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市場調查報告書
商品編碼
2097310

製造業巨量資料分析：市場佔有率分析、產業趨勢與統計、成長預測（2025-2030）

Big Data Analytics in the Manufacturing Industry - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 161 Pages | 商品交期: 2-3個工作天內

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簡介目錄

根據 Mordor Intelligence 預測，製造業巨量資料分析市場規模預計將在 2025 年達到 73 億美元，到 2030 年達到 143 億美元，複合年成長率為 14.40%。

製造業巨量資料分析市場圖1

本報告按元件（軟體和服務）、部署模式（本地部署、雲端部署、邊緣/霧部署）、分析類型（說明分析等）、資料類型（結構化資料等）、應用程式（品管等）、最終用戶產業（汽車、半導體和電子等）以及地區進行細分。市場預測以美元價值表示。

製造業巨量資料分析市場洞察與趨勢

技術中心價值鏈的演變

製造商正從獨立的自動化系統轉向以資產為中心的統一資料架構，將工程、生產和售後服務洞察整合到一個連續的循環中。西門子將於2024年發表的Simatic自動化工作站，使工廠能夠以容器化的軟體邏輯取代固定硬體PLC，進而整合IT和營運技術（OT），並縮短換車時間。作為汽車行業的先驅，福特已展示了軟體定義的組裝單元，該單元可在幾分鐘內（而非幾小時）切換車輛型號。這減少了模具庫存，並實現了客製化生產。此類整合正在推動預測性維護計劃的實施，從而延長設備使用壽命並推遲重大資本投資。同時，循環經濟的需求正促使原始設備製造商（OEM）收集生命週期資料以收回再製造成本，並將分析平台從成本項目轉變為收入來源。

工業4.0推動工業自動化快速發展

隨著工資上漲削弱了傳統的成本優勢，亞太地區的工廠正引領工業4.0的全面應用。現代汽車位於清奈的工廠報告稱，在其加工中心部署人工智慧智慧感測器後，意外維護時間減少了5%。印度的國家「工業4.0」藍圖，64%的重複性任務自動化將使生產率每年提高1.4%，並節省相當於7,490億工時的成本。越南和泰國也出現了類似的趨勢，該地區正從依賴勞動力成本優勢轉向依賴技術成本優勢，這催生了對雲端原生分析套件的需求，這些套件能夠整合和管理數千個網實整合資產，同時滿足出口市場的可追溯性標準。

缺乏網路安全意識和擔憂

2024年，製造業成為遭受網路攻擊最多的產業，佔工業領域所有網路安全事件的68%。每次資料外洩的平均成本高達488萬美元，導致企業董事會對雲端和遠端連線專案持謹慎態度。三分之一的工廠高層表示，擔心營運技術（OT）網路遭到入侵是他們延遲採用分析技術的主要原因。儘管供應商正在透過零信任架構、加密資料口袋和主權雲端實例來解決這個問題，但許多中小型工廠仍將網路安全預算視為「可有可無的支出」。

細分市場分析

2024年，軟體仍是領先細分市場，佔銷售額的68.8%，這主要得益於歷史資料庫、資料整合中心以及整合到製造業巨量資料分析中的人工智慧平台。然而，服務細分市場預計將以16.2%的複合年成長率成長，成為成長最快的細分市場，因為工廠需要具備將這些工具整合到傳統MES和SCADA系統中的專業知識的人員。諮詢服務現已擴展到價值流程圖繪製、感測器部署和模型管治等領域，這反映出人們認知到，僅靠「開箱即用」的軟體無法創造永續的價值。基於績效定價的數位孿生維護和預測演算法的管理服務正日益普及，尤其是在缺乏分析負責人的二級供應商中。

一個值得注意的指標是多年轉型契約，其中業務收益在協議生效的第三年就超過了許可費。系統整合商透過捆綁增強型網路安全、邊緣節點編配和持續模型監控，降低了整體整合風險。隨著客戶從一次性先導計畫轉向全廠部署，那些能夠快速回應並提供協作創新實驗室和聯合概念驗證(PoC) 資金的供應商正在擴大其市場佔有率。

