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市场调查报告书
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2021753

人工智慧市场预测：供应链优化（2034 年）—按组件、技术、应用、最终用户和地区分類的全球分析

AI in Supply Chain Optimization Market Forecasts to 2034 - Global Analysis By Component (Software, Hardware, and Services), Technology, Application, 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，全球供应链优化人工智慧市场预计将在 2026 年达到 125 亿美元，到 2034 年达到 950 亿美元，预测期内复合年增长率为 30%。

人工智慧在供应链优化的应用，运用先进的演算法、机器学习和数据分析，提升供应链营运的效率、准确性和应对力。这有助于需求预测、库存管理、路线优化和即时决策。透过处理大量结构化和非结构化数据，人工智慧能够降低营运成本、减少风险、简化工作流程，进而提升整个供应链的整体绩效和客户满意度。

全球供应链日益复杂，以及对即时视觉性的需求日益增长

现代供应链跨越多个地区，涉及众多供应商、承运商和法规环境。这种复杂性导致资料孤岛和决策延迟。利用人工智慧可以实现即时货物追踪、自动异常处理，并根据天气和交通状况动态调整路线。随着客户对更快交付和更透明的进度资讯的期望不断提高，企业正在部署人工智慧驱动的控制塔和预测分析。这些工具提供端到端的可视性，帮助企业主动解决瓶颈问题并缩短前置作业时间。跨境电商交易量的不断增长进一步增加了对智慧供应链调整的需求，使人工智慧成为在动盪的市场中保持竞争优势的关键工具。

高昂的实施成本和资料整合挑战

将人工智慧解决方案应用于供应链需要对物联网感测器、边缘设备、云端基础设施和熟练人员进行大量投资。由于缺乏标准化的资料格式，将人工智慧平台与众多旧有系统整合既复杂又耗时。中小企业往往难以证明这些初始成本的合理性。此外，数据品质问题，例如记录不完整或不一致，会导致预测不准确，并削弱人们对人工智慧输出结果的信心。重新培训员工以操作人工智慧驱动的系统也会增加成本。如果无法清楚证明投资报酬率，并且无法与现有的ERP和WMS平台无缝互通性，人工智慧的普及速度将依然缓慢，尤其是在传统产业这种格局分散的领域。

扩展生成式人工智慧在自主供应链决策中的应用

生成式人工智慧透过实现场景模拟、自动化合约谈判和动态补货策略，为供应链优化开闢了新的可能性。与传统的预测模型不同，生成式人工智慧能够针对各种突发情况提案创新解决方案，例如替代采购路线和库存重新分配计划。数位双胞胎技术的普及与生成式人工智慧的结合，使得企业能够在虚拟环境中测试无数种「假设」情景，然后再将其应用于现实世界。此外，人工智慧聊天机器人正在改善与供应商的沟通和订单追踪。随着云端人工智慧平台的成本日益降低，中型物流供应商无需巨额资本投入即可获得这些功能，从而为零售、製造和医疗保健行业创造了巨大的市场扩张机会。

网路安全漏洞和对黑盒模型的过度依赖

供应链优化中的人工智慧系统通常会聚合供应商定价、库存水准和客户位置等敏感数据，这使其成为网路攻击的主要目标。一旦人工智慧模型遭到破坏，就可能导致需求预测不准确、配送路线错误，甚至库存操纵。此外，许多先进的人工智慧演算法以「黑箱」的形式运行，其决策过程缺乏透明度。这种缺乏可解释性的做法会引发供应链管理人员的信任危机，尤其是在监管审计或出现错误时。过度依赖人工智慧而缺乏人工监督会加剧系统性风险，例如多个地点同时出现缺货。应对这些威胁需要强大的网路安全框架和可解释的人工智慧技术。

新冠疫情的影响：

新冠疫情揭露了全球供应链的关键脆弱性，包括过度依赖单一供应商和缺乏即时可视性。封锁和劳动力短缺扰乱了製造业和物流业，迫切需要采用人工智慧进行需求预测和风险监控。许多公司加快了对预测分析的投资，以应对消费行为的波动和原材料供应的不确定性。疫情后，供应链韧性成为经营团队的首要任务，推动了对人工智慧解决方案的持续需求。儘管在危机高峰期预算有限，但在復苏阶段，基于云端的人工智慧应用激增。疫情使人工智慧供应链市场受益，因为它永久地将焦点从单纯的成本优化转移到韧性和敏捷性。

