NVIDIA
• Oracle
• PTC
• SAP
• Siemens
• Synopsys

About the report

Product design and engineering processes are shifting from linear workflows to integrated systems model frameworks. The digitalization of hardware products, ongoing post-shipment software updates, and embedded intelligence correlate with the need for manufacturers to align mechanical, electrical, and software domains across the product lifecycle.

The Design & Engineering Software Adoption Report 2026 provides a structured analysis of software adoption across the product development lifecycle. It examines the market across 5 primary software categories: Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), Product Lifecycle Management (PLM), Software Development Lifecycle (SDLC)/Application Lifecycle Management (ALM), and D&E Cloud Platforms.

The research relies on a survey of hardware manufacturers conducted in May 2025 and June 2025, supplemented by in-depth interviews with industry experts. The respondent sample comprises OEMs and component suppliers with over 1,000 employees across 16 countries in North America, Europe, and Asia-Pacific.

Report at a glance

• Adoption report: Details the adoption of MBSE, shift-left engineering, cloud platforms, AI, and digital threads across 148 pages.
• Stakeholder insights: Comprises data from 120 hardware manufacturers, including engineering decision-makers, R&D leaders, and IT architects across 16 countries.
• Market forecasts: Outlines market size and competitive leadership data across CAD, CAE, PLM, and Cloud Platform Services through 2032 (SDLC is not sized quantitatively)
• Strategic outlook: Identifies 5 critical watch items in engineering software through 2027.

Key topics of the report

• Introduction to design & engineering: Details the technical backbone of the product development lifecycle and examines key strategic outcomes: differentiation, speed, innovation, and customer-centricity. It outlines the transition from traditional linear workflows to systems engineering and MBSE.
• Market & competitive landscape: Details the expansion of the overall D&E software market and its projected growth trajectory. Outlines market share distribution across categories, identifying category leaders such as Dassault Systemes, Ansys, PTC, and Microsoft.
• Systems engineering & MBSE adoption: Analyzes the transition from document-based systems engineering to model-based methods. Highlights how organizations coordinate software sprint cycles with hardware milestones, while a smaller portion directly connect software lifecycle tools like Atlassian Jira or GitLab to PLM systems. Foremost among integration obstacles is a lack of shared systems for traceability.
• Shift-left engineering adoption: Examines the adoption of simulation-driven design at concept stages, noting its broad institutionalization among manufacturers. Correlates shift-left practices with notable R&D domain benefits in mechanical and electrical design, while highlighting a lack of shared simulation models as a primary implementation challenge.
• Cloud adoption: Analyzes toolchain deployment models across cloud, hybrid, and on-premises environments. Source-code management leads cloud adoption, whereas ECAD/PCB design trails behind. Demonstrates that data security and compliance remain top priorities when evaluating cloud migration, with intellectual property protection emerging as a primary concern.
• AI adoption: Details expected value and current trust levels for generative AI and agentic AI tools. While there is a widespread expectation for AI to be embedded into CAD, PLM, and ALM platforms, high trust in AI outputs remains limited, particularly for LLM-generated documentation and AI-based part design. Identifies potential orchestrators for AI-powered engineering workflows, led predominantly by traditional CAx/PLM vendors.
• Digital thread adoption: Evaluates data continuity across engineering disciplines, finding the strongest integration between MCAD and simulation, and the weakest between ECAD and MCAD. Parallel to this, findings highlight ongoing bill of materials (BOM) misalignment across PLM, ERP, and MES systems.
• Market outlook: Identifies critical strategic themes for the near future, analyzing AI trust verification, platform orchestration competition, hybrid cloud deployment, common data model adoption, and engineering workforce skill requirements.

A data-driven foundation for key business functions

• Strategy & corporate development: Inform strategic planning and portfolio decisions with market projections through 2032, vendor consolidation analyses, and evaluation of incumbent versus hyperscaler orchestration platforms.
• Product management & marketing: Guide product roadmaps and positioning using adoption data on generative AI, shift-left simulation toolchains, and domain-specific integration requirements.
• Sales leaders & account managers: Identify buyer pain points and qualify account readiness using data on BOM misalignment rates, PLM-ALM traceability gaps, and workload-specific cloud migration preferences.
• R&D & engineering leadership: Benchmark internal engineering workflows against industry standards for software-hardware synchronization, digital thread maturity, and MBSE implementation.
• Engineering IT & toolchain architects: Direct technical architecture priorities with insights into cross-domain data federation, cloud deployment security concerns, and AI orchestration preferences.