2024年，本地部署仍將維持52.6%的市場佔有率，鞏固其在製造業巨量資料分析市場中確定性控制和智慧財產權保護的重要地位。然而，雲端原生部署預計將以每年16.7%的速度成長，這主要得益於其能夠靈活利用GPU進行複雜模型訓練。大多數製造商正在轉向混合拓撲結構，其中對延遲敏感的工作負載運行在邊緣伺服器和本地微型資料中心，而長期規劃和跨站點基準測試則在區域雲中進行。這種架構最大限度地發揮了本地自主性和全球協作的優勢。

同時，雲端服務供應商正在推出符合 ITAR 和 GxP 等合規模板的產業專用的區域，以緩解航太和製藥業的監管擔憂。此外，透過雲端交付的低程式碼 AI 管線正在縮短模型開發週期，並幫助小規模工廠將迭代測試和學習實驗遷移到異地沙箱，並將提煉的推理引擎回程傳輸傳至邊緣閘道器。

區域分析

2024年，北美引領製造業巨量資料分析市場，佔38.8%的銷售額。美國航太、化工和重型設備製造商繼續試行邊緣人工智慧技術，以實現零缺陷目標；加拿大礦業公司則實施能源最佳化分析，以抵銷碳定價的影響。墨西哥出口型汽車組裝透過實施即時統計製程控制（SPC）儀表板，滿足原始設備製造商（OEM）的準時制生產要求，鞏固了其在該地區高附加價值製造商的地位。

預計亞太地區將實現最快成長，到2030年複合年成長率（CAGR）預計達到15.2%。印度的「工業4.0」政策為連網機械提供稅額扣抵，刺激了包括三級供應商在內的所有企業採用該技術。中國的智慧工廠補貼正在加速5G和邊緣運算技術的應用，使工廠能夠在專用網路上實施封閉回路型品管。在日本，分析技術正被應用於精密加工領域，因為微米級的誤差都可能威脅到企業的聲譽。同時，新加坡和馬來西亞正將自身定位為分析中心，與半導體巨頭合作營運區域工業物聯網沙盒。

由於德國在「工業4.0」領域的領先地位和嚴格的永續性指令，歐洲仍然是強大的經濟基礎。博世已在三年內投入25億歐元用於人工智慧，為其整個工廠建造數位孿生模型，以減少廢棄物和能源消耗。脫歐後的英國製造商正在利用分析技術來彌補生產力下降，而義大利的奢侈品工坊則使用電腦視覺技術來取代手工品質檢查。一家北歐鑄造廠正在使用即時排放儀錶板來應對歐盟提出的碳排放邊境調節機制，這表明分析技術在快速適應法規方面可以發揮重要作用。

其他好處：

  • Excel格式的市場預測（ME）表
  • 3個月的分析師支持

目錄

第1章：引言

  • 研究假設和市場定義
  • 調查範圍

第2章：調查方法

第3章執行摘要

第4章 市場狀況

  • 市場概覽
  • 市場促進因素
    • 以不斷發展的技術、資產和工程為核心的價值鏈。
    • 工業4.0推動工業自動化快速發展
    • 利用工業物聯網進行邊緣分析的廣泛應用
    • 數位孿生技術的興起正在推動數據粒度的提高。
    • 引入能夠產生高頻時間序列資料的智慧感測器。
    • OEM主導的PDaaS（生產數據即服務）貨幣化
  • 市場限制因素
    • 缺乏網路安全意識和擔憂
    • 具備領域專業知識的資料科學專業人員短缺
    • 產生非標準資料格式的舊式設備
    • Petabyte級機器資料的出站雲傳輸成本不斷上升。
  • 價值鏈分析
  • 監理情勢
  • 技術展望
  • 波特五力分析
  • 宏觀經濟因素對市場的影響

