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市场调查报告书
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1865538

全球供应链人工智慧市场：未来预测（至2032年）－按产品、技术、应用、最终用户和地区进行分析

AI in Supply Chain Market Forecasts to 2032 - Global Analysis By Offering (Hardware, Software and Services), Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2025 年，全球供应链人工智慧市场规模将达到 100.2 亿美元，到 2032 年将达到 1,105.3 亿美元，预测期内复合年增长率将达到 40.9%。

供应链人工智慧 (AI) 指的是利用先进的演算法、机器学习模型和数据驱动技术来提高供应链营运的效率、准确性和反应速度。透过分析海量的结构化和非结构化数据，人工智慧能够实现需求预测、即时库存管理、智慧物流优化和自动化决策。透过预测中断并识别营运改善机会，它有助于降低风险、减少成本并提高客户满意度。将人工智慧整合到采购、生产、仓储和配送流程中，可以将传统的供应链转变为敏捷、弹性且智慧的网络，从而能够应对动态的市场需求和全球不确定性。

改进的库存管理

企业利用人工智慧引擎预测需求、优化库存水准并降低仓库和配销中心的持有成本。该平台支援即时追踪、异常检测以及利用历史数据和外部变数进行自动补货。与企业资源计划 (ERP) 系统、物联网感测器和物流网路的整合提高了可视性和应对力。零售、製造和医疗保健产业对预测性和自适应库存管理的需求日益增长。这些趋势正在推动以库存为中心的供应链生态系统采用该平台。

熟练劳动力短缺

熟练劳动力短缺限制了人工智慧赋能供应链的平台扩充性和营运效率。人工智慧的应用需要资料科学、机器学习和供应链方面的专业知识，但这些人才在许多地区仍然短缺。企业在招募、培养和留住管理模型、解读输出结果和协调决策所需的人才方面面临挑战。缺乏标准化培训和跨职能协作阻碍了平台的可靠性和业务影响力。这些限制因素持续阻碍中型企业和旧有系统供应链主导型企业采用人工智慧技术。

数据驱动决策

企业正利用人工智慧模拟各种场景、优化路线，并根据即时和历史数据分配资源。平台支援动态定价、供应商评分以及全球网路中的中断预测。与云端基础设施和分析仪表板的整合增强了透明度，并提升了经营团队的决策一致性。采购营运和客户互动中对扩充性的决策支援的需求日益增长。这些趋势正在推动以洞察主导、数位化成熟的供应链生态系统的整体发展。

变革阻力与组织文化

传统流程、职能孤岛和规避风险的心态正在阻碍人工智慧的普及和跨职能协作。员工可能不信任演算法决策或担心失业，导致人工智慧利用率低落和负面情绪。企业必须投资于变革管理、相关人员和管治框架，以确保目标一致和信任。经营团队缺乏理解和文化准备仍然限制着平台性能和策略影响力。

新冠疫情的感染疾病：

疫情暴露了全球供应链的脆弱性，并加速了人工智慧在提升韧性和敏捷性方面的应用。企业利用人工智慧来应对供应链中断、预测需求，并在动盪的市场环境下优化物流。各行各业对云端原生平台、远端监控和情境规划的投资激增。消费者和政策制定者对供应链风险和数位转型的认知度也显着提高。后疫情时代的策略将人工智慧定位为供应链现代化和业务连续性的核心支柱。这些变化强化了对人工智慧基础设施和决策支援系统的长期投资。

在预测期内，预测分析和机器学习领域将占据最大的市场份额。

由于预测分析和机器学习在供应链运营的预测优化和异常检测中发挥基础性作用，预计在预测期内，该领域将占据最大的市场份额。平台利用监督式和非监督式模型，实现高精度的需求预测、诈欺侦测和物流场景模拟。与即时资料来源、ERP系统和外部资料来源的集成，提高了应对力和决策灵活性。企业正在采用预测引擎来减少缺货、优化运输并预测供应商风险。供应商提供模组化引擎、API和视觉化工具，以促进跨部门应用和绩效追踪。零售、製造和医疗保健物流领域对扩充性、可解释和可适应的人工智慧的需求日益增长。

在预测期内，医疗保健和生命科学产业的复合年增长率将最高。

在预测期内，医疗保健和生命科学领域预计将保持最高的成长率，这主要得益于人工智慧平台在医药物流、医疗供应链和以病人为中心的医疗服务模式中的应用。企业正在利用人工智慧来管理低温运输规性、优化库存并预测医院和分销网路的需求。与电子健康记录 (EHR) 系统、物联网设备和法规结构的集成，增强了高价值和敏感货物的可追溯性和风险缓解能力。疫苗分发、临床试验和个人化医疗工作流程正在推动对扩充性且合规的人工智慧基础设施的需求。医疗服务提供者正在将供应链策略与病人安全、治疗依从性和基于价值的医疗指标结合。这些趋势正在推动医疗保健专用供应链平台和服务的快速成长。

