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市場調查報告書
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1538651

全球化學品人工智慧市場規模研究（按組成部分、業務應用、最終用戶）和 2022-2032 年區域預測

Global AI in Chemicals Market Size Study, by Component, by Business Application, by End User, and Regional Forecasts 2022-2032
出版日期: | 出版商: Bizwit Research & Consulting LLP | 英文 285 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2023年全球化學品人工智慧市場價值約為11.4億美元，預計在2024-2032年預測期內將以超過39.72%的健康成長率成長。化工人工智慧是指人工智慧技術在化學產業的應用，以增強流程、最佳化生產、推動創新。機器學習和資料分析等人工智慧技術用於分析複雜的化學資料、預測結果並改進化學產品和製程的設計。這包括最佳化反應條件、識別新材料和加強品質控制。人工智慧透過預測潛在風險來幫助加速研發、降低營運成本並增強安全性。人工智慧在化學品中的整合有助於更有效率、更精確的操作，從而推動產業內產品開發和流程最佳化的進步。此外，先進的分析和機器學習演算法可以實現精確的成本和性能估計，而人工智慧驅動的自動化則簡化了實驗程序，從而提高了效率、準確性和安全性。

研究和開發中對人工智慧不斷成長的需求正在顯著推動化學品市場中的人工智慧。隨著化學產業尋求加速創新和簡化研發流程，人工智慧技術透過分析大量資料、預測實驗結果和最佳化化學流程提供了關鍵支援。人工智慧透過先進的演算法和機器學習促進新材料的發現、改善反應條件並增強產品開發。這種能力使研究人員能夠更有效率、更有效地做出數據驅動的決策，從而減少與傳統研發方法相關的時間和成本。因此，越來越依賴人工智慧來推動研發，推動了化學產業對人工智慧解決方案的需求不斷擴大。

全球化學品人工智慧市場的關鍵區域包括北美、歐洲、亞太地區、拉丁美洲、中東和非洲。從地理上看，在強勁的研發資金和促進人工智慧的政府戰略舉措的推動下，預計到 2023 年，北美將佔據化學品人工智慧市場的最大佔有率。該地區對創新和數位轉型的高度重視推動了人工智慧技術的採用，以增強化學製程、最佳化生產並加速產品開發。北美的主要企業和研究機構正在利用人工智慧來獲得競爭優勢、提高營運效率並促進創新。此外，支持性的政府政策和對人工智慧驅動計劃的大量資金有助於北美在這個快速成長的市場中保持領先地位。此外，在多元化的化學工業和政府支持政策的推動下，亞太地區預計將以最快的複合年成長率成長。

目錄

第 1 章：化學品市場中的全球人工智慧執行摘要

  • 全球化學品人工智慧市場規模及預測（2022-2032）
  • 區域概要
  • 分部摘要
    • 按組件
    • 按業務申請
    • 按最終用戶
  • 主要趨勢
  • 經濟衰退的影響
  • 分析師推薦與結論

第 2 章：全球化學品人工智慧市場定義與研究假設

  • 研究目的
  • 市場定義
  • 研究假設
    • 包容與排除
    • 限制
    • 供給側分析
      • 可用性
      • 基礎設施
      • 監管環境
      • 市場競爭
      • 經濟可行性（消費者的角度）
    • 需求面分析
      • 監理框架
      • 技術進步
      • 環境考慮
      • 消費者意識和接受度
  • 估算方法
  • 研究涵蓋的年份
  • 貨幣兌換率

第 3 章：全球人工智慧在化學品市場動態的應用

  • 市場促進因素
    • 研發領域對人工智慧的需求不斷成長
    • 採用先進的數位技術
    • 更加重視改進大量生產調度
  • 市場挑戰
    • 初始投資和營運成本高
    • 監管問題和資料隱私問題
  • 市場機會
    • 新興市場擴張
    • 技術進步與創新
    • 人工智慧開發商與化學品製造商之間的合作

第 4 章：全球化學品市場人工智慧產業分析

  • 波特的五力模型
    • 供應商的議價能力
    • 買家的議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭競爭
    • 波特五力模型的未來方法
    • 波特的五力影響分析
  • PESTEL分析
    • 政治的
    • 經濟
    • 社會的
    • 技術性
    • 環境的
    • 合法的
  • 頂級投資機會
  • 最佳制勝策略
  • 顛覆性趨勢
  • 產業專家視角
  • 分析師推薦與結論

