全球认知供应链市场：市场规模和份额分析 - 趋势、驱动因素、竞争格局和预测（2024-2030）

市场概况

2023 年，全球认知供应链产业估值为 87.982 亿美元，预计到 2030 年将激增至 249.827 亿美元，预测期内复合年增长率为 16.2%。

认知 SCM 解决方案是强大的工具，有助于最大限度地减少损失、选择有利的分销管道并促进在全球商业界日益流行的绿色实践。 从这个意义上说，企业和公司所属的世界其他地区/供应链的永续发展目标可以同时转向更永续的实践。

也许最明显的例子是贸易扩张时代对绿色、高效供应链解决方案的高需求。 借助认知供应链解决方案，可以实现复杂全球网路中的闭环控制和完整的营运监督。 这使您能够顺利处理与复杂供应链相关的决策。 目前正在追求永续发展，即拍击技术，以有效利用废物循环中的资源和环境友好的处理方法。

供应链营运正在进入人工智慧和机器学习技术领域，这些技术带来智慧洞察和流程自动化。 透过预测分析分析资料模式，可以实现人工智慧辅助的需求预测、库存优化和动态路线规划。

关键见解

大公司拥有较大的市场份额，因为它们有能力投资认知供应链解决方案等尖端技术。

中小型企业可以透过在其业务中应用认知、更实惠且相关的供应链解决方案来更快地发展。

机器学习 (ML) 类别预计从 2024 年到 2030 年将以 16.5% 的复合年增长率成长，占据最大的市场份额。

到 2023 年，本地部署将占据约 65% 的巨大市场份额。 此部署为自订认知供应链解决方案提供了更多选项，以满足您的特定业务需求。

北美是最大的市场区域，预计到 2030 年将占全球销售额的约 50%。 推动北美优势的因素包括高度重视效率、降低成本和提高生产力。

与北美一样，欧洲也占了相当大的份额，德国、英国和法国等国家迅速实施了供应链管理认知解决方案。

本报告分析了全球认知供应链市场，包括市场的基本结构和最新情况、主要促进和抑制因素以及全球、按地区和主要国家的市场规模前景（以货币形式计算） ），2017- 2030），按公司规模、技术、部署方法和最终用户划分的详细趋势、市场竞争的现状以及主要公司的概况。

目录

第1章研究范围

第2章研究方法

第 3 章执行摘要

第 4 章市场指标

第5章产业展望

• 市场动态
  • 趋势
  • 促进因素
  • 抑制因素/挑战
  • 促进/抑制因子影响分析
• 新型冠状病毒感染 (COVID-19) 的影响
• 波特五力分析

第6章世界市场

• 摘要
• 市场收入：依公司规模划分（2017-2030 年）
• 市场收入：依技术划分（2017-2030 年）
• 市场收入：依部署方法划分（2017-2030 年）
• 市场收入：以最终用户划分（2017-2030 年）
• 市场收入：按地区划分（2017-2030 年）

第7章北美市场

• 摘要
• 市场收入：依公司规模划分（2017-2030 年）
• 市场收入：依技术划分（2017-2030 年）
• 市场收入：依部署方法划分（2017-2030 年）
• 市场收入：以最终用户划分（2017-2030 年）
• 市场收入：依国家/地区划分（2017-2030 年）

第8章欧洲市场

第9章亚太市场

第10章拉丁美洲市场

第11章中东及非洲市场

第12章美国市场

• 摘要
• 市场收入：依公司规模划分（2017-2030 年）
• 市场收入：依技术划分（2017-2030 年）
• 市场收入：依部署方法划分（2017-2030 年）
• 市场收入：以最终用户划分（2017-2030 年）

第13章加拿大市场

第14章德国市场

第15章法国市场

第16章英国市场

第17章义大利市场

第18章西班牙市场

第19章日本市场

第20章中国市场

第21章印度市场

第22章澳洲市场

第23章韩国市场

第24章巴西市场

第25章墨西哥市场

第26章沙乌地阿拉伯市场

第27章南非市场

第 28 章阿联酋 (UAE) 市场

第29章竞争态势

• 市场参与者及其产品列表
• 主要公司的竞争基准
• 各大公司的产品基准
• 近期策略发展状况

第30章公司简介

• IBM Corporation
• Accenture plc
• Oracle Corporation
• Amazon.com
• Intel Corporation
• NVIDIA Corporation
• Honeywell International Inc.
• Panasonic Holdings Corporation
• SAP SE
• Siemens AG
• Microsoft Corporation

第31章 附录

Market Overview

The cognitive supply chain industry was valued at USD 8,798.2 million in 2023, which is projected to surge to USD 24,982.7 million in 2030, experiencing a 16.2% CAGR during the forecast period.