在预测期内，软体领域预计将成为规模最大的领域。

预计软体领域将占据最大的市场份额，这主要得益于人工智慧平台、仓库管理系统 (WMS) 和需求预测工具的广泛应用。这些软体解决方案构成了智慧供应链的核心，能够实现资料聚合、演算法执行和使用者友好的仪表板。与硬体不同，软体具有扩充性和可随时更新的特性，使其成为企业的理想选择。机器学习库和云端供应链计画套件的持续创新进一步巩固了软体的优势。

在预测期内，边缘运算设备细分市场预计将呈现最高的复合年增长率。

在供应链营运中，需要在靠近资料来源（例如仓库、车辆和生产线）的地方进行即时处理，这使得边缘运算设备有望展现出最高的成长速度。边缘设备透过在本地分析RFID、摄影机和感测器数据，无需将所有数据发送到云端，即可降低延迟和频宽成本。自动堆高机、库存无人机和智慧托盘的兴起，正在加速对环境适应性强的边缘硬体的需求。此外，5G的普及也提高了设备间的通讯速度。在低温运输监控和准时性至关重要的物流领域，边缘运算能够立即侦测异常情况，使其成为人工智慧驱动的供应链优化领域成长最快的硬体类别。

市占率最大的地区：

在整个预测期内，北美预计将保持最大的市场份额。这主要得益于北美对先进技术的早期应用、AWS和微软等主要云端服务供应商的存在，以及竞争激烈的电子商务环境。美国在人工智慧驱动的仓库自动化领域处于主导地位，亚马逊和沃尔玛等公司已树立了行业标竿。大量创业投资涌入供应链人工智慧Start-Ups，以及成熟的物流基础设施，进一步巩固了这一优势。此外，政府主导的旨在增强后疫情时代供应链韧性的倡议，推动了製造业和零售业对预测分析和数数位双胞胎的投资，从而巩固了北美的主导地位。

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进程、中国和印度电子商务的蓬勃发展以及不断上涨的人事费用推动了自动化进程。日本、韩国和新加坡等国家正大力投资智慧工厂和人工智慧物流园区。该地区庞大的製造业基础产生了大量数据，使其成为人工智慧驱动优化的理想之地。随着后疫情时代供应链日益本地化，亚太企业正在寻求人工智慧解决方案，以平衡成本、速度和韧性，这推动了该地区的快速成长。

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：全球供应链优化中的人工智慧市场：按组件划分

  • 软体
    • 人工智慧平台和分析工具
    • 仓库管理系统（WMS）
    • 供应链计画与执行软体
    • 运输管理系统（TMS）
    • 需求预测与库存优化软体
  • 硬体
    • 人工智慧晶片和处理器
    • 自动驾驶汽车和无人机
    • 物联网感测器和射频识别
    • 边缘运算设备
  • 服务
    • 咨询和策略服务
    • 託管服务
    • 整合和配置服务
    • 培训和支援服务

第六章：全球供应链优化中的人工智慧市场：按技术划分

  • 机器学习（ML）
  • 人工智慧世代
  • 深度学习
  • 预测分析
  • 自然语言处理（NLP）
  • 强化学习
  • 电脑视觉

第七章：全球供应链优化中的人工智慧市场：按应用领域划分

  • 需求预测与规划
  • 风险管理与韧性
  • 库存最佳化
  • 供应商和采购管理
  • 仓库自动化
  • 运输和物流优化
  • 其他用途

第八章：全球供应链优化中的人工智慧市场：按最终用户划分

  • 零售与电子商务
  • 製造业
  • 食品/饮料
  • 医疗和药品
  • 车
  • 物流/运输
  • 其他最终用户

第九章：全球供应链优化中的人工智慧市场：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

第十章 战略市场资讯

  • 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

第十一章 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

第十二章：公司简介

  • IBM Corporation
  • o9 Solutions, Inc.
  • Microsoft Corporation
  • Manhattan Associates
  • Google LLC
  • Coupa Software
  • Amazon Web Services（AWS）
  • C3.ai
  • Oracle Corporation
  • Kinaxis Inc.
  • SAP SE
  • Blue Yonder Group, Inc.
  • NVIDIA Corporation
  • Logility, Inc.
  • Intel Corporation
Product Code: SMRC35018