第5章 市場規模與成長預測

  • 按組件
    • 軟體
      • 資料管理和整合平台
      • 高階分析平台
    • 服務
      • 專業服務
      • 託管服務
  • 部署模式
    • 現場
    • 雲
    • 邊緣/霧
  • 按分析類型
    • 說明分析
    • 診斷分析
    • 預測分析
    • 指示性分析
  • 類型
    • 結構化
    • 非結構化
    • 半結構化
  • 透過使用
    • 品管
    • 狀態監控
    • 預測性保護
    • 庫存和供應鏈最佳化
    • 能源管理
    • 生產計畫和調度
    • 流程最佳化
  • 按最終用戶行業分類
    • 車
    • 半導體和電子學
    • 航太/國防
    • 食品/飲料
    • 化學與材料
    • 製藥和生命科學
    • 重型機械和設備
    • 其他終端用戶產業
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 墨西哥
    • 南美洲
      • 巴西
      • 阿根廷
      • 智利
      • 其他南美國家
    • 歐洲
      • 德國
      • 英國
      • 法國
      • 義大利
      • 西班牙
      • 其他歐洲國家
    • 亞太地區
      • 中國
      • 日本
      • 印度
      • 韓國
      • 澳洲
      • 新加坡
      • 馬來西亞
      • 其他亞太國家
    • 中東和非洲
      • 中東
        • 沙烏地阿拉伯
        • 阿拉伯聯合大公國
        • 土耳其
        • 其他中東國家
      • 非洲
        • 南非
        • 奈及利亞
        • 其他非洲國家

第6章 競爭情勢

  • 市場集中度
  • 策略趨勢
  • 市佔率分析
  • 公司簡介
    • ABB Ltd.
    • Alteryx Inc.
    • Altair Engineering Inc.（RapidMiner）
    • Aspen Technology Inc.
    • Bosch Rexroth AG
    • Databricks Inc.
    • Fujitsu Ltd.
    • GE Digital（General Electric Co.）
    • Hitachi Vantara LLC
    • Honeywell International Inc.
    • IBM Corporation
    • KNIME AG
    • Microsoft Corporation
    • Oracle Corporation
    • Palantir Technologies Inc.
    • PTC Inc.
    • QlikTech International AB
    • Rockwell Automation Inc.
    • SAP SE
    • SAS Institute Inc.
    • Schneider Electric SE
    • Siemens Digital Industries Software
    • TIBCO Software Inc.（Cloud Software Group）
    • Toshiba Digital Solutions Corporation

第7章 投資分析

第8章 市場機會與未來展望

  • 評估未開發的領域和未滿足的需求
簡介目錄
Product Code: 95923

According to Mordor Intelligence, the big data analytics in the manufacturing industry market stood at USD 7.30 billion in 2025 and is forecast to reach USD 14.30 billion by 2030, registering a 14.40% CAGR.

Big Data Analytics in the Manufacturing Industry - Market - IMG1

This report is Segmented by Component (Software and Services), Deployment Mode (On-Premise, Cloud, and Edge/Fog), Analytics Type (Descriptive Analytics, and More), Data Type (Structured, and More), Application (Quality Management, and More), End-User Industry (Automotive, Semiconductor and Electronics, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Insights and Trends of Big Data Analytics Market in the Manufacturing Industry

Evolving Technology-Centric Value Chain

Manufacturers are pivoting from discrete automation islands toward unified, asset-centric data fabrics that stitch engineering, production, and after-sales insights into one continuous loop. Siemens' 2024 launch of the Simatic Automation Workstation lets plants replace fixed hardware PLCs with containerized software logic, unifying IT and operational technology while lowering changeover time. Automotive early adopter Ford demonstrated software-defined assembly cells that swap vehicle variants in minutes instead of hours, reducing tooling inventory and enabling bespoke production runs. Such integration fuels predictive maintenance programs that stretch equipment life and defer large capital outlays. Simultaneously, circular-economy mandates are nudging OEMs to capture lifecycle data for remanufacturing revenue, turning analytics platforms into profit centers rather than cost items.

Rapid Industrial Automation Led by Industry 4.0

Asia-Pacific factories are spearheading full-scale Industry 4.0 rollouts as wage inflation compresses traditional cost advantages. Hyundai's Chennai plant reported a 5% cut in unplanned maintenance hours after fitting AI-ready smart sensors across machining centers. India's national Manufacturing 4.0 roadmap projects productivity gains of 1.4% each year and savings equivalent to 749 billion work-hours once 64% of repetitive tasks are automated. Similar momentum in Vietnam and Thailand underscores a regional shift from labor-arbitrage toward technology-arbitrage, spawning demand for cloud-native analytics suites that orchestrate thousands of cyber-physical assets while satisfying export-market traceability norms.