Table of Contents

Design & Engineering Adoption Report 2026

1. Executive summary

2. Introduction

• Introduction: Chapter overview
• Starting point: Design and engineering serves as the technical and creative backbone of the product development lifecycle
• Why does D&E matter?…. It helps manufacturers achieve strategic objectives
• But what gets built has fundamentally changed in recent years
• These shifts place new demands on D&E teams
• Traditional D&E cannot meet these demands so it is giving way to SE and MBSE as new paradigms
• This report is structured along key topics that make MBSE a reality
• The report is based on a survey of hardware manufacturers and in-depth interviews with experts in the D&E domain

3. Background: D&E software market & competitive landscape

• Background: D&E software market & competitive landscape: Chapter overview
• Market definition & segmentation
• Design & engineering software market forecasts by software category
• Market shares by D&E software category
• Competitive dynamics: Industry has been consolidating lately while new AI-native challengers are emerging
• Siemens D&E priorities
• Dassault Systemes D&E priorities
• Usage share: Which vendors survey respondents use
• Usage share: Where the most-used vendors are strong
• Design software company profile 1: Dassault Systemes
• Design software company profile 2: Autodesk
• Design software company profile 3: Siemens
• Design software company profile 4: PTC
• Simulation software company profile 1: Ansys (acquired by Synopsys)
• Simulation software company profile 2: MathWorks
• Simulation software company profile 3: Siemens
• Simulation software company profile 4: Altair (acquired by Siemens)
• PLM software company profile 1: PTC
• PLM software company profile 2: Siemens
• PLM software company profile 3: SAP
• PLM software company profile 4: Dassault Systemes
• PLM software company profile 5: Autodesk
• Software development lifecycle (SDLC) platforms company profile 1: Atlassian
• Software development lifecycle (SDLC) platforms company profile 2: Github
• Software development lifecycle (SDLC) platforms company profile 3: Gitlab
• Cloud platform company profile 1: Microsoft
• Cloud platform company profile 2: AWS
• Cloud platform company profile 3: Google Cloud

4. Systems engineering & MBSE adoption

• Systems engineering & MBSE: Chapter overview
• Fundamentals of SE and MBSE
• MBSE: Key features of MBSE
• Software and hardware development alignment (2 parts)
• Root causes responsible of low software-hardware collaboration
• Other challenges in SE (2 parts)
• Challenge 1
• How manufacturers are using MBSE: Example-Siemens Healthineers
• MBSE outlook: The shift from human-driven to AI-augmented MBSE

5. Shift-left engineering adoption

• Shift-left engineering: Chapter overview
• Shift-left engineering: Overview
• Adoption of shift-left practices (2 parts)
• Benefits of shift-left practices (2 parts)
• Shift-left enables early engineering input sharing (2 parts)
• Maturity in shift-left practices (2 parts)
• Challenges in shift-left practices (2 parts)
• Shift-left engineering’s application across vendors and adopters

6. Cloud adoptionn

• Cloud adoption: Chapter overview
• Cloud deployment across D&E toolchain components (2 parts)
• Cloud adoption outlook for D&E workloads (2 parts)
• Current cloud adoption for D&E workloads: By region/industry/company size
• Example: Siemens’ cloud and SaaS strategy
• Considerations for cloud migration (2 parts)
• Data security & compliance is a critical consideration for cloud migration
• Cloud migration concerns (2 parts)
• The engineering workloads that are unlikely to migrate to the cloud

7. AI adoption

• AI Adoption: Chapter overview
• Role of AI in D&E (2 parts)
• AI’s added value in D&E (2 parts)
• Trust in AI tools (2 parts)
• GenAI in D&E (2 parts)
• GenAI adoption by D&E vendors: Example – PTC and Siemens
• Agentic AI in D&E (2 parts)
• Agentic AI tools in D&E workflows: Example – Cadence and Ansys
• Agentic AI’s added value in D&E (2 parts)
• Trust in agentic AI (2 parts)
• Agentic AI orchestration in D&E (2 parts)
• Agentic AI example: AI agents are taking on orchestration roles across D&E systems
• Hyperscalers enter the discussion as D&E orchestrators

8. Digital thread adoption

• Digital thread adoption: Chapter overview
• What is a digital thread?
• Adoption of digital thread (2 parts)
• Barriers to digital thread adoption
• Key challenges in digital threads (2 parts)
• Solutions for digital thread challenges
• Adoption of digital thread tools and practices (2 parts)
• Barriers to adopting digital thread tools and practices
• Cloud and API adoption in digital threads (2 parts)
• Digital threads across vendor tools (2 parts)
• Digital thread offerings: Examples – Siemens and AWS

9. Outlook

• What is next for design & engineering software?
• Trend 1
• Trend 2
• Trend 3
• Trend 4
• Trend 5 (3 parts)

10. Research methodology

• About this report
• Complete list of survey questions (2 parts)
• Respondent sampling overview (3 parts)

11. About IoT Analytics

• About IoT Analytics
• Other publications by IoT Analytics
• Information and contact