According to Stratistics MRC, the Global AI in Supply Chain Optimization Market is accounted for $12.5 billion in 2026 and is expected to reach $95.0 billion by 2034, growing at a CAGR of 30% during the forecast period. AI in supply chain optimization is the application of advanced algorithms, machine learning, and data analytics to improve the efficiency, accuracy, and responsiveness of supply chain operations. It supports demand forecasting, inventory management, route optimization, and real-time decision-making. By processing large volumes of structured and unstructured data, it helps reduce operational costs, mitigate risks, and streamline workflows, leading to enhanced overall performance and improved customer satisfaction across the supply chain.

Market Dynamics:

Driver:

Rising complexity of global supply chains and need for real-time visibility

Modern supply chains span multiple geographies, involving numerous suppliers, carriers, and regulatory environments. This complexity creates data silos and delays in decision-making. AI enables real-time tracking of shipments, automated exception handling, and dynamic rerouting based on weather or traffic conditions. With increasing customer expectations for faster deliveries and transparent updates, companies are adopting AI-driven control towers and predictive analytics. These tools provide end-to-end visibility, helping firms proactively address bottlenecks and reduce lead times. The growing volume of cross-border e-commerce further amplifies the need for intelligent supply chain orchestration, making AI an indispensable tool for maintaining competitive advantage in volatile markets.

Restraint:

High implementation costs and data integration challenges

Deploying AI solutions in supply chains requires substantial investment in IoT sensors, edge devices, cloud infrastructure, and skilled personnel. Many legacy systems lack standardized data formats, making integration with AI platforms complex and time-consuming. Small and medium-sized enterprises often struggle to justify these upfront costs. Additionally, data quality issues such as incomplete or inconsistent records can lead to inaccurate predictions, undermining trust in AI outputs. Retraining workforce to operate AI-driven systems also adds to expenses. Without clear ROI demonstration and seamless interoperability between existing ERP and WMS platforms, adoption remains slow, particularly in traditional industries with fragmented technology landscapes.

Opportunity:

Expansion of generative AI for autonomous supply chain decision-making

Generative AI is opening new frontiers in supply chain optimization by enabling scenario simulation, automated contract negotiation, and dynamic replenishment strategies. Unlike traditional predictive models, generative AI can propose novel solutions to disruptions, such as alternative sourcing routes or inventory redistribution plans. The growth of digital twins combined with generative AI allows companies to test countless "what-if" scenarios in virtual environments before real-world execution. Furthermore, AI-powered chatbots are improving supplier communication and order tracking. As cloud-based AI platforms become more affordable, mid-sized logistics providers can access these capabilities without massive capital expenditure, creating significant opportunities for market expansion across retail, manufacturing, and healthcare sectors.

Threat:

Cybersecurity vulnerabilities and over-reliance on black-box models

AI systems in supply chain optimization often aggregate sensitive data, including supplier pricing, inventory levels, and customer locations, making them attractive targets for cyberattacks. A compromised AI model could lead to false demand forecasts, misrouted shipments, or inventory manipulation. Additionally, many advanced AI algorithms operate as "black boxes," offering little transparency into how decisions are made. This lack of explainability creates trust issues among supply chain managers, especially during regulatory audits or when errors occur. Over-reliance on AI without human oversight can amplify systemic risks, such as simultaneous stockouts across multiple locations. Addressing these threats requires robust cybersecurity frameworks and explainable AI techniques.

Covid-19 Impact:

The COVID-19 pandemic exposed critical weaknesses in global supply chains, including over-reliance on single-source suppliers and lack of real-time visibility. Lockdowns and labor shortages disrupted manufacturing and logistics, prompting urgent adoption of AI for demand sensing and risk monitoring. Many companies accelerated investments in predictive analytics to manage volatile consumer behavior and raw material availability. Post-pandemic, supply chain resilience has become a board-level priority, driving sustained demand for AI solutions. While initial budgets were constrained during peak crisis, the recovery phase saw a surge in cloud-based AI deployments. The pandemic permanently shifted focus from cost-only optimization to resilience and agility, benefiting the AI supply chain market.