占比最大的地区：

由于企业对供应链技术、数位基础设施和创新文化的大力投资，预计北美将在预测期内保持最大的市场份额。零售、製造、物流和医疗保健等行业的企业正在采用人工智慧平台，以优化营运并增强在动盪环境下的韧性。对云端迁移、资料管治和人才培养的投资正在支持各行业的扩充性发展和合规性。主要供应商、研究机构和法规结构的存在正在推动生态系统的成熟和跨产业的应用。企业正在将人工智慧策略与环境、社会和治理 (ESG) 目标、客户体验以及供应链各环节的竞争优势相结合。官民合作关係和联邦政府主导的措施正在推动人工智慧在关键基础设施和国家物流网路中的整合。

预计年复合成长率最高的地区：

预计亚太地区在预测期内将呈现最高的复合年增长率，这主要得益于工业数位化、电子商务的蓬勃发展以及医疗健康现代化在区域经济中的融合。中国、印度、日本和韩国等国家正在製造业、物流和公共卫生供应链中推广人工智慧平台。政府支持计画正在推动人工智慧在供应链应用场景中的普及、基础设施建设和Start-Ups孵化。本地供应商正在提供符合监管和营运需求、经济高效且行动优先的本地化解决方案。消费者期望的不断提高正在推动都市区和农村供应链网路中对扩充性、文化相容的人工智慧基础设施的需求。企业正在将预测引擎与智慧仓库管理、末端配送和跨境物流平台整合。

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订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球供应链人工智慧市场依产品/服务分类

  • 介绍
  • 硬体
    • 人工智慧感测器和物联网设备
    • 自主机器人与无人机
    • 边缘运算设备
  • 软体
    • 基于人工智慧的供应链平台
    • 预测分析和优化工具
    • 库存和需求管理系统
    • 运输和车队管理软体
  • 服务
    • 咨询及实施服务
    • 培训和支持
    • 託管服务

6. 全球供应链人工智慧市场（按类型划分）

  • 介绍
  • 预测分析和机器学习
  • 自然语言处理（NLP）
  • 电脑视觉
  • 数位双胞胎
  • 机器人流程自动化 (RPA)
  • 物联网和边缘人工智慧实现即时可视性
  • 用于需求规划的生成式人工智慧
  • 其他技术

7. 全球供应链人工智慧市场（按应用划分）

  • 介绍
  • 需求预测
  • 库存最佳化
  • 仓库自动化
  • 车队管理
  • 供应商关係管理
  • 风险与合规监控
  • 采购情报
  • 其他用途

8. 全球供应链人工智慧市场（按最终用户划分）

  • 介绍
  • 车
  • 零售与电子商务
  • 製造业
  • 医学与生命科​​学
  • 食品/饮料
  • 物流/运输
  • 能源与公用事业
  • 其他最终用户

9. 全球供应链人工智慧市场（按地区划分）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十一章 公司简介

  • International Business Machines Corporation（IBM）
  • Microsoft Corporation
  • Oracle Corporation
  • SAP SE
  • Amazon.com Inc.
  • Google LLC
  • Blue Yonder Group Inc.
  • C3.ai Inc.
  • Llamasoft Inc.
  • Coupa Software Inc.
  • Kinaxis Inc.
  • Manhattan Associates Inc.
  • Infor Inc.
  • Siemens AG
  • NVIDIA Corporation
Product Code: SMRC32178

According to Stratistics MRC, the Global AI in Supply Chain Market is accounted for $10.02 billion in 2025 and is expected to reach $110.53 billion by 2032 growing at a CAGR of 40.9% during the forecast period. Artificial Intelligence (AI) in supply chain refers to the use of advanced algorithms, machine learning models, and data-driven technologies to enhance the efficiency, accuracy, and responsiveness of supply chain operations. By analyzing vast volumes of structured and unstructured data, AI enables predictive demand forecasting, real-time inventory management, intelligent logistics optimization, and automated decision-making. It supports risk mitigation, cost reduction, and improved customer satisfaction by anticipating disruptions and identifying opportunities for operational improvement. Integrating AI across procurement, production, warehousing, and distribution transforms traditional supply chains into agile, resilient, and intelligent networks capable of adapting to dynamic market demands and global uncertainties.

