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市場調查報告書
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2007799

人工智慧驅動的供應鏈市場預測至2034年：按功能、技術、部署類型、組織規模、最終用戶和地區分類的全球分析

AI Powered Supply Chain Market Forecasts to 2034- Global Analysis By Function, Technology, Deployment Mode, Organization Size, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球人工智慧驅動的供應鏈市場將達到 141.1 億美元，在預測期內以 40.9% 的複合年成長率成長，到 2034 年將達到 2,193.1 億美元。

人工智慧驅動的供應鏈是指將機器學習、預測分析和自動化等先進的人工智慧技術整合到供應鏈營運中，以提高效率、準確性和應對力。這使得企業能夠透過數據驅動的洞察，實現即時需求預測、智慧庫存管理、路線最佳化和風險規避。透過利用大量資料集和自主決策，企業可以降低營運成本、提升客戶滿意度並增強敏捷性。這種方法將傳統的供應鏈轉變為自適應的自學習系統，能夠預測快速變化的市場環境中可能出現的干擾，並最佳化端到端的物流績效。

對效率和成本最佳化的需求日益成長

各行各業的組織都面臨著在維持服務品質的同時精簡營運、降低成本的挑戰。人工智慧驅動的供應鏈能夠實現即時分析、預測規劃和自動化，從而顯著提高營運效率。透過最大限度地減少浪費、最佳化庫存水準並提高需求預測的準確性，企業可以大幅節省成本。此外，人工智慧洞察有助於加快決策速度並最佳化資源分配，使供應鏈更加敏捷和靈活，從而能夠適應不斷變化的市場需求和全球性挑戰。

實施成本高且複雜

實施人工智慧驅動的供應鏈解決方案需要前期在基礎設施、軟體和專業人才方面進行大量投資。將人工智慧技術與舊有系統整合在技術上十分複雜，耗時較長，而且通常需要進行大量的客製化開發。中小企業在實施此類先進系統時可能會面臨財務和營運方面的限制。此外，持續的維護、資料管理和系統升級需求進一步增加了整體擁有成本，從而阻礙了此類解決方案的廣泛應用。

提高數據可用性和連接性

物聯網設備、數位平台和互聯系統驅動的資料產生量快速成長，為人工智慧驅動的供應鏈帶來了巨大的發展機會。連接性的提升使得整個供應鏈網路能夠實現即時資料共用，從而增強可視性和協作性。人工智慧演算法可以利用這些海量資料產生可執行的洞察，提高預測精度，並最佳化物流運營。隨著數位生態系統的擴展，企業可以利用數據驅動的智慧來建立更智慧、反應更迅速、高度整合的供應鏈基礎設施。

資料隱私和網路安全問題

人工智慧驅動的供應鏈高度依賴資料交換和數位連接，因此也越來越容易受到網路威脅和資料外洩的攻擊。包括供應商資料和營運指標在內的敏感商業資訊可能遭到未授權存取。確保強大的網路安全態勢並遵守資料保護條例至關重要，但挑戰依然存在。安全漏洞可能導致營運中斷、品牌聲譽受損和經濟損失，最終阻礙人工智慧主導的供應鏈技術的應用。

新冠疫情的影響：

新冠疫情顯著加速了人工智慧驅動型供應鏈的普及，因為全球物流和需求模式面臨前所未有的衝擊。企業更加依賴人工智慧解決方案，以實現即時視覺化、預測分析和風險緩解，從而應對供應鏈的不確定性。這場危機凸顯了傳統系統的局限性，並促使企業加強對自動化和數位轉型的投資。即使在疫情過後，企業仍優先考慮建立具有韌性、靈活性和智慧化的供應鏈模式，以應對未來的挑戰和不斷變化的市場環境。

在預測期內，電腦視覺領域預計將佔據最大的市場佔有率。

預計在預測期內，電腦視覺領域將佔據最大的市場佔有率，因為它在提升營運視覺性和自動化方面發揮著至關重要的作用。這項技術透過影像識別和影像分析，能夠對貨物、倉庫營運和品質檢測進行即時監控。這有助於減少人為錯誤，提高準確性，並加快決策速度。在庫存追蹤、缺陷檢測和物流最佳化方面的廣泛應用，顯著鞏固了該領域在人工智慧驅動的供應鏈解決方案中的主導地位。