The software segment is expected to be the largest during the forecast period

The software segment is projected to hold the largest market share, driven by widespread adoption of AI platforms, warehouse management systems (WMS), and demand forecasting tools. These software solutions form the brain of intelligent supply chains, enabling data aggregation, algorithm execution, and user-friendly dashboards. Unlike hardware, software offers scalability and regular over-the-air updates, making it attractive for enterprises. Continuous innovation in machine learning libraries and cloud-based supply chain planning suites further cements software dominance.

The edge computing devices segment is expected to have the highest CAGR during the forecast period

The edge computing devices are anticipated to witness the highest growth rate, as supply chain operations require real-time processing closer to data sources like warehouses, vehicles, and production lines. Edge devices reduce latency and bandwidth costs by analyzing RFID, camera, and sensor data locally without sending everything to the cloud. The rise of autonomous forklifts, drones for inventory counting, and smart pallets accelerates demand for ruggedized edge hardware. Additionally, 5G expansion enables faster device-to-device communication. For cold chain monitoring and time-sensitive logistics, edge computing ensures immediate anomaly detection, making it the fastest-growing hardware category within AI supply chain optimization.

Region with largest share:

During the forecast period, North America is expected to hold the largest market share, driven by early adoption of advanced technologies, presence of major cloud providers like AWS and Microsoft, and a highly competitive e-commerce landscape. The United States leads in AI-driven warehouse automation with companies like Amazon and Walmart setting benchmarks. Strong venture capital funding for supply chain AI startups and mature logistics infrastructure further support dominance. Additionally, government initiatives for supply chain resilience post-pandemic encourage investments in predictive analytics and digital twins across manufacturing and retail sectors, solidifying North America's leading position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid industrialization, booming e-commerce in China and India, and increasing labor costs pushing automation. Countries like Japan, South Korea, and Singapore are investing heavily in smart factories and AI-powered logistics parks. The region's vast manufacturing base generates massive data volumes, ideal for AI optimization. As supply chains become more regionalized post-pandemic, APAC companies seek AI solutions to balance cost, speed, and resilience, driving the fastest growth.

Key players in the market

Some of the key players in AI in Supply Chain Optimization Market include IBM Corporation, o9 Solutions, Inc., Microsoft Corporation, Manhattan Associates, Google LLC, Coupa Software, Amazon Web Services (AWS), C3.ai, Oracle Corporation, Kinaxis Inc., SAP SE, Blue Yonder Group, Inc., NVIDIA Corporation, Logility, Inc., and Intel Corporation.

Key Developments:

In April 2026, IBM announced a strategic collaboration with Arm to develop new dual-architecture hardware that helps enterprises run future AI and data intensive workloads with greater flexibility, reliability, and security. IBM's leadership in system design, from silicon to software and security, has helped enterprises adopt emerging technologies with the scale and reliability required for mission-critical workloads.

In March 2026, Oracle announced the latest updates to Oracle AI Agent Studio for Fusion Applications, a complete development platform for building, connecting, and running AI automation and agentic applications. The latest updates to Oracle AI Agent Studio include a new agentic applications builder as well as new capabilities that support workflow orchestration, content intelligence, contextual memory, and ROI measurement.

Components Covered:

  • Software
  • Hardware
  • Services

Technologies Covered:

  • Machine Learning (ML)
  • Generative AI
  • Deep Learning
  • Predictive Analytics
  • Natural Language Processing (NLP)
  • Reinforcement Learning
  • Computer Vision

Applications Covered:

  • Demand Forecasting & Planning
  • Risk Management & Resilience
  • Inventory Optimization
  • Supplier & Procurement Management
  • Warehouse Automation
  • Transportation & Logistics Optimization
  • Other Applications

End Users Covered:

  • Retail & E-commerce
  • Manufacturing
  • Food & Beverage
  • Healthcare & Pharmaceuticals
  • Automotive
  • Logistics & Transportation
  • 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 2023, 2024, 2025, 2026, 2027, 2028, 2029, 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 in Supply Chain Optimization Market, By Component

  • 5.1 Software
    • 5.1.1 AI Platforms & Analytics Tools
    • 5.1.2 Warehouse Management Systems (WMS)
    • 5.1.3 Supply Chain Planning & Execution Software
    • 5.1.4 Transportation Management Systems (TMS)
    • 5.1.5 Demand Forecasting & Inventory Optimization Software
  • 5.2 Hardware
    • 5.2.1 AI Chips & Processors
    • 5.2.2 Autonomous Vehicles & Drones
    • 5.2.3 IoT Sensors & RFID
    • 5.2.4 Edge Computing Devices
  • 5.3 Services
    • 5.3.1 Consulting & Strategy Services
    • 5.3.2 Managed Services
    • 5.3.3 Integration & Deployment Services
    • 5.3.4 Training & Support Services