第 5 章：全球化學品人工智慧市場規模與預測：按組成部分 - 2022-2032

  • 細分儀表板
  • 全球化學品市場人工智慧：2022 年和 2032 年元件收入趨勢分析
    • 硬體
    • 軟體
    • 服務

第 6 章：全球化學品人工智慧市場規模與預測：按業務應用分類 - 2022-2032

  • 細分儀表板
  • 全球化學品市場人工智慧：2022 年和 2032 年商業應用收入趨勢分析
    • 研發
    • 生產
    • 供應鏈管理
    • 策略管理

第 7 章：全球化學品人工智慧市場規模與預測：按最終用戶分類 - 2022-2032

  • 細分儀表板
  • 全球化學品市場人工智慧：2022 年和 2032 年最終用戶收入趨勢分析
    • 基礎化學品
    • 先進材料
    • 活性成分
    • 綠色生化
    • 油漆和塗料
    • 黏合劑和密封劑
    • 水處理和服務
    • 其他最終用戶

第 8 章：全球化學品人工智慧市場規模與預測：按地區 - 2022-2032

  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 西班牙
    • 義大利
    • 歐洲其他地區
  • 亞太
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 韓國
    • 亞太其他地區
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 拉丁美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 南非
    • 中東和非洲其他地區

第 9 章：競爭情報

  • 重點企業SWOT分析
  • 頂級市場策略
  • 公司簡介
    • IBM
      • 關鍵訊息
      • 概述
      • 財務（視數據可用性而定）
      • 產品概要
      • 市場策略
    • Schneider Electric (France)
    • Google
    • Microsoft
    • SAP
    • AWS
    • NVIDIA
    • C3.ai
    • GE Vernova
    • Siemens

第 10 章：研究過程

  • 研究過程
    • 資料探勘
    • 分析
    • 市場預測
    • 驗證
    • 出版
  • 研究屬性
簡介目錄

The Global AI in Chemicals Market is valued at approximately USD 1.14 billion in 2023 and is anticipated to grow with a healthy growth rate of more than 39.72% over the forecast period 2024-2032. AI in chemicals refers to the application of artificial intelligence technologies to the chemical industry to enhance processes, optimize production, and drive innovation. AI techniques, such as machine learning and data analytics, are used to analyze complex chemical data, predict outcomes, and improve the design of chemical products and processes. This includes optimizing reaction conditions, identifying new materials, and enhancing quality control. AI helps accelerate research and development, reduces operational costs, and enhances safety by predicting potential risks. The integration of AI in chemicals facilitates more efficient and precise operations, leading to advancements in product development and process optimization within the industry. Furthermore, advanced analytics and machine learning algorithms enable precise cost and performance estimations, while AI-driven automation streamlines experimental procedures, thereby enhancing efficiency, accuracy, and safety.

The growing demand for AI in research and development is significantly driving the AI in chemicals market. As the chemical industry seeks to accelerate innovation and streamline R&D processes, AI technologies provide critical support by analyzing vast amounts of data, predicting experimental outcomes, and optimizing chemical processes. AI facilitates the discovery of new materials, improves reaction conditions, and enhances product development through advanced algorithms and machine learning. This capability allows researchers to make data-driven decisions more efficiently and effectively, thereby reducing time and costs associated with traditional R&D methods. Consequently, the increasing reliance on AI to advance research and development fuels the expanding demand for AI solutions within the chemical sector.

The key region in the Global AI in Chemicals Market include North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. Geographically, North America is expected to hold the largest share of the AI in Chemicals market in 2023, driven by robust R&D funding and strategic government initiatives promoting AI. The region's strong focus on innovation and digital transformation drives the adoption of AI technologies to enhance chemical processes, optimize production, and accelerate product development. Major corporations and research institutions in North America are leveraging AI to gain competitive advantages, improve operational efficiency, and foster innovation. Additionally, supportive government policies and substantial funding for AI-driven initiatives contribute to North America's leadership in this rapidly growing market. Furthermore, the Asia-Pacific region is poised to grow at the fastest CAGR, fueled by its diverse chemical industry and supportive governmental policies.