Lack of Awareness and Cyber-Security Concerns

Manufacturing ranked as the most-attacked vertical in 2024, accounting for 68% of all industrial cyber incidents. Average breach costs touched USD 4.88 million, prompting board-level caution toward cloud or remote connectivity projects. One-third of plant executives cite fear of exposing operational technology (OT) networks as the primary reason for delaying analytics deployments. Vendors respond with zero-trust architectures, encrypted data pockets, and sovereign-cloud instances, yet many small plants still view cybersecurity budgets as discretionary.

Other drivers and restraints analyzed in the detailed report include:

  1. Growing Proliferation of IIoT-Enabled Edge Analytics
  2. Rise of Digital Twins Driving Data Granularity
  3. Shortage of Data-Science Talent with Domain Expertise

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software kept its 68.8% revenue lead in 2024, anchored by historians, data-integration hubs, and AI platforms embedded within the big data analytics in the manufacturing industry market. Yet services will grow fastest at 16.2% CAGR as factories seek domain specialists to stitch these tools into legacy MES and SCADA stacks. Consulting engagements now span value-stream mapping, sensor placement, and model governance, reflecting recognition that out-of-the-box software cannot deliver sustainable value alone. Managed services for digital twin upkeep and predictive algorithms are gaining traction under outcome-based pricing, especially among tier-2 suppliers lacking analytics headcount.

A telling indicator is multiyear transformation deals where service revenue exceeds license fees by the third contract year. Systems integrators bundle cybersecurity hardening, edge-node orchestration, and continuous model monitoring, reducing total integration risk. Vendors quick to offer co-innovation labs and joint proof-of-concept funding are capturing wallet share as customers move from single-use pilot projects to plant-wide rollouts.

On-premise deployments retained a 52.6% share in 2024, cementing their role for deterministic control and intellectual-property protection within the big data analytics in the manufacturing industry market. However, cloud-native deployments will rise 16.7% annually on the back of elastic GPU availability for complex model training. Most manufacturers are converging on hybrid topologies: latency-sensitive workloads run on edge servers or local micro-data centers, while long-horizon planning and cross-site benchmarking run in regional clouds. This architecture offers the best of both-local autonomy and global coordination.

Cloud providers, meanwhile, introduce sector-specific regions with compliance templates such as ITAR or GxP, easing regulatory qualms in aerospace and pharmaceuticals. Simultaneously, cloud-delivered low-code AI pipelines shrink model-development cycles, encouraging smaller plants to migrate test-and-learn experiments to off-premise sandboxes before back-hauling distilled inference engines to edge gateways.

Complete Report Scope:

  • By Component
    • Software
      • Data Management and Integration Platforms
      • Advanced Analytics Platforms
    • Services
      • Professional Services
      • Managed Services
  • By Deployment Mode
    • On-premise
    • Cloud
    • Edge/Fog
  • By Analytics Type
    • Descriptive Analytics
    • Diagnostic Analytics
    • Predictive Analytics
    • Prescriptive Analytics
  • By Data Type
    • Structured
    • Unstructured
    • Semi-Structured
  • By Application
    • Quality Management
    • Condition Monitoring
    • Predictive Maintenance
    • Inventory and Supply-chain Optimization
    • Energy Management
    • Production Planning and Scheduling
    • Process Optimization
  • By End-user Industry
    • Automotive
    • Semiconductor and Electronics
    • Aerospace and Defense
    • Food and Beverage
    • Chemicals and Materials
    • Pharmaceuticals and Life Sciences
    • Heavy Machinery and Equipment
    • Other End-user Industries
  • By Geography
    • North America
      • United States
      • Canada
      • Mexico
    • South America
      • Brazil
      • Argentina
      • Chile
      • Rest of South America
    • Europe
      • Germany
      • United Kingdom
      • France
      • Italy
      • Spain
      • Rest of Europe
    • Asia-Pacific
      • China
      • Japan
      • India
      • South Korea
      • Australia
      • Singapore
      • Malaysia
      • Rest of Asia-Pacific
    • Middle East and Africa
      • Middle East
        • Saudi Arabia
        • United Arab Emirates
        • Turkey
        • Rest of Middle East
      • Africa
        • South Africa
        • Nigeria
        • Rest of Africa