Cognitive SCM solutions represent the potent tools that contribute to minimizing loss, selecting favorable distribution channels, and empowering green practices increasingly accepted by the global business community. In this sense, both business sustainability goals and the rest of the global supply chain in which the company is part are able to simultaneously shift to more sustainable practices.

Perhaps the most prominent case is the high demand experienced in the era of increasing trade for green and efficient supply chain solutions. Closed loop control and complete operation supervising on complex global networks are done with the help of cognitive supply chain solutions. Therefore, it makes the processing of decisions that are connected with intricate supply chains smooth. Now what is being pursued as slap technology for better utilization of the resources that are embedded in the current waste cycles and environment-friendly processing practices is guided by sustainability.

Supply chain operations are forging into the AI and ML technologies sphere as these bring intelligent insights and process automation. AI-assisted in-demand forecasting, inventory optimization, and dynamic route planning were achieved by analyzing the patterns within data through predictive analytics.

Key Insights

Large enterprises held a larger market share due to their ability to invest in modern technologies like cognitive supply chain solutions.

These enterprises can afford complete cognitive systems with autonomous decision-making, real-time visibility, and predictive analytics.

Large organizations most often integrate into the global supply chain, involving several regions and companies within the network, therefore it is technology-oriented and intended to simplify operations, help managers make better decisions, and mitigate risks.

SMEs will be able to see quicker growth when they apply cognitive more affordable and suitable supply chain solutions across their businesses.

The machine learning category is expected to grow at a CAGR of 16.5% during 2024-2030 and hold the largest market share.

ML enables data-driven decision-making, cost reduction, productivity increase, and optimization of supply chain processes.

ML-driven solutions automate tasks, analyze large data volumes, and identify patterns and insights for a competitive edge.

The on-premises category held a larger market share, approximately 65%, in 2023.

This deployment mode offers more customization options for cognitive supply chain solutions tailored to specific business needs.

Integrating these solutions into existing workflows is easier with on-premises deployment.

Older technologies can often work more efficiently when combined with on-premises solutions.

North America is the largest market region, expected to contribute around 50% of global revenue by 2030.

Factors driving North America's dominance include a strong focus on efficiency, cost savings, and productivity improvement.

Cognitive supply chain technologies enable businesses in North America to detect patterns, forecast demand, and optimize logistics so that the number of resources involved is reduced with a subsequent drop in waste.

The emerging AI and big data are the fundamental enablers of the transition to cognitive supply chain solutions across the region.

Along with North America, Europe represents a rather big piece of the pie, as countries like Germany, the UK, and France quickly implement cognitive solutions for supply chain management.

A partnership between technology firms, institutions of learning, and business leaders makes it possible for Europe to shift forward with innovation and quickly find solutions for implementation.

Table of Contents

Chapter 1. Research Scope

• 1.1. Research Objectives
• 1.2. Market Definition
• 1.3. Analysis Period
• 1.4. Market Size Breakdown by Segments
  • 1.4.1. Market size breakdown, by enterprise size
  • 1.4.2. Market size breakdown, by technology
  • 1.4.3. Market size breakdown, by deployment mode
  • 1.4.4. Market size breakdown, by end user
  • 1.4.5. Market size breakdown, by region
  • 1.4.6. Market size breakdown, by country
• 1.5. Market Data Reporting Unit
  • 1.5.1. Value
• 1.6. Key Stakeholders

Chapter 2. Research Methodology

• 2.1. Secondary Research
  • 2.1.1. Paid
  • 2.1.2. Unpaid
  • 2.1.3. P&S Intelligence database
• 2.2. Primary Research
• 2.3. Market Size Estimation
• 2.4. Data Triangulation
• 2.5. Currency Conversion Rates
• 2.6. Assumptions for the Study
• 2.7. Notes and Caveats

Chapter 3. Executive Summary

Chapter 4. Market Indicators

Chapter 5. Industry Outlook

• 5.1. Market Dynamics
  • 5.1.1. Trends
  • 5.1.2. Drivers
  • 5.1.3. Restraints/challenges
  • 5.1.4. Impact analysis of drivers/restraints
• 5.2. Impact of COVID-19
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Bargaining power of buyers
  • 5.3.2. Bargaining power of suppliers
  • 5.3.3. Threat of new entrants
  • 5.3.4. Intensity of rivalry
  • 5.3.5. Threat of substitutes

Chapter 6. Global Market

• 6.1. Overview
• 6.2. Market Revenue, by Enterprise Size (2017-2030)
• 6.3. Market Revenue, by Technology (2017-2030)
• 6.4. Market Revenue, by Deployment Mode (2017-2030)
• 6.5. Market Revenue, by End User (2017-2030)
• 6.6. Market Revenue, by Region (2017-2030)

Chapter 7. North America Market

• 7.1. Overview
• 7.2. Market Revenue, by Enterprise Size (2017-2030)
• 7.3. Market Revenue, by Technology (2017-2030)
• 7.4. Market Revenue, by Deployment Mode (2017-2030)
• 7.5. Market Revenue, by End User (2017-2030)
• 7.6. Market Revenue, by Country (2017-2030)

Chapter 8. Europe Market

• 8.1. Overview
• 8.2. Market Revenue, by Enterprise Size (2017-2030)
• 8.3. Market Revenue, by Technology (2017-2030)
• 8.4. Market Revenue, by Deployment Mode (2017-2030)
• 8.5. Market Revenue, by End User (2017-2030)
• 8.6. Market Revenue, by Country (2017-2030)

Chapter 9. APAC Market

• 9.1. Overview
• 9.2. Market Revenue, by Enterprise Size (2017-2030)
• 9.3. Market Revenue, by Technology (2017-2030)
• 9.4. Market Revenue, by Deployment Mode (2017-2030)
• 9.5. Market Revenue, by End User (2017-2030)
• 9.6. Market Revenue, by Country (2017-2030)

Chapter 10. LATAM Market

• 10.1. Overview
• 10.2. Market Revenue, by Enterprise Size (2017-2030)
• 10.3. Market Revenue, by Technology (2017-2030)
• 10.4. Market Revenue, by Deployment Mode (2017-2030)
• 10.5. Market Revenue, by End User (2017-2030)
• 10.6. Market Revenue, by Country (2017-2030)

Chapter 11. MEA Market

• 11.1. Overview
• 11.2. Market Revenue, by Enterprise Size (2017-2030)
• 11.3. Market Revenue, by Technology (2017-2030)
• 11.4. Market Revenue, by Deployment Mode (2017-2030)
• 11.5. Market Revenue, by End User (2017-2030)
• 11.6. Market Revenue, by Country (2017-2030)

Chapter 12. U.S. Market

• 12.1. Overview
• 12.2. Market Revenue, by Enterprise Size (2017-2030)
• 12.3. Market Revenue, by Technology (2017-2030)
• 12.4. Market Revenue, by Deployment Mode (2017-2030)
• 12.5. Market Revenue, by End User (2017-2030)

Chapter 13. Canada Market

• 13.1. Overview
• 13.2. Market Revenue, by Enterprise Size (2017-2030)
• 13.3. Market Revenue, by Technology (2017-2030)
• 13.4. Market Revenue, by Deployment Mode (2017-2030)
• 13.5. Market Revenue, by End User (2017-2030)

Chapter 14. Germany Market

• 14.1. Overview
• 14.2. Market Revenue, by Enterprise Size (2017-2030)
• 14.3. Market Revenue, by Technology (2017-2030)
• 14.4. Market Revenue, by Deployment Mode (2017-2030)
• 14.5. Market Revenue, by End User (2017-2030)

Chapter 15. France Market

• 15.1. Overview
• 15.2. Market Revenue, by Enterprise Size (2017-2030)
• 15.3. Market Revenue, by Technology (2017-2030)
• 15.4. Market Revenue, by Deployment Mode (2017-2030)
• 15.5. Market Revenue, by End User (2017-2030)

Chapter 16. U.K. Market

• 16.1. Overview
• 16.2. Market Revenue, by Enterprise Size (2017-2030)
• 16.3. Market Revenue, by Technology (2017-2030)
• 16.4. Market Revenue, by Deployment Mode (2017-2030)
• 16.5. Market Revenue, by End User (2017-2030)

Chapter 17. Italy Market

• 17.1. Overview
• 17.2. Market Revenue, by Enterprise Size (2017-2030)
• 17.3. Market Revenue, by Technology (2017-2030)
• 17.4. Market Revenue, by Deployment Mode (2017-2030)
• 17.5. Market Revenue, by End User (2017-2030)

Chapter 18. Spain Market

• 18.1. Overview
• 18.2. Market Revenue, by Enterprise Size (2017-2030)
• 18.3. Market Revenue, by Technology (2017-2030)
• 18.4. Market Revenue, by Deployment Mode (2017-2030)
• 18.5. Market Revenue, by End User (2017-2030)

Chapter 19. Japan Market

• 19.1. Overview
• 19.2. Market Revenue, by Enterprise Size (2017-2030)
• 19.3. Market Revenue, by Technology (2017-2030)
• 19.4. Market Revenue, by Deployment Mode (2017-2030)
• 19.5. Market Revenue, by End User (2017-2030)

Chapter 20. China Market

• 20.1. Overview
• 20.2. Market Revenue, by Enterprise Size (2017-2030)
• 20.3. Market Revenue, by Technology (2017-2030)
• 20.4. Market Revenue, by Deployment Mode (2017-2030)
• 20.5. Market Revenue, by End User (2017-2030)