Major market players included in this report are:

  • IBM
  • Schneider Electric
  • Google
  • Microsoft
  • SAP
  • AWS
  • NVIDIA
  • C3.ai
  • GE Vernova
  • Siemens

The detailed segments and sub-segment of the market are explained below:

By Component:

  • Hardware
  • Software
  • Services

By Business Application:

  • R&D
  • Production
  • Supply Chain Management
  • Strategy Management

By End User:

  • Basic Chemicals
  • Advanced Materials
  • Active Ingredients
  • Green & Biochemicals
  • Paints & Coatings
  • Adhesives & Sealants
  • Water Treatment & Services
  • Other End Users

By Region:

  • North America
  • U.S.
  • Canada
  • Europe
  • UK
  • Germany
  • France
  • Spain
  • Italy
  • ROE
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • RoAPAC
  • Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
  • Middle East & Africa
  • Saudi Arabia
  • South Africa
  • RoMEA

Years considered for the study are as follows:

  • Historical year - 2022
  • Base year - 2023
  • Forecast period - 2024 to 2032

Key Takeaways:

  • Market Estimates & Forecast for 10 years from 2022 to 2032.
  • Annualized revenues and regional level analysis for each market segment.
  • Detailed analysis of geographical landscape with Country level analysis of major regions.
  • Competitive landscape with information on major players in the market.
  • Analysis of key business strategies and recommendations on future market approach.
  • Analysis of competitive structure of the market.
  • Demand side and supply side analysis of the market.

Table of Contents

Chapter 1. Global AI in Chemicals Market Executive Summary

  • 1.1. Global AI in Chemicals Market Size & Forecast (2022-2032)
  • 1.2. Regional Summary
  • 1.3. Segmental Summary
    • 1.3.1. By Component
    • 1.3.2. By Business Application
    • 1.3.3. By End User
  • 1.4. Key Trends
  • 1.5. Recession Impact
  • 1.6. Analyst Recommendation & Conclusion

Chapter 2. Global AI in Chemicals Market Definition and Research Assumptions

  • 2.1. Research Objective
  • 2.2. Market Definition
  • 2.3. Research Assumptions
    • 2.3.1. Inclusion & Exclusion
    • 2.3.2. Limitations
    • 2.3.3. Supply Side Analysis
      • 2.3.3.1. Availability
      • 2.3.3.2. Infrastructure
      • 2.3.3.3. Regulatory Environment
      • 2.3.3.4. Market Competition
      • 2.3.3.5. Economic Viability (Consumer's Perspective)
    • 2.3.4. Demand Side Analysis
      • 2.3.4.1. Regulatory frameworks
      • 2.3.4.2. Technological Advancements
      • 2.3.4.3. Environmental Considerations
      • 2.3.4.4. Consumer Awareness & Acceptance
  • 2.4. Estimation Methodology
  • 2.5. Years Considered for the Study
  • 2.6. Currency Conversion Rates

Chapter 3. Global AI in Chemicals Market Dynamics

  • 3.1. Market Drivers
    • 3.1.1. Growing demand for AI in research & development
    • 3.1.2. Adoption of advanced digital techniques
    • 3.1.3. Increased emphasis on improved batch production scheduling
  • 3.2. Market Challenges
    • 3.2.1. High initial investment and operational costs
    • 3.2.2. Regulatory concerns and data privacy issues
  • 3.3. Market Opportunities
    • 3.3.1. Expansion in emerging markets
    • 3.3.2. Technological advancements and innovations
    • 3.3.3. Collaboration between AI developers and chemical manufacturers

Chapter 4. Global AI in Chemicals Market Industry Analysis

  • 4.1. Porter's 5 Force Model
    • 4.1.1. Bargaining Power of Suppliers
    • 4.1.2. Bargaining Power of Buyers
    • 4.1.3. Threat of New Entrants
    • 4.1.4. Threat of Substitutes
    • 4.1.5. Competitive Rivalry
    • 4.1.6. Futuristic Approach to Porter's 5 Force Model
    • 4.1.7. Porter's 5 Force Impact Analysis
  • 4.2. PESTEL Analysis
    • 4.2.1. Political
    • 4.2.2. Economical
    • 4.2.3. Social
    • 4.2.4. Technological
    • 4.2.5. Environmental
    • 4.2.6. Legal
  • 4.3. Top investment opportunity
  • 4.4. Top winning strategies
  • 4.5. Disruptive Trends
  • 4.6. Industry Expert Perspective
  • 4.7. Analyst Recommendation & Conclusion

