查看购物车
  • Traditional Chinese
  • English
封面
市场调查报告书
商品编码
1538651

全球化学品人工智慧市场规模研究（按组成部分、业务应用、最终用户）和 2022-2032 年区域预测

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个工作天内

价格
简介目录

2023年全球化学品人工智慧市场价值约为11.4亿美元，预计在2024-2032年预测期内将以超过39.72%的健康成长率成长。化工人工智慧是指人工智慧技术在化学产业的应用，以增强流程、优化生产、推动创新。机器学习和资料分析等人工智慧技术用于分析复杂的化学资料、预测结果并改进化学产品和製程的设计。这包括优化反应条件、识别新材料和加强品质控制。人工智慧透过预测潜在风险来帮助加速研发、降低营运成本并增强安全性。人工智慧在化学品中的整合有助于更有效率、更精确的操作，从而推动产业内产品开发和流程优化的进步。此外，先进的分析和机器学习演算法可以实现精确的成本和性能估计，而人工智慧驱动的自动化则简化了实验程序，从而提高了效率、准确性和安全性。

研究和开发中对人工智慧不断增长的需求正在显着推动化学品市场中的人工智慧。随着化学产业寻求加速创新和简化研发流程，人工智慧技术透过分析大量资料、预测实验结果和优化化学流程提供了关键支援。人工智慧透过先进的演算法和机器学习促进新材料的发现、改善反应条件并增强产品开发。这种能力使研究人员能够更有效率、更有效地做出数据驱动的决策，从而减少与传统研发方法相关的时间和成本。因此，越来越依赖人工智慧来推动研发，推动了化学产业对人工智慧解决方案的需求不断扩大。

全球化学品人工智慧市场的关键区域包括北美、欧洲、亚太地区、拉丁美洲、中东和非洲。从地理上看，在强劲的研发资金和促进人工智慧的政府战略倡议的推动下，预计到 2023 年，北美将占据化学品人工智慧市场的最大份额。该地区对创新和数位转型的高度重视推动了人工智慧技术的采用，以增强化学製程、优化生产并加速产品开发。北美的主要企业和研究机构正在利用人工智慧来获得竞争优势、提高营运效率并促进创新。此外，支持性的政府政策和对人工智慧驱动计划的大量资金有助于北美在这个快速成长的市场中保持领先地位。此外，在多元化的化学工业和政府支持政策的推动下，亚太地区预计将以最快的复合年增长率成长。

目录

第 1 章：化学品市场中的全球人工智慧执行摘要

  • 全球化学品人工智慧市场规模及预测（2022-2032）
  • 区域概要
  • 分部摘要
    • 按组件
    • 按业务申请
    • 按最终用户
  • 主要趋势
  • 经济衰退的影响
  • 分析师推荐与结论

第 2 章：全球化学品人工智慧市场定义与研究假设

  • 研究目的
  • 市场定义
  • 研究假设
    • 包容与排除
    • 限制
    • 供给侧分析
      • 可用性
      • 基础设施
      • 监管环境
      • 市场竞争
      • 经济可行性（消费者的角度）
    • 需求面分析
      • 监理框架
      • 技术进步
      • 环境考虑
      • 消费者意识和接受度
  • 估算方法
  • 研究考虑的年份
  • 货币兑换率

第 3 章：全球人工智慧在化学品市场动态的应用

  • 市场驱动因素
    • 研发领域对人工智慧的需求不断增长
    • 采用先进的数位技术
    • 更加重视改进大量生产调度
  • 市场挑战
    • 初始投资和营运成本高
    • 监管问题和资料隐私问题
  • 市场机会
    • 新兴市场扩张
    • 技术进步与创新
    • 人工智慧开发商与化学品製造商之间的合作

第 4 章：全球化学品市场人工智慧产业分析

  • 波特的五力模型
    • 供应商的议价能力
    • 买家的议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争竞争
    • 波特五力模型的未来方法
    • 波特的五力影响分析
  • PESTEL分析
    • 政治的
    • 经济
    • 社会的
    • 技术性
    • 环境的
    • 合法的
  • 顶级投资机会
  • 最佳制胜策略
  • 颠覆性趋势
  • 产业专家视角
  • 分析师推荐与结论

第 5 章：全球化学品人工智慧市场规模与预测：按组成部分 - 2022-2032

  • 细分仪表板
  • 全球化学品市场人工智慧：2022 年和 2032 年元件收入趋势分析
    • 硬体
    • 软体
    • 服务

第 6 章：全球化学品人工智慧市场规模与预测：按业务应用分类 - 2022-2032

  • 细分仪表板
  • 全球化学品市场人工智慧：2022 年和 2032 年商业应用收入趋势分析
    • 研发
    • 生产
    • 供应链管理
    • 策略管理

第 7 章：全球化学品人工智慧市场规模与预测：按最终用户分类 - 2022-2032

  • 细分仪表板
  • 全球化学品市场人工智慧：2022 年和 2032 年最终用户收入趋势分析
    • 基础化学品
    • 先进材料
    • 活性成分
    • 绿色生化
    • 油漆和涂料
    • 黏合剂和密封剂
    • 水处理和服务
    • 其他最终用户

第 8 章：全球化学品人工智慧市场规模与预测：按地区 - 2022-2032

  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 西班牙
    • 义大利
    • 欧洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 韩国
    • 亚太其他地区
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 拉丁美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 南非
    • 中东和非洲其他地区

第 9 章：竞争情报

  • 重点企业SWOT分析
  • 顶级市场策略
  • 公司简介
    • IBM
      • 关键讯息
      • 概述
      • 财务（视数据可用性而定）
      • 产品概要
      • 市场策略
    • Schneider Electric (France)
    • Google
    • Microsoft
    • SAP
    • AWS
    • NVIDIA
    • C3.ai
    • GE Vernova
    • Siemens

第 10 章：研究过程

  • 研究过程
    • 资料探勘
    • 分析
    • 市场预测
    • 验证
    • 出版
  • 研究属性
简介目录

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