查看购物车
  • Traditional Chinese
  • English
  • Japanese
封面
市场调查报告书
商品编码
1776776

2032 年供应链优化市场人工智慧预测：按产品、技术、应用、最终用户和地区进行的全球分析

AI in Supply Chain Optimization 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，全球供应链优化人工智慧市场规模预计在 2025 年达到 99 亿美元，到 2032 年将达到 1,050 亿美元，预测期内的复合年增长率为 40.1%。

供应链优化中的人工智慧 (AI) 涉及利用人工智慧来增强物流和营运。人工智慧演算法可以分析数据并简化库存管理、需求预测和运输路线等流程。它们透过预测中断并优化整个供应链的资源分配，来提高效率、降低成本并增强决策能力。

据麦肯锡称，在供应链中使用人工智慧的公司已经将物流成本降低了 12.7%，库存水准降低了 20.3%，节省了数十亿美元。

电子商务和全球贸易的成长

电商平台的激增和供应网路的全球化正在加速人工智慧供应链解决方案的采用。在消费者对即时交付和透明度的期望推动下，企业正在使用人工智慧来优化库存、路线和履约业务。在管理海量产品和多层级供应商生态系统的需求推动下，人工智慧提供了端到端的可视性和回应能力。在成本优化需求的推动下，人工智慧正迅速成为提高现代供应链效率和韧性的策略工具。

数据集成和互通性问题

儘管人工智慧的能力日益增强，但将其整合到现有的供应链基础设施中仍面临重大挑战。由于IT系统碎片化以及跨部门和合作伙伴的资料孤岛，实现无缝互通性往往举步维艰。由于缺乏即时资料处理能力的遗留旧有系统，许多公司仍未充分挖掘人工智慧的潜力。在这些限制因素的驱动下，统一的数位架构和强大的数据标准对于人工智慧主导的供应链充分发挥其潜力至关重要。

提高需求预测准确性

人工智慧能够改善需求预测，进而改变供应链的效率和回应能力。基于历史资料、天气趋势、市场情绪和社会经济指标训练的机器学习模型，使预测更加动态和精细。预测误差的减少，使企业能够最大限度地减少缺货、降低持有成本并提升服务水准。在人工智慧预测能力的指导下，企业还可以模拟各种「假设」供应链场景，从而提高准备度和敏捷性。

过度依赖人工智慧系统

供应链管理决策日益依赖人工智慧，这会带来与系统故障和意外资料异常相关的风险。在关键流程自动化的推动下，过度依赖人工智慧可能会降低人类的监督和解决问题的能力。人工智慧解读背景和应对「黑天鹅」事件的能力可能会被推到极限，导致组织在特殊情况下容易受到干扰。这些担忧促使企业必须在人工智慧自动化和人类专业知识之间取得平衡，以维护具有韧性的供应链。

COVID-19的影响：

新冠疫情暴露了全球供应链的严重漏洞，并加速了对人工智慧优化工具的投资。在不可预测的需求模式、运输延误和原材料短缺的刺激下，人工智慧帮助企业快速重构了采购和分销模式。随着向远端办公和云端协作工具的转变，人工智慧平台在疫情期间变得更加易于存取和可扩展。受这些经验教训的启发，企业现在正将人工智慧更深入地嵌入其供应链策略中，以确保长期的韧性。

机器学习领域预计将成为预测期内最大的领域

机器学习领域预计将在预测期内占据最大的市场占有率，因为它能够灵活应对各种供应链挑战。在监督学习和无监督学习模式不断进步的推动下，这项技术正越来越多地被纳入企业供应链软体。随着机器学习在采购、分销、物流和客户服务领域的广泛应用，它正被整体采用。由于其扩充性和整合潜力，预计该领域将在整个预测期内保持主导地位。

预计供应链规划部门在预测期内将以最高复合年增长率成长

预计供应链规划领域将在预测期内实现最高成长率，这得益于对即时可视性和主动决策日益增长的需求。消费者需求波动和地缘政治不确定性造成的干扰，使得人工智慧主导的规划工具变得至关重要。基于人工智慧的规划，由需求感知、生产调度和资源分配的整合所驱动，提供了一种统一且动态的方法。在竞争压力和以客户为中心的物流的推动下，规划功能正在发展成为人工智慧驱动的供应链转型的核心参与者。