Chapter 21. India Market

• 21.1. Overview
• 21.2. Market Revenue, by Enterprise Size (2017-2030)
• 21.3. Market Revenue, by Technology (2017-2030)
• 21.4. Market Revenue, by Deployment Mode (2017-2030)
• 21.5. Market Revenue, by End User (2017-2030)

Chapter 22. Australia Market

• 22.1. Overview
• 22.2. Market Revenue, by Enterprise Size (2017-2030)
• 22.3. Market Revenue, by Technology (2017-2030)
• 22.4. Market Revenue, by Deployment Mode (2017-2030)
• 22.5. Market Revenue, by End User (2017-2030)

Chapter 23. South Korea Market

• 23.1. Overview
• 23.2. Market Revenue, by Enterprise Size (2017-2030)
• 23.3. Market Revenue, by Technology (2017-2030)
• 23.4. Market Revenue, by Deployment Mode (2017-2030)
• 23.5. Market Revenue, by End User (2017-2030)

Chapter 24. Brazil Market

• 24.1. Overview
• 24.2. Market Revenue, by Enterprise Size (2017-2030)
• 24.3. Market Revenue, by Technology (2017-2030)
• 24.4. Market Revenue, by Deployment Mode (2017-2030)
• 24.5. Market Revenue, by End User (2017-2030)

Chapter 25. Mexico Market

• 25.1. Overview
• 25.2. Market Revenue, by Enterprise Size (2017-2030)
• 25.3. Market Revenue, by Technology (2017-2030)
• 25.4. Market Revenue, by Deployment Mode (2017-2030)
• 25.5. Market Revenue, by End User (2017-2030)

Chapter 26. Saudi Arabia Market

• 26.1. Overview
• 26.2. Market Revenue, by Enterprise Size (2017-2030)
• 26.3. Market Revenue, by Technology (2017-2030)
• 26.4. Market Revenue, by Deployment Mode (2017-2030)
• 26.5. Market Revenue, by End User (2017-2030)

Chapter 27. South Africa Market

• 27.1. Overview
• 27.2. Market Revenue, by Enterprise Size (2017-2030)
• 27.3. Market Revenue, by Technology (2017-2030)
• 27.4. Market Revenue, by Deployment Mode (2017-2030)
• 27.5. Market Revenue, by End User (2017-2030)

Chapter 28. U.A.E. Market

• 28.1. Overview
• 28.2. Market Revenue, by Enterprise Size (2017-2030)
• 28.3. Market Revenue, by Technology (2017-2030)
• 28.4. Market Revenue, by Deployment Mode (2017-2030)
• 28.5. Market Revenue, by End User (2017-2030)

Chapter 29. Competitive Landscape

• 29.1. List of Market Players and their Offerings
• 29.2. Competitive Benchmarking of Key Players
• 29.3. Product Benchmarking of Key Players
• 29.4. Recent Strategic Developments

Chapter 30. Company Profiles

• 30.1. IBM Corporation
  • 30.1.1. Business overview
  • 30.1.2. Product and service offerings
  • 30.1.3. Key financial summary
• 30.2. Accenture plc
  • 30.2.1. Business overview
  • 30.2.2. Product and service offerings
  • 30.2.3. Key financial summary
• 30.3. Oracle Corporation
  • 30.3.1. Business overview
  • 30.3.2. Product and service offerings
  • 30.3.3. Key financial summary
• 30.4. Amazon.com
  • 30.4.1. Business overview
  • 30.4.2. Product and service offerings
  • 30.4.3. Key financial summary
• 30.5. Intel Corporation
  • 30.5.1. Business overview
  • 30.5.2. Product and service offerings
  • 30.5.3. Key financial summary
• 30.6. NVIDIA Corporation
  • 30.6.1. Business overview
  • 30.6.2. Product and service offerings
  • 30.6.3. Key financial summary
• 30.7. Honeywell International Inc.
  • 30.7.1. Business overview
  • 30.7.2. Product and service offerings
  • 30.7.3. Key financial summary
• 30.8. Panasonic Holdings Corporation
  • 30.8.1. Business overview
  • 30.8.2. Product and service offerings
  • 30.8.3. Key financial summary
• 30.9. SAP SE
  • 30.9.1. Business overview
  • 30.9.2. Product and service offerings
  • 30.9.3. Key financial summary
• 30.10. Siemens AG
  • 30.10.1. Business overview
  • 30.10.2. Product and service offerings
  • 30.10.3. Key financial summary
• 30.11. Microsoft Corporation
  • 30.11.1. Business overview
  • 30.11.2. Product and service offerings
  • 30.11.3. Key financial summary

Chapter 31. Appendix

• 31.1. Abbreviations
• 31.2. Sources and References
• 31.3. Related Reports