6 Global AI in Supply Chain Optimization Market, By Technology

  • 6.1 Machine Learning (ML)
  • 6.2 Generative AI
  • 6.3 Deep Learning
  • 6.4 Predictive Analytics
  • 6.5 Natural Language Processing (NLP)
  • 6.6 Reinforcement Learning
  • 6.7 Computer Vision

7 Global AI in Supply Chain Optimization Market, By Application

  • 7.1 Demand Forecasting & Planning
  • 7.2 Risk Management & Resilience
  • 7.3 Inventory Optimization
  • 7.4 Supplier & Procurement Management
  • 7.5 Warehouse Automation
  • 7.6 Transportation & Logistics Optimization
  • 7.7 Other Applications

8 Global AI in Supply Chain Optimization Market, By End User

  • 8.1 Retail & E-commerce
  • 8.2 Manufacturing
  • 8.3 Food & Beverage
  • 8.4 Healthcare & Pharmaceuticals
  • 8.5 Automotive
  • 8.6 Logistics & Transportation
  • 8.7 Other End Users

9 Global AI in Supply Chain Optimization Market, By Geography

  • 9.1 North America
    • 9.1.1 United States
    • 9.1.2 Canada
    • 9.1.3 Mexico
  • 9.2 Europe
    • 9.2.1 United Kingdom
    • 9.2.2 Germany
    • 9.2.3 France
    • 9.2.4 Italy
    • 9.2.5 Spain
    • 9.2.6 Netherlands
    • 9.2.7 Belgium
    • 9.2.8 Sweden
    • 9.2.9 Switzerland
    • 9.2.10 Poland
    • 9.2.11 Rest of Europe
  • 9.3 Asia Pacific
    • 9.3.1 China
    • 9.3.2 Japan
    • 9.3.3 India
    • 9.3.4 South Korea
    • 9.3.5 Australia
    • 9.3.6 Indonesia
    • 9.3.7 Thailand
    • 9.3.8 Malaysia
    • 9.3.9 Singapore
    • 9.3.10 Vietnam
    • 9.3.11 Rest of Asia Pacific
  • 9.4 South America
    • 9.4.1 Brazil
    • 9.4.2 Argentina
    • 9.4.3 Colombia
    • 9.4.4 Chile
    • 9.4.5 Peru
    • 9.4.6 Rest of South America
  • 9.5 Rest of the World (RoW)
    • 9.5.1 Middle East
      • 9.5.1.1 Saudi Arabia
      • 9.5.1.2 United Arab Emirates
      • 9.5.1.3 Qatar
      • 9.5.1.4 Israel
      • 9.5.1.5 Rest of Middle East
    • 9.5.2 Africa
      • 9.5.2.1 South Africa
      • 9.5.2.2 Egypt
      • 9.5.2.3 Morocco
      • 9.5.2.4 Rest of Africa

10 Strategic Market Intelligence

  • 10.1 Industry Value Network and Supply Chain Assessment
  • 10.2 White-Space and Opportunity Mapping
  • 10.3 Product Evolution and Market Life Cycle Analysis
  • 10.4 Channel, Distributor, and Go-to-Market Assessment

11 Industry Developments and Strategic Initiatives

  • 11.1 Mergers and Acquisitions
  • 11.2 Partnerships, Alliances, and Joint Ventures
  • 11.3 New Product Launches and Certifications
  • 11.4 Capacity Expansion and Investments
  • 11.5 Other Strategic Initiatives

12 Company Profiles

  • 12.1 IBM Corporation
  • 12.2 o9 Solutions, Inc.
  • 12.3 Microsoft Corporation
  • 12.4 Manhattan Associates
  • 12.5 Google LLC
  • 12.6 Coupa Software
  • 12.7 Amazon Web Services (AWS)
  • 12.8 C3.ai
  • 12.9 Oracle Corporation
  • 12.10 Kinaxis Inc.
  • 12.11 SAP SE
  • 12.12 Blue Yonder Group, Inc.
  • 12.13 NVIDIA Corporation
  • 12.14 Logility, Inc.
  • 12.15 Intel Corporation