預計庫存管理細分市場在預測期內將呈現最高的複合年成長率。

在預測期內，庫存管理領域預計將呈現最高的成長率，這主要得益於對即時庫存可見度和高效資源利用的需求不斷成長。人工智慧驅動的庫存管理系統能夠增強需求預測能力、將補貨流程自動化，並最大限度地減少缺貨和庫存積壓。越來越多的企業正在採用這些解決方案來提高營運效率和客戶滿意度。全球供應鏈日益複雜化進一步推動了對智慧庫存最佳化的需求，從而促進了該領域的快速普及和高速成長。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其對先進技術的早期應用以及主要市場參與者的強大影響力。該地區受益於完善的數位基礎設施、對人工智慧研究的大量投資以及各行業自動化技術的廣泛應用。此外，企業對供應鏈韌性和效率日益成長的關注也推動了對人工智慧解決方案的需求，進一步鞏固了北美在不斷變化的市場格局中的領先地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的工業化、蓬勃發展的電子商務以及不斷推進的數位轉型。該地區各國正大力投資智慧物流和人工智慧技術，以提高供應鏈效率。隨著物聯網和數據分析的日益普及以及政府政策的支持，市場成長正在進一步加速。隨著企業尋求可擴展且經濟高效的解決方案，人工智慧驅動的供應鏈在亞太地區展現出強勁的發展勢頭。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域細分
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧驅動的供應鏈市場：按功能分類

  • 需求預測
  • 庫存管理
  • 採購
  • 物流/運輸
  • 倉庫管理
  • 供應商風險管理

第6章：全球人工智慧驅動的供應鏈市場：依技術分類

  • 機器學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 機器人流程自動化 (RPA)
  • 預測分析

第7章：全球人工智慧驅動的供應鏈市場：依部署模式分類

  • 基於雲端的
  • 現場

第8章：全球人工智慧驅動的供應鏈市場：按組織規模分類

  • 中小企業
  • 主要企業

第9章：全球人工智慧驅動的供應鏈市場：按最終用戶分類

  • 汽車和運輸業
  • 零售與電子商務
  • 製造業
  • 醫療和藥品
  • 食品/飲料
  • 消費品
  • 電子和半導體

第10章：全球人工智慧驅動的供應鏈市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • SAP SE
  • Oracle Corporation
  • IBM Corporation
  • Microsoft Corporation
  • Amazon Web Services（AWS）
  • Google LLC
  • NVIDIA Corporation
  • Intel Corporation
  • Siemens AG
  • Manhattan Associates
  • Kinaxis
  • Blue Yonder Group
  • Infor
  • Descartes Systems Group
  • E2open
Product Code: SMRC34671

According to Stratistics MRC, the Global AI Powered Supply Chain Market is accounted for $14.11 billion in 2026 and is expected to reach $219.31 billion by 2034 growing at a CAGR of 40.9% during the forecast period. AI powered supply chain refers to the integration of advanced artificial intelligence technologies such as machine learning, predictive analytics, and automation into supply chain operations to enhance efficiency, accuracy, and responsiveness. It enables real time demand forecasting, intelligent inventory management, route optimization, and risk mitigation through data driven insights. By leveraging vast datasets and autonomous decision making, organizations can reduce operational costs, improve customer satisfaction, and increase agility. This approach transforms traditional supply chains into adaptive, self-learning systems capable of anticipating disruptions and optimizing end to end logistics performance in dynamic market environments.

Market Dynamics:

Driver:

Rising Demand for Efficiency and Cost Optimization

Organizations across industries are under constant pressure to streamline operations and reduce costs while maintaining service quality. AI powered supply chains enable real time analytics, predictive planning, and automation, significantly improving operational efficiency. By minimizing waste, optimizing inventory levels, and enhancing demand forecasting accuracy, businesses can achieve substantial cost savings. Additionally, AI driven insights support faster decision making and improved resource allocation, making supply chains more agile, resilient, and capable of adapting to fluctuating market demands and global disruptions.

Restraint:

High Implementation Costs and Complexity

The adoption of AI-powered supply chain solutions involves substantial upfront investments in infrastructure, software, and skilled personnel. Integrating AI technologies with legacy systems can be technically complex and time-consuming, often requiring significant customization. Small and medium-sized enterprises may face financial and operational constraints in implementing such advanced systems. Moreover, ongoing maintenance, data management, and the need for continuous system upgrades further add to the total cost of ownership, posing a barrier to widespread adoption.

Opportunity:

Increased Data Availability and Connectivity

The exponential growth in data generation, driven by IoT devices, digital platforms, and interconnected systems, presents significant opportunities for AI powered supply chains. Enhanced connectivity enables real-time data sharing across supply chain networks, facilitating better visibility and coordination. AI algorithms can leverage this vast data to generate actionable insights, improve forecasting accuracy, and optimize logistics operations. As digital ecosystems expand, organizations can harness data driven intelligence to build smarter, more responsive, and highly integrated supply chain infrastructures.