Chapter 5. Global AI in Chemicals Market Size & Forecasts by Component 2022-2032

  • 5.1. Segment Dashboard
  • 5.2. Global AI in Chemicals Market: Component Revenue Trend Analysis, 2022 & 2032 (USD Billion)
    • 5.2.1. Hardware
    • 5.2.2. Software
    • 5.2.3. Services

Chapter 6. Global AI in Chemicals Market Size & Forecasts by Business Application 2022-2032

  • 6.1. Segment Dashboard
  • 6.2. Global AI in Chemicals Market: Business Application Revenue Trend Analysis, 2022 & 2032 (USD Billion)
    • 6.2.1. R&D
    • 6.2.2. Production
    • 6.2.3. Supply Chain Management
    • 6.2.4. Strategy Management

Chapter 7. Global AI in Chemicals Market Size & Forecasts by End User 2022-2032

  • 7.1. Segment Dashboard
  • 7.2. Global AI in Chemicals Market: End User Revenue Trend Analysis, 2022 & 2032 (USD Billion)
    • 7.2.1. Basic Chemicals
    • 7.2.2. Advanced Materials
    • 7.2.3. Active Ingredients
    • 7.2.4. Green & Biochemicals
    • 7.2.5. Paints & Coatings
    • 7.2.6. Adhesives & Sealants
    • 7.2.7. Water Treatment & Services
    • 7.2.8. Other End Users

Chapter 8. Global AI in Chemicals Market Size & Forecasts by Region 2022-2032

  • 8.1. North America AI in Chemicals Market
    • 8.1.1. U.S. AI in Chemicals Market
      • 8.1.1.1. Component breakdown size & forecasts, 2022-2032
      • 8.1.1.2. Business Application breakdown size & forecasts, 2022-2032
      • 8.1.1.3. End User breakdown size & forecasts, 2022-2032
    • 8.1.2. Canada AI in Chemicals Market
  • 8.2. Europe AI in Chemicals Market
    • 8.2.1. UK AI in Chemicals Market
    • 8.2.2. Germany AI in Chemicals Market
    • 8.2.3. France AI in Chemicals Market
    • 8.2.4. Spain AI in Chemicals Market
    • 8.2.5. Italy AI in Chemicals Market
    • 8.2.6. Rest of Europe AI in Chemicals Market
  • 8.3. Asia-Pacific AI in Chemicals Market
    • 8.3.1. China AI in Chemicals Market
    • 8.3.2. India AI in Chemicals Market
    • 8.3.3. Japan AI in Chemicals Market
    • 8.3.4. Australia AI in Chemicals Market
    • 8.3.5. South Korea AI in Chemicals Market
    • 8.3.6. Rest of Asia-Pacific AI in Chemicals Market
  • 8.4. Latin America AI in Chemicals Market
    • 8.4.1. Brazil AI in Chemicals Market
    • 8.4.2. Mexico AI in Chemicals Market
    • 8.4.3. Rest of Latin America AI in Chemicals Market
  • 8.5. Middle East & Africa AI in Chemicals Market
    • 8.5.1. Saudi Arabia AI in Chemicals Market
    • 8.5.2. South Africa AI in Chemicals Market
    • 8.5.3. Rest of Middle East & Africa AI in Chemicals Market

Chapter 9. Competitive Intelligence

  • 9.1. Key Company SWOT Analysis
  • 9.2. Top Market Strategies
  • 9.3. Company Profiles
    • 9.3.1. IBM
      • 9.3.1.1. Key Information
      • 9.3.1.2. Overview
      • 9.3.1.3. Financial (Subject to Data Availability)
      • 9.3.1.4. Product Summary
      • 9.3.1.5. Market Strategies
    • 9.3.2. Schneider Electric (France)
    • 9.3.3. Google
    • 9.3.4. Microsoft
    • 9.3.5. SAP
    • 9.3.6. AWS
    • 9.3.7. NVIDIA
    • 9.3.8. C3.ai
    • 9.3.9. GE Vernova
    • 9.3.10. Siemens

Chapter 10. Research Process

  • 10.1. Research Process
    • 10.1.1. Data Mining
    • 10.1.2. Analysis
    • 10.1.3. Market Estimation
    • 10.1.4. Validation
    • 10.1.5. Publishing
  • 10.2. Research Attributes