List of Tables

  • Table 1 Global AI in Supply Chain Optimization Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Supply Chain Optimization Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Supply Chain Optimization Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI in Supply Chain Optimization Market Outlook, By AI Platforms & Analytics Tools (2023-2034) ($MN)
  • Table 5 Global AI in Supply Chain Optimization Market Outlook, By Warehouse Management Systems (WMS) (2023-2034) ($MN)
  • Table 6 Global AI in Supply Chain Optimization Market Outlook, By Supply Chain Planning & Execution Software (2023-2034) ($MN)
  • Table 7 Global AI in Supply Chain Optimization Market Outlook, By Transportation Management Systems (TMS) (2023-2034) ($MN)
  • Table 8 Global AI in Supply Chain Optimization Market Outlook, By Demand Forecasting & Inventory Optimization Software (2023-2034) ($MN)
  • Table 9 Global AI in Supply Chain Optimization Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 10 Global AI in Supply Chain Optimization Market Outlook, By AI Chips & Processors (2023-2034) ($MN)
  • Table 11 Global AI in Supply Chain Optimization Market Outlook, By Autonomous Vehicles & Drones (2023-2034) ($MN)
  • Table 12 Global AI in Supply Chain Optimization Market Outlook, By IoT Sensors & RFID (2023-2034) ($MN)
  • Table 13 Global AI in Supply Chain Optimization Market Outlook, By Edge Computing Devices (2023-2034) ($MN)
  • Table 14 Global AI in Supply Chain Optimization Market Outlook, By Services (2023-2034) ($MN)
  • Table 15 Global AI in Supply Chain Optimization Market Outlook, By Consulting & Strategy Services (2023-2034) ($MN)
  • Table 16 Global AI in Supply Chain Optimization Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 17 Global AI in Supply Chain Optimization Market Outlook, By Integration & Deployment Services (2023-2034) ($MN)
  • Table 18 Global AI in Supply Chain Optimization Market Outlook, By Training & Support Services (2023-2034) ($MN)
  • Table 19 Global AI in Supply Chain Optimization Market Outlook, By Technology (2023-2034) ($MN)
  • Table 20 Global AI in Supply Chain Optimization Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 21 Global AI in Supply Chain Optimization Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 22 Global AI in Supply Chain Optimization Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 23 Global AI in Supply Chain Optimization Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 24 Global AI in Supply Chain Optimization Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 25 Global AI in Supply Chain Optimization Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 26 Global AI in Supply Chain Optimization Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 27 Global AI in Supply Chain Optimization Market Outlook, By Application (2023-2034) ($MN)
  • Table 28 Global AI in Supply Chain Optimization Market Outlook, By Demand Forecasting & Planning (2023-2034) ($MN)
  • Table 29 Global AI in Supply Chain Optimization Market Outlook, By Risk Management & Resilience (2023-2034) ($MN)
  • Table 30 Global AI in Supply Chain Optimization Market Outlook, By Inventory Optimization (2023-2034) ($MN)
  • Table 31 Global AI in Supply Chain Optimization Market Outlook, By Supplier & Procurement Management (2023-2034) ($MN)
  • Table 32 Global AI in Supply Chain Optimization Market Outlook, By Warehouse Automation (2023-2034) ($MN)
  • Table 33 Global AI in Supply Chain Optimization Market Outlook, By Transportation & Logistics Optimization (2023-2034) ($MN)
  • Table 34 Global AI in Supply Chain Optimization Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 35 Global AI in Supply Chain Optimization Market Outlook, By End User (2023-2034) ($MN)
  • Table 36 Global AI in Supply Chain Optimization Market Outlook, By Retail & E-commerce (2023-2034) ($MN)
  • Table 37 Global AI in Supply Chain Optimization Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 38 Global AI in Supply Chain Optimization Market Outlook, By Food & Beverage (2023-2034) ($MN)
  • Table 39 Global AI in Supply Chain Optimization Market Outlook, By Healthcare & Pharmaceuticals (2023-2034) ($MN)
  • Table 40 Global AI in Supply Chain Optimization Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 41 Global AI in Supply Chain Optimization Market Outlook, By Logistics & Transportation (2023-2034) ($MN)
  • Table 42 Global AI in Supply Chain Optimization Market Outlook, By Other End Users (2023-2034) ($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.