Threat:

Data Privacy and Cybersecurity Concerns

As AI-powered supply chains rely heavily on data exchange and digital connectivity, they become increasingly vulnerable to cyber threats and data breaches. Sensitive business information, including supplier data and operational metrics, may be exposed to unauthorized access. Ensuring robust cybersecurity frameworks and compliance with data protection regulations is critical but challenging. Any security lapse can disrupt operations, damage brand reputation, and lead to financial losses, thereby hindering the adoption of AI driven supply chain technologies.

Covid-19 Impact:

The COVID-19 pandemic significantly accelerated the adoption of AI powered supply chains as organizations faced unprecedented disruptions in global logistics and demand patterns. Companies increasingly turned to AI solutions for real-time visibility, predictive analytics, and risk mitigation to manage supply chain uncertainties. The crisis highlighted the limitations of traditional systems, driving investments in automation and digital transformation. Post-pandemic, businesses continue to prioritize resilient, flexible, and intelligent supply chain models to better prepare for future disruptions and evolving market conditions.

The computer vision segment is expected to be the largest during the forecast period

The computer vision segment is expected to account for the largest market share during the forecast period, due to its critical role in enhancing operational visibility and automation. It enables real-time monitoring of goods, warehouse operations, and quality inspection through image recognition and video analytics. This technology reduces human error, improves accuracy, and accelerates decision-making. Its widespread application in inventory tracking, defect detection, and logistics optimization significantly contributes to its dominant position in AI powered supply chain solutions.

The inventory management segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the inventory management segment is predicted to witness the highest growth rate, due to increasing demand for real-time stock visibility and efficient resource utilization. AI-driven inventory systems enhance demand forecasting, automate replenishment processes, and minimize stockouts or overstocking. Businesses are increasingly adopting these solutions to improve operational efficiency and customer satisfaction. The rising complexity of global supply chains further drives the need for intelligent inventory optimization, supporting rapid adoption and high growth.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to early adoption of advanced technologies and strong presence of key market players. The region benefits from well-established digital infrastructure, high investment in AI research, and widespread implementation of automation across industries. Additionally, the growing focus on supply chain resilience and efficiency among enterprises drives demand for AI-powered solutions, reinforcing North America's leadership in this evolving market landscape.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, owing to rapid industrialization, expanding e-commerce sector, and increasing digital transformation initiatives. Countries in the region are investing heavily in smart logistics and AI technologies to enhance supply chain efficiency. The growing adoption of IoT and data analytics, coupled with supportive government policies, further accelerates market growth. As businesses seek scalable and cost-effective solutions, AI-powered supply chains gain strong momentum across Asia Pacific.

Key players in the market

Some of the key players in AI Powered Supply Chain Market include SAP SE, Oracle Corporation, IBM Corporation, Microsoft Corporation, Amazon Web Services (AWS), Google LLC, NVIDIA Corporation, Intel Corporation, Siemens AG, Manhattan Associates, Kinaxis, Blue Yonder Group, Infor, Descartes Systems Group and E2open.

Key Developments:

In February 2026, IBM introduced the next-generation autonomous storage portfolio featuring IBM Flash System 5600, 7600, and 9600, powered by agentic AI. The systems automate storage management, improve cyber-resilience, and optimize enterprise data operations, helping organizations manage AI workloads more efficiently. This launch strengthens IBM's hybrid cloud and AI infrastructure ecosystem by reducing manual IT operations and enabling autonomous data storage environments.

In January 2026, IBM partnered with telecom group e& to deploy enterprise-grade agentic AI solutions for governance and regulatory compliance. The collaboration focuses on implementing advanced AI agents capable of automating compliance monitoring, operational decision-making, and enterprise analytics. Announced at the World Economic Forum in Davos, the initiative demonstrates IBM's growing focus on enterprise AI ecosystems.

Functions Covered:

  • Demand Forecasting
  • Inventory Management
  • Procurement
  • Logistics & Transportation
  • Warehouse Management
  • Supplier Risk Management

Technologies Covered:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Robotic Process Automation (RPA)
  • Predictive Analytics

Deployment Modes Covered:

  • Cloud Based
  • On Premises

Organization Sizes Covered:

  • Small & Medium Enterprises (SMEs)
  • Large Enterprises

End Users Covered:

  • Automotive & Transportation
  • Retail & E-commerce
  • Manufacturing
  • Healthcare & Pharmaceuticals
  • Food & Beverage
  • Consumer Goods
  • Electronics & Semiconductors

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI Powered Supply Chain Market, By Function