比最大的地区

亚太地区预计将在预测期内占据最大的市场占有率，这得益于其作为全球製造和物流中心的地位。在中国、日本和印度等国家快速数位转型的推动下，人工智慧的应用正在工业和零售供应链中不断扩展。在政府的大力支持下，该地区的新兴企业越来越多地提供针对区域市场动态客製化的人工智慧供应链管理平台。在具有成本竞争力的劳动力、庞大的分销网络和不断发展的数位基础设施的推动下，亚太地区在人工智慧主导的供应链应用方面占据主导地位。

复合年增长率最高的地区：

在预测期内，北美预计将呈现最高的复合年增长率，这主要得益于零售、汽车和医疗保健行业的需求。即时供应计划和预测性维护是重点关注领域。随着全球动盪加剧，北美企业正转向人工智慧来降低风险并增强情境建模。在由人工智慧开发者、云端服务供应商和整合商组成的强大生态系统的支援下，该地区的企业在物流和采购领域应用人工智慧方面处于领先地位。在数据管治标准和创新津贴的指导下，该地区将继续引领供应链转型。

提供免费客製化：

此报告的订阅者可以从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他市场公司进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户兴趣对主要国家进行的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布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 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球供应链优化人工智慧市场（按产品提供）

  • 硬体
  • 软体
  • 服务

6. 全球供应链优化人工智慧市场（按技术）

  • 机器学习
  • 电脑视觉
  • 自然语言处理
  • 情境感知计算
  • 其他技术

7. 全球供应链优化人工智慧市场（按应用）

  • 供应链计划
  • 仓库管理
  • 车队管理
  • 虚拟助手
  • 风险管理
  • 库存管理
  • 规划与物流

8. 全球供应链优化人工智慧市场（按最终用户）

  • 製造业
  • 食品/饮料
  • 卫生保健
  • 车
  • 航太
  • 零售
  • 消费品
  • 其他最终用户

9. 全球供应链优化人工智慧市场（按地区）

  • 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
    • 其他欧洲国家
  • 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：主要发展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十一章 公司概况

  • Oracle Corporation
  • Google LLC（Alphabet Inc.）
  • Amazon Web Services, Inc.
  • NVIDIA Corporation
  • Kinaxis Inc.
  • Anaplan, Inc.
  • Coupa Software Inc.
  • Infor
  • O9 Solutions, Inc.
  • Llamasoft, Inc.
  • ToolsGroup
  • Manhattan Associates, Inc.
  • ClearMetal
  • Project44
  • FusionOps
  • C3.ai, Inc.
  • Blue Yonder Group, Inc.
  • IBM Corporation
  • Microsoft Corporation
  • SAP SE
Product Code: SMRC30107

According to Stratistics MRC, the Global AI in Supply Chain Optimization Market is accounted for $9.9 billion in 2025 and is expected to reach $105 billion by 2032 growing at a CAGR of 40.1% during the forecast period. AI in supply chain optimization involves using artificial intelligence to enhance logistics and operations. AI algorithms analyze data to streamline processes like inventory management, demand forecasting, and transportation routing. It improves efficiency, reduces costs, and enhances decision-making by predicting disruptions and optimizing resource allocation across the supply chain.

According to McKinsey, companies using AI in supply chains have already seen a 12.7% drop in logistics costs and a 20.3% reduction in inventory levels, resulting in billions in savings.

Market Dynamics:

Driver:

Growth in e-commerce and global trade

The proliferation of e-commerce platforms and the globalization of supply networks are accelerating the adoption of AI-powered supply chain solutions. Spurred by consumer expectations for real-time delivery and transparency, companies are leveraging AI to optimize inventory, routing, and fulfillment operations. Motivated by the need to manage vast product assortments and multi-tier supplier ecosystems, AI provides end-to-end visibility and responsiveness. By cost-optimization mandates, AI is fast becoming a strategic tool in enhancing the efficiency and resilience of modern supply chains.

Restraint:

Data integration and interoperability issues

Despite the growing capabilities of AI, integrating it into existing supply chain infrastructures poses significant challenges. Driven by fragmented IT systems and siloed data across departments and partners, seamless interoperability is often difficult to achieve. Backed by legacy systems that lack real-time data handling capabilities, the potential of AI remains underutilized in many enterprises. Fueled by these limitations, a unified digital architecture and strong data standards are critical for AI-driven supply chains to realize their full potential.

Opportunity:

Enhanced demand forecasting accuracy

AI's ability to improve demand forecasting represents a transformative opportunity for supply chain efficiency and responsiveness. Spurred by machine learning models trained on historical data, weather trends, market sentiment, and socio-economic indicators, forecasts are now more dynamic and granular. Fueled by reduced forecasting errors, companies benefit from minimized stