全球認知供應鏈市場：市場規模和份額分析 - 趨勢、驅動因素、競爭格局和預測（2024-2030）

市場概況

2023 年，全球認知供應鏈產業估值為 87.982 億美元，預計到 2030 年將激增至 249.827 億美元，預測期內複合年增長率為 16.2%。

認知 SCM 解決方案是強大的工具，有助於最大限度地減少損失、選擇有利的分銷管道並促進在全球商業界日益流行的綠色實踐。 從這個意義上說，企業和公司所屬的世界其他地區/供應鏈的永續發展目標可以同時轉向更永續的實踐。

也許最明顯的例子是貿易擴張時代對綠色、高效供應鏈解決方案的高需求。 借助認知供應鏈解決方案，可以實現複雜全球網路中的閉環控制和完整的營運監督。 這使您能夠順利處理與複雜供應鏈相關的決策。 目前正在追求永續發展，即拍擊技術，以有效利用廢物循環中的資源和環境友好的處理方法。

供應鏈營運正在進入人工智慧和機器學習技術領域，這些技術帶來智慧洞察和流程自動化。 透過預測分析分析資料模式，可以實現人工智慧輔助的需求預測、庫存優化和動態路線規劃。

關鍵見解

大公司擁有較大的市場份額，因為它們有能力投資認知供應鏈解決方案等尖端技術。

中小型企業可以透過在其業務中應用認知、更實惠且相關的供應鏈解決方案來更快地發展。

機器學習 (ML) 類別預計從 2024 年到 2030 年將以 16.5% 的複合年增長率成長，佔據最大的市場份額。

到 2023 年，本地部署將佔據約 65% 的巨大市場份額。 此部署為自訂認知供應鏈解決方案提供了更多選項，以滿足您的特定業務需求。

北美是最大的市場區域，預計到 2030 年將佔全球銷售額的約 50%。 推動北美優勢的因素包括高度重視效率、降低成本和提高生產力。

與北美一樣，歐洲也佔了相當大的份額，德國、英國和法國等國家迅速實施了供應鏈管理認知解決方案。

本報告分析了全球認知供應鏈市場，包括市場的基本結構和最新情況、主要促進和抑制因素以及全球、按地區和主要國家的市場規模前景（以貨幣形式計算） ），2017- 2030），按公司規模、技術、部署方法和最終用戶劃分的詳細趨勢、市場競爭的現狀以及主要公司的概況。

目錄

第1章研究範圍

第2章研究方法

第 3 章執行摘要

第 4 章市場指標

第5章產業展望

• 市場動態
  • 趨勢
  • 促進因素
  • 抑制因素/挑戰
  • 促進/抑制因子影響分析
• 新型冠狀病毒感染 (COVID-19) 的影響
• 波特五力分析

第6章世界市場

• 摘要
• 市場收入：依公司規模劃分（2017-2030 年）
• 市場收入：依技術劃分（2017-2030 年）
• 市場收入：依部署方法劃分（2017-2030 年）
• 市場收入：以最終用戶劃分（2017-2030 年）
• 市場收入：按地區劃分（2017-2030 年）

第7章北美市場

• 摘要
• 市場收入：依公司規模劃分（2017-2030 年）
• 市場收入：依技術劃分（2017-2030 年）
• 市場收入：依部署方法劃分（2017-2030 年）
• 市場收入：以最終用戶劃分（2017-2030 年）
• 市場收入：依國家/地區劃分（2017-2030 年）

第8章歐洲市場

第9章亞太市場

第10章拉丁美洲市場

第11章中東及非洲市場

第12章美國市場

• 摘要
• 市場收入：依公司規模劃分（2017-2030 年）
• 市場收入：依技術劃分（2017-2030 年）
• 市場收入：依部署方法劃分（2017-2030 年）
• 市場收入：以最終用戶劃分（2017-2030 年）

第13章加拿大市場

第14章德國市場

第15章法國市場

第16章英國市場

第17章義大利市場

第18章西班牙市場

第19章日本市場

第20章中國市場

第21章印度市場

第22章澳洲市場

第23章韓國市場

第24章巴西市場

第25章墨西哥市場

第26章沙烏地阿拉伯市場

第27章南非市場

第 28 章阿聯酋 (UAE) 市場

第29章競爭態勢

• 市場參與者及其產品列表
• 主要公司的競爭基準
• 各大公司的產品基準
• 近期策略發展狀況

第30章公司簡介

• 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 附錄

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