查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
2007799

人工智慧驱动的供应链市场预测至2034年：按功能、技术、部署类型、组织规模、最终用户和地区分類的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2026 年，全球人工智慧驱动的供应链市场将达到 141.1 亿美元，在预测期内以 40.9% 的复合年增长率增长，到 2034 年将达到 2,193.1 亿美元。

人工智慧驱动的供应链是指将机器学习、预测分析和自动化等先进的人工智慧技术整合到供应链营运中，以提高效率、准确性和应对力。这使得企业能够透过数据驱动的洞察，实现即时需求预测、智慧库存管理、路线优化和风险规避。透过利用大量资料集和自主决策，企业可以降低营运成本、提升客户满意度并增强敏捷性。这种方法将传统的供应链转变为自适应的自学习系统，能够预测快速变化的市场环境中可能出现的干扰，并优化端到端的物流绩效。

对效率和成本优化的需求日益增长

各行各业的组织都面临着在维持服务品质的同时精简营运、降低成本的挑战。人工智慧驱动的供应链能够实现即时分析、预测规划和自动化，从而显着提高营运效率。透过最大限度地减少浪费、优化库存水准并提高需求预测的准确性，企业可以大幅节省成本。此外，人工智慧洞察有助于加快决策速度并优化资源分配，使供应链更加敏捷和灵活，从而能够适应不断变化的市场需求和全球性挑战。

实施成本高且复杂

实施人工智慧驱动的供应链解决方案需要前期在基础设施、软体和专业人才方面进行大量投资。将人工智慧技术与旧有系统整合在技术上十分复杂，耗时较长，而且通常需要进行大量的客製化开发。中小企业在实施此类先进系统时可能会面临财务和营运方面的限制。此外，持续的维护、资料管理和系统升级需求进一步增加了整体拥有成本，从而阻碍了此类解决方案的广泛应用。

提高数据可用性和连接性

物联网设备、数位平台和互联繫统驱动的资料产生量快速成长，为人工智慧驱动的供应链带来了巨大的发展机会。连接性的提升使得整个供应链网路能够实现即时资料共用，从而增强可视性和协作性。人工智慧演算法可以利用这些海量资料产生可执行的洞察，提高预测精度，并优化物流运营。随着数位生态系统的扩展，企业可以利用数据驱动的智慧来建立更智慧、反应更迅速、高度整合的供应链基础设施。

资料隐私和网路安全问题

人工智慧驱动的供应链高度依赖资料交换和数位连接，因此也越来越容易受到网路威胁和资料外洩的攻击。包括供应商资料和营运指标在内的敏感商业资讯可能遭到未授权存取。确保强大的网路安全态势并遵守资料保护条例至关重要，但挑战依然存在。安全漏洞可能导致营运中断、品牌声誉受损和经济损失，最终阻碍人工智慧主导的供应链技术的应用。

新冠疫情的影响：

新冠疫情显着加速了人工智慧驱动型供应链的普及，因为全球物流和需求模式面临前所未有的衝击。企业更加依赖人工智慧解决方案，以实现即时视觉化、预测分析和风险缓解，从而应对供应链的不确定性。这场危机凸显了传统系统的局限性，并促使企业加强对自动化和数位转型的投资。即使在疫情过后，企业仍优先考虑建立具有韧性、灵活性和智慧化的供应链模式，以应对未来的挑战和不断变化的市场环境。

在预测期内，电脑视觉领域预计将占据最大的市场份额。

预计在预测期内，电脑视觉领域将占据最大的市场份额，因为它在提升营运视觉性和自动化方面发挥着至关重要的作用。这项技术透过影像识别和影像分析，能够对货物、仓库营运和品质检测进行即时监控。这有助于减少人为错误，提高准确性，并加快决策速度。在库存追踪、缺陷检测和物流优化方面的广泛应用，显着巩固了该领域在人工智慧驱动的供应链解决方案中的主导地位。

预计库存管理细分市场在预测期内将呈现最高的复合年增长率。

在预测期内，库存管理领域预计将呈现最高的成长率，这主要得益于对即时库存可见度和高效资源利用的需求不断增长。人工智慧驱动的库存管理系统能够增强需求预测能力、将补货流程自动化，并最大限度地减少缺货和库存积压。越来越多的企业正在采用这些解决方案来提高营运效率和客户满意度。全球供应链日益复杂化进一步推动了对智慧库存优化的需求，从而促进了该领域的快速普及和高速成长。

市占率最大的地区：

在预测期内，北美预计将占据最大的市场份额，这得益于其对先进技术的早期应用以及主要市场参与者的强大影响力。该地区受益于完善的数位基础设施、对人工智慧研究的大量投资以及各行业自动化技术的广泛应用。此外，企业对供应链韧性和效率日益增长的关注也推动了对人工智慧解决方案的需求，进一步巩固了北美在不断变化的市场格局中的领先地位。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于快速的工业化、蓬勃发展的电子商务以及不断推进的数位转型。该地区各国正大力投资智慧物流和人工智慧技术，以提高供应链效率。随着物联网和数据分析的日益普及以及政府政策的支持，市场成长正在进一步加速。随着企业寻求可扩展且经济高效的解决方案，人工智慧驱动的供应链在亚太地区展现出强劲的发展势头。

免费客製化服务：

所有购买此报告的客户均可享受以下免费自订选项之一：

  • 企业概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 应客户要求，我们提供主要国家和地区的市场估算和预测，以及复合年增长率（註：需进行可行性检查）。
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章执行摘要

  • 市场概览及主要亮点
  • 驱动因素、挑战与机会
  • 竞争格局概述
  • 战略洞察与建议

第二章：研究框架

  • 研究目标和范围
  • 相关人员分析
  • 研究假设和限制
  • 调查方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 技术与创新展望
  • 新兴市场/高成长市场
  • 监管和政策环境
  • 新冠疫情的影响及復苏前景

第四章：竞争环境与策略评估

  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 替代品的威胁
    • 新进入者的威胁
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章：全球人工智慧驱动的供应链市场：按功能划分

  • 需求预测
  • 库存管理
  • 采购
  • 物流/运输
  • 仓库管理
  • 供应商风险管理

第六章：全球人工智慧驱动的供应链市场：依技术划分

  • 机器学习
  • 自然语言处理（NLP）
  • 电脑视觉
  • 机器人流程自动化 (RPA)
  • 预测分析

第七章：全球人工智慧驱动的供应链市场：依部署模式划分

  • 基于云端的
  • 现场

第八章：全球人工智慧驱动的供应链市场：按组织规模划分

  • 中小企业
  • 大公司

第九章：全球人工智慧驱动的供应链市场：按最终用户划分

  • 汽车和运输业
  • 零售与电子商务
  • 製造业
  • 医疗和药品
  • 食品/饮料
  • 消费品
  • 电子和半导体

第十章：全球人工智慧驱动的供应链市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十一章 策略市场资讯

  • 工业价值网络和供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十二章 产业趋势与策略倡议

  • 併购
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十三章：公司简介

  • 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.