  • 5.1 Demand Forecasting
  • 5.2 Inventory Management
  • 5.3 Procurement
  • 5.4 Logistics & Transportation
  • 5.5 Warehouse Management
  • 5.6 Supplier Risk Management

6 Global AI Powered Supply Chain Market, By Technology

  • 6.1 Machine Learning
  • 6.2 Natural Language Processing (NLP)
  • 6.3 Computer Vision
  • 6.4 Robotic Process Automation (RPA)
  • 6.5 Predictive Analytics

7 Global AI Powered Supply Chain Market, By Deployment Mode

  • 7.1 Cloud Based
  • 7.2 On Premises

8 Global AI Powered Supply Chain Market, By Organization Size

  • 8.1 Small & Medium Enterprises (SMEs)
  • 8.2 Large Enterprises

9 Global AI Powered Supply Chain Market, By End User

  • 9.1 Automotive & Transportation
  • 9.2 Retail & E-commerce
  • 9.3 Manufacturing
  • 9.4 Healthcare & Pharmaceuticals
  • 9.5 Food & Beverage
  • 9.6 Consumer Goods
  • 9.7 Electronics & Semiconductors

10 Global AI Powered Supply Chain Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 SAP SE
  • 13.2 Oracle Corporation
  • 13.3 IBM Corporation
  • 13.4 Microsoft Corporation
  • 13.5 Amazon Web Services (AWS)
  • 13.6 Google LLC
  • 13.7 NVIDIA Corporation
  • 13.8 Intel Corporation
  • 13.9 Siemens AG
  • 13.10 Manhattan Associates
  • 13.11 Kinaxis
  • 13.12 Blue Yonder Group
  • 13.13 Infor
  • 13.14 Descartes Systems Group
  • 13.15 E2open

List of Tables

  • Table 1 Global AI Powered Supply Chain Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI Powered Supply Chain Market Outlook, By Function (2023-2034) ($MN)
  • Table 3 Global AI Powered Supply Chain Market Outlook, By Demand Forecasting (2023-2034) ($MN)
  • Table 4 Global AI Powered Supply Chain Market Outlook, By Inventory Management (2023-2034) ($MN)
  • Table 5 Global AI Powered Supply Chain Market Outlook, By Procurement (2023-2034) ($MN)
  • Table 6 Global AI Powered Supply Chain Market Outlook, By Logistics & Transportation (2023-2034) ($MN)
  • Table 7 Global AI Powered Supply Chain Market Outlook, By Warehouse Management (2023-2034) ($MN)
  • Table 8 Global AI Powered Supply Chain Market Outlook, By Supplier Risk Management (2023-2034) ($MN)
  • Table 9 Global AI Powered Supply Chain Market Outlook, By Technology (2023-2034) ($MN)
  • Table 10 Global AI Powered Supply Chain Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 11 Global AI Powered Supply Chain Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 12 Global AI Powered Supply Chain Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 13 Global AI Powered Supply Chain Market Outlook, By Robotic Process Automation (RPA) (2023-2034) ($MN)
  • Table 14 Global AI Powered Supply Chain Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 15 Global AI Powered Supply Chain Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 16 Global AI Powered Supply Chain Market Outlook, By Cloud Based (2023-2034) ($MN)
  • Table 17 Global AI Powered Supply Chain Market Outlook, By On Premises (2023-2034) ($MN)
  • Table 18 Global AI Powered Supply Chain Market Outlook, By Organization Size (2023-2034) ($MN)
  • Table 19 Global AI Powered Supply Chain Market Outlook, By Small & Medium Enterprises (SMEs) (2023-2034) ($MN)
  • Table 20 Global AI Powered Supply Chain Market Outlook, By Large Enterprises (2023-2034) ($MN)
  • Table 21 Global AI Powered Supply Chain Market Outlook, By End User (2023-2034) ($MN)
  • Table 22 Global AI Powered Supply Chain Market Outlook, By Automotive & Transportation (2023-2034) ($MN)
  • Table 23 Global AI Powered Supply Chain Market Outlook, By Retail & E-commerce (2023-2034) ($MN)
  • Table 24 Global AI Powered Supply Chain Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 25 Global AI Powered Supply Chain Market Outlook, By Healthcare & Pharmaceuticals (2023-2034) ($MN)
  • Table 26 Global AI Powered Supply Chain Market Outlook, By Food & Beverage (2023-2034) ($MN)
  • Table 27 Global AI Powered Supply Chain Market Outlook, By Consumer Goods (2023-2034) ($MN)
  • Table 28 Global AI Powered Supply Chain Market Outlook, By Electronics & Semiconductors (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.