Market Dynamics:

Driver:

Improved inventory management

Enterprises use AI engines to forecast demand optimize stock levels and reduce holding costs across warehouses and distribution centers. Platforms support real-time tracking anomaly detection and automated replenishment using historical data and external variables. Integration with ERP systems IoT sensors and logistics networks enhances visibility and responsiveness. Demand for predictive and adaptive inventory control is rising across retail manufacturing and healthcare sectors. These dynamics are propelling platform deployment across inventory-centric supply chain ecosystems.

Restraint:

Shortage of skilled workforce

Shortage of skilled workforce is limiting platform scalability and operational performance across AI-enabled supply chains. AI deployment requires expertise in data science machine learning and supply chain domain knowledge which remains scarce across many regions. Enterprises face challenges in recruiting training and retaining talent to manage models interpret outputs and align decisions. Lack of standardized training and cross-functional collaboration hampers platform reliability and business impact. These constraints continue to hinder adoption across mid-sized firms and legacy-heavy supply chain environments.

Opportunity:

Data-driven decision making

Enterprises use AI to simulate scenarios optimizes routes and allocate resources based on real-time and historical data. Platforms support dynamic pricing supplier scoring and disruption forecasting across global networks. Integration with cloud infrastructure and analytics dashboards enhances transparency and executive alignment. Demand for intelligent and scalable decision support is rising across procurement operations and customer fulfillment. These trends are fostering growth across insight-driven and digitally mature supply chain ecosystems.

Threat:

Resistance to change and organizational culture

Legacy processes siloed teams and risk-averse mindsets delay AI integration and cross-functional collaboration. Employees may distrust algorithmic decisions or fear job displacement leading to underutilization and pushback. Enterprises must invest in change management stakeholder engagement and governance frameworks to ensure alignment and trust. Lack of leadership buy-in and cultural readiness continues to constrain platform performance and strategic impact.

Covid-19 Impact:

The pandemic exposed vulnerabilities in global supply chains and accelerated AI adoption for resilience and agility. Enterprises used AI to manage disruptions forecast demand and optimize logistics under volatile conditions. Investment in cloud-native platforms remote monitoring and scenario planning surged across sectors. Public awareness of supply chain risk and digital transformation increased across consumer and policy circles. Post-pandemic strategies now include AI as a core pillar of supply chain modernization and operational continuity. These shifts are reinforcing long-term investment in AI-enabled infrastructure and decision support.

The predictive analytics & machine learning segment is expected to be the largest during the forecast period

The predictive analytics & machine learning segment is expected to account for the largest market share during the forecast period due to its foundational role in forecasting optimization and anomaly detection across supply chain workflows. Platforms use supervised and unsupervised models to predict demand detect fraud and simulate logistics scenarios with high accuracy. Integration with real-time data sources ERP systems and external feeds enhances responsiveness and decision-making agility. Enterprises deploy predictive engines to reduce stockouts optimize transportation and anticipate supplier risks. Vendors offer modular engines APIs and visualization tools to support cross-functional adoption and performance tracking. Demand for scalable explainable and adaptive AI is rising across retail manufacturing and healthcare logistics.

The healthcare & life sciences segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare & life sciences segment is predicted to witness the highest growth rate as AI platforms expand across pharmaceutical logistics medical supply chains and patient-centric delivery models. Enterprises use AI to manage cold chain compliance optimize inventory and forecast demand across hospitals and distribution networks. Integration with EHR systems IoT devices and regulatory frameworks enhances traceability and risk mitigation across sensitive and high-value shipments. Demand for scalable and compliant AI infrastructure is rising across vaccine distribution clinical trials and personalized medicine workflows. Providers are aligning supply chain strategies with patient safety treatment adherence and value-based care metrics. These dynamics are driving rapid growth across healthcare-focused supply chain platforms and services.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its enterprise investment digital infrastructure and innovation culture across supply chain technologies. Firms deploy AI platforms across retail manufacturing logistics and healthcare to optimize operations and enhance resilience under volatile conditions. Investment in cloud migration data governance and workforce development supports scalability and regulatory compliance across sectors. Presence of leading vendors research institutions and regulatory frameworks drives ecosystem maturity and cross-industry adoption. Enterprises align AI strategies with ESG goals customer experience and competitive differentiation across supply chain functions. Public-private partnerships and federal initiatives are reinforcing AI integration across critical infrastructure and national logistics networks.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as industrial digitization e-commerce expansion and healthcare modernization converge across regional economies. Countries like China India Japan and South Korea scale AI platforms across manufacturing logistics and public health supply chains. Government-backed programs support AI adoption infrastructure development and startup incubation across supply chain use cases. Local providers offer cost-effective mobile-first and regionally adapted solutions tailored to regulatory and operational needs. Demand for scalable and culturally aligned AI infrastructure is rising across urban and rural supply networks with growing consumer expectations. Enterprises are integrating predictive engines with smart warehousing last-mile delivery and cross-border logistics platforms.

Key players in the market

Some of the key players in AI in Supply Chain Market include International Business Machines Corporation (IBM), Microsoft Corporation, Oracle Corporation, SAP SE, Amazon.com Inc., Google LLC, Blue Yonder Group Inc., C3.ai Inc., Llamasoft Inc., Coupa Software Inc., Kinaxis Inc., Manhattan Associates Inc., Infor Inc., Siemens AG and NVIDIA Corporation.

Key Developments:

In October 2025, IBM announced a strategic alliance with S&P Global to embed watsonx Orchestrate agentic AI into S&P's supply chain offerings. The partnership aimed to enhance vendor selection, procurement intelligence, and country risk modeling using AI-powered agents. This collaboration marked a major step in combining enterprise-grade orchestration with real-time supply chain data.

In April 2025, Microsoft launched AI-powered Copilot features for Dynamics 365 Supply Chain Management, transforming procurement, planning, and logistics workflows. The release included real-time transportation insights, intelligent demand forecasting, and vendor rebate automation, replacing manual processes with predictive AI. These tools improved visibility, reduced delays, and enhanced decision-making across global supply networks.

Offerings Covered:

  • Hardware
  • Software
  • Services

Technologies Covered:

  • Predictive Analytics & Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Digital Twins
  • Robotic Process Automation (RPA)
  • IoT & Edge AI for Real-Time Visibility
  • Generative AI for Demand Planning
  • Other Technologies

Applications Covered:

  • Demand Forecasting
  • Inventory Optimization
  • Warehouse Automation
  • Fleet Management
  • Supplier Relationship Management
  • Risk & Compliance Monitoring
  • Procurement Intelligence
  • Other Applications

End Users Covered:

  • Automotive
  • Retail & E-Commerce
  • Manufacturing
  • Healthcare & Life Sciences
  • Food & Beverage
  • Logistics & Transportation
  • Energy & Utilities
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & 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 2024, 2025, 2026, 2028, and 2032
  • 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

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI in Supply Chain Market, By Offering

  • 5.1 Introduction
  • 5.2 Hardware
    • 5.2.1 AI-Enabled Sensors & IoT Devices
    • 5.2.2 Autonomous Robots & Drones
    • 5.2.3 Edge Computing Devices
  • 5.3 Software
    • 5.3.1 AI-Based Supply Chain Platforms
    • 5.3.2 Predictive Analytics & Optimization Tools
    • 5.3.3 Inventory & Demand Management Systems
    • 5.3.4 Transportation & Fleet Management Software
  • 5.4 Services
    • 5.4.1 Consulting & Implementation Services
    • 5.4.2 Training & Support
    • 5.4.3 Managed Services

6 Global AI in Supply Chain Market, By Technology

  • 6.1 Introduction
  • 6.2 Predictive Analytics & Machine Learning
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Computer Vision
  • 6.5 Digital Twins
  • 6.6 Robotic Process Automation (RPA)
  • 6.7 IoT & Edge AI for Real-Time Visibility
  • 6.8 Generative AI for Demand Planning
  • 6.9 Other Technologies

7 Global AI in Supply Chain Market, By Application

  • 7.1 Introduction
  • 7.2 Demand Forecasting
  • 7.3 Inventory Optimization
  • 7.4 Warehouse Automation
  • 7.5 Fleet Management
  • 7.6 Supplier Relationship Management
  • 7.7 Risk & Compliance Monitoring
  • 7.8 Procurement Intelligence
  • 7.9 Other Applications

8 Global AI in Supply Chain Market, By End User

  • 8.1 Introduction
  • 8.2 Automotive
  • 8.3 Retail & E-Commerce
  • 8.4 Manufacturing
  • 8.5 Healthcare & Life Sciences
  • 8.6 Food & Beverage
  • 8.7 Logistics & Transportation
  • 8.8 Energy & Utilities
  • 8.9 Other End Users