Geography Analysis

North America dominated the big data analytics in the manufacturing industry market with 38.8% revenue share in 2024. U.S. aerospace, chemicals, and heavy-equipment makers continue to pilot edge AI for zero-defect programs, while Canadian miners adopt energy-optimization analytics to offset carbon-pricing schemes. Mexico's export-oriented auto assemblers deploy real-time SPC dashboards to meet OEM just-in-time mandates, cementing the region's high-value manufacturing position.

Asia-Pacific is poised for the quickest expansion, recording a forecast 15.2% CAGR to 2030. India's Manufacturing 4.0 policy offers tax credits on connected machinery, spurring adoption even among tier-3 suppliers. Chinese smart-factory subsidies accelerate 5G and edge rollouts, letting factories run closed-loop quality control off private networks. Japan applies analytics to precision machining, where micron-level deviations jeopardize reputation. Singapore and Malaysia, meanwhile, brand themselves as analytics hubs, hosting regional IIoT sandboxes tied to semiconductor giants.

Europe remains a stronghold courtesy of Germany's Industrie 4.0 leadership and stringent sustainability directives. Bosch earmarked EUR 2.5 billion for AI over three years, channeling funds into plant-wide digital twins that curb scrap and energy usage. UK manufacturers post-Brexit leverage analytics for productivity offsets, whereas Italian luxury-goods workshops employ computer vision for artisanal quality checks. Nordic foundries use real-time emissions dashboards to meet EU carbon border adjustment proposals, showcasing analytics' role in regulatory agility.

  1. ABB Ltd.
  2. Alteryx Inc.
  3. Altair Engineering Inc. (RapidMiner)
  4. Aspen Technology Inc.
  5. Bosch Rexroth AG
  6. Databricks Inc.
  7. Fujitsu Ltd.
  8. GE Digital (General Electric Co.)
  9. Hitachi Vantara LLC
  10. Honeywell International Inc.
  11. IBM Corporation
  12. KNIME AG
  13. Microsoft Corporation
  14. Oracle Corporation
  15. Palantir Technologies Inc.
  16. PTC Inc.
  17. QlikTech International AB
  18. Rockwell Automation Inc.
  19. SAP SE
  20. SAS Institute Inc.
  21. Schneider Electric SE
  22. Siemens Digital Industries Software
  23. TIBCO Software Inc. (Cloud Software Group)
  24. Toshiba Digital Solutions Corporation

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Evolving Technology, Asset and Engineering-oriented Value Chain
    • 4.2.2 Rapid Industrial Automation led by Industry 4.0
    • 4.2.3 Growing Proliferation of IIoT-enabled Edge Analytics
    • 4.2.4 Rise of Digital Twins Driving Data Granularity
    • 4.2.5 Smart-Sensor Adoption Generating High-Frequency Time-Series Data
    • 4.2.6 OEM-led Monetization of Production Data-as-a-Service
  • 4.3 Market Restraints
    • 4.3.1 Lack of Awareness and Cyber-security Concerns
    • 4.3.2 Shortage of Data-Science Talent with Domain Expertise
    • 4.3.3 Legacy Equipment Producing Non-standardized Data Formats
    • 4.3.4 Rising Cloud Egress Costs for Petabyte-scale Machine Data
  • 4.4 Value Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Porter's Five Forces Analysis
    • 4.7.1 Bargaining Power of Suppliers
    • 4.7.2 Bargaining Power of Buyers
    • 4.7.3 Threat of New Entrants
    • 4.7.4 Threat of Substitutes
    • 4.7.5 Intensity of Competitive Rivalry
  • 4.8 Impact of Macroeconomic Factors on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUES)