9 Global AI in Supply Chain Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 International Business Machines Corporation (IBM)
  • 11.2 Microsoft Corporation
  • 11.3 Oracle Corporation
  • 11.4 SAP SE
  • 11.5 Amazon.com Inc.
  • 11.6 Google LLC
  • 11.7 Blue Yonder Group Inc.
  • 11.8 C3.ai Inc.
  • 11.9 Llamasoft Inc.
  • 11.10 Coupa Software Inc.
  • 11.11 Kinaxis Inc.
  • 11.12 Manhattan Associates Inc.
  • 11.13 Infor Inc.
  • 11.14 Siemens AG
  • 11.15 NVIDIA Corporation

List of Tables

  • Table 1 Global AI in Supply Chain Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Supply Chain Market Outlook, By Offering (2024-2032) ($MN)
  • Table 3 Global AI in Supply Chain Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global AI in Supply Chain Market Outlook, By AI-Enabled Sensors & IoT Devices (2024-2032) ($MN)
  • Table 5 Global AI in Supply Chain Market Outlook, By Autonomous Robots & Drones (2024-2032) ($MN)
  • Table 6 Global AI in Supply Chain Market Outlook, By Edge Computing Devices (2024-2032) ($MN)
  • Table 7 Global AI in Supply Chain Market Outlook, By Software (2024-2032) ($MN)
  • Table 8 Global AI in Supply Chain Market Outlook, By AI-Based Supply Chain Platforms (2024-2032) ($MN)
  • Table 9 Global AI in Supply Chain Market Outlook, By Predictive Analytics & Optimization Tools (2024-2032) ($MN)
  • Table 10 Global AI in Supply Chain Market Outlook, By Inventory & Demand Management Systems (2024-2032) ($MN)
  • Table 11 Global AI in Supply Chain Market Outlook, By Transportation & Fleet Management Software (2024-2032) ($MN)
  • Table 12 Global AI in Supply Chain Market Outlook, By Services (2024-2032) ($MN)
  • Table 13 Global AI in Supply Chain Market Outlook, By Consulting & Implementation Services (2024-2032) ($MN)
  • Table 14 Global AI in Supply Chain Market Outlook, By Training & Support (2024-2032) ($MN)
  • Table 15 Global AI in Supply Chain Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 16 Global AI in Supply Chain Market Outlook, By Technology (2024-2032) ($MN)
  • Table 17 Global AI in Supply Chain Market Outlook, By Predictive Analytics & Machine Learning (2024-2032) ($MN)
  • Table 18 Global AI in Supply Chain Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 19 Global AI in Supply Chain Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 20 Global AI in Supply Chain Market Outlook, By Digital Twins (2024-2032) ($MN)
  • Table 21 Global AI in Supply Chain Market Outlook, By Robotic Process Automation (RPA) (2024-2032) ($MN)
  • Table 22 Global AI in Supply Chain Market Outlook, By IoT & Edge AI for Real-Time Visibility (2024-2032) ($MN)
  • Table 23 Global AI in Supply Chain Market Outlook, By Generative AI for Demand Planning (2024-2032) ($MN)
  • Table 24 Global AI in Supply Chain Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 25 Global AI in Supply Chain Market Outlook, By Application (2024-2032) ($MN)
  • Table 26 Global AI in Supply Chain Market Outlook, By Demand Forecasting (2024-2032) ($MN)
  • Table 27 Global AI in Supply Chain Market Outlook, By Inventory Optimization (2024-2032) ($MN)
  • Table 28 Global AI in Supply Chain Market Outlook, By Warehouse Automation (2024-2032) ($MN)
  • Table 29 Global AI in Supply Chain Market Outlook, By Fleet Management (2024-2032) ($MN)
  • Table 30 Global AI in Supply Chain Market Outlook, By Supplier Relationship Management (2024-2032) ($MN)
  • Table 31 Global AI in Supply Chain Market Outlook, By Risk & Compliance Monitoring (2024-2032) ($MN)
  • Table 32 Global AI in Supply Chain Market Outlook, By Procurement Intelligence (2024-2032) ($MN)
  • Table 33 Global AI in Supply Chain Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 34 Global AI in Supply Chain Market Outlook, By End User (2024-2032) ($MN)
  • Table 35 Global AI in Supply Chain Market Outlook, By Automotive (2024-2032) ($MN)
  • Table 36 Global AI in Supply Chain Market Outlook, By Retail & E-Commerce (2024-2032) ($MN)
  • Table 37 Global AI in Supply Chain Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 38 Global AI in Supply Chain Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 39 Global AI in Supply Chain Market Outlook, By Food & Beverage (2024-2032) ($MN)
  • Table 40 Global AI in Supply Chain Market Outlook, By Logistics & Transportation (2024-2032) ($MN)
  • Table 41 Global AI in Supply Chain Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 42 Global AI in Supply Chain Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.