  • 5.1 By Component
    • 5.1.1 Software
      • 5.1.1.1 Data Management and Integration Platforms
      • 5.1.1.2 Advanced Analytics Platforms
    • 5.1.2 Services
      • 5.1.2.1 Professional Services
      • 5.1.2.2 Managed Services
  • 5.2 By Deployment Mode
    • 5.2.1 On-premise
    • 5.2.2 Cloud
    • 5.2.3 Edge/Fog
  • 5.3 By Analytics Type
    • 5.3.1 Descriptive Analytics
    • 5.3.2 Diagnostic Analytics
    • 5.3.3 Predictive Analytics
    • 5.3.4 Prescriptive Analytics
  • 5.4 By Data Type
    • 5.4.1 Structured
    • 5.4.2 Unstructured
    • 5.4.3 Semi-Structured
  • 5.5 By Application
    • 5.5.1 Quality Management
    • 5.5.2 Condition Monitoring
    • 5.5.3 Predictive Maintenance
    • 5.5.4 Inventory and Supply-chain Optimization
    • 5.5.5 Energy Management
    • 5.5.6 Production Planning and Scheduling
    • 5.5.7 Process Optimization
  • 5.6 By End-user Industry
    • 5.6.1 Automotive
    • 5.6.2 Semiconductor and Electronics
    • 5.6.3 Aerospace and Defense
    • 5.6.4 Food and Beverage
    • 5.6.5 Chemicals and Materials
    • 5.6.6 Pharmaceuticals and Life Sciences
    • 5.6.7 Heavy Machinery and Equipment
    • 5.6.8 Other End-user Industries
  • 5.7 By Geography
    • 5.7.1 North America
      • 5.7.1.1 United States
      • 5.7.1.2 Canada
      • 5.7.1.3 Mexico
    • 5.7.2 South America
      • 5.7.2.1 Brazil
      • 5.7.2.2 Argentina
      • 5.7.2.3 Chile
      • 5.7.2.4 Rest of South America
    • 5.7.3 Europe
      • 5.7.3.1 Germany
      • 5.7.3.2 United Kingdom
      • 5.7.3.3 France
      • 5.7.3.4 Italy
      • 5.7.3.5 Spain
      • 5.7.3.6 Rest of Europe
    • 5.7.4 Asia-Pacific
      • 5.7.4.1 China
      • 5.7.4.2 Japan
      • 5.7.4.3 India
      • 5.7.4.4 South Korea
      • 5.7.4.5 Australia
      • 5.7.4.6 Singapore
      • 5.7.4.7 Malaysia
      • 5.7.4.8 Rest of Asia-Pacific
    • 5.7.5 Middle East and Africa
      • 5.7.5.1 Middle East
        • 5.7.5.1.1 Saudi Arabia
        • 5.7.5.1.2 United Arab Emirates
        • 5.7.5.1.3 Turkey
        • 5.7.5.1.4 Rest of Middle East
      • 5.7.5.2 Africa
        • 5.7.5.2.1 South Africa
        • 5.7.5.2.2 Nigeria
        • 5.7.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
    • 6.4.1 ABB Ltd.
    • 6.4.2 Alteryx Inc.
    • 6.4.3 Altair Engineering Inc. (RapidMiner)
    • 6.4.4 Aspen Technology Inc.
    • 6.4.5 Bosch Rexroth AG
    • 6.4.6 Databricks Inc.
    • 6.4.7 Fujitsu Ltd.
    • 6.4.8 GE Digital (General Electric Co.)
    • 6.4.9 Hitachi Vantara LLC
    • 6.4.10 Honeywell International Inc.
    • 6.4.11 IBM Corporation
    • 6.4.12 KNIME AG
    • 6.4.13 Microsoft Corporation
    • 6.4.14 Oracle Corporation
    • 6.4.15 Palantir Technologies Inc.
    • 6.4.16 PTC Inc.
    • 6.4.17 QlikTech International AB
    • 6.4.18 Rockwell Automation Inc.
    • 6.4.19 SAP SE
    • 6.4.20 SAS Institute Inc.
    • 6.4.21 Schneider Electric SE
    • 6.4.22 Siemens Digital Industries Software
    • 6.4.23 TIBCO Software Inc. (Cloud Software Group)
    • 6.4.24 Toshiba Digital Solutions Corporation

7 INVESTMENT ANALYSIS

8 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 8.1 White-space and Unmet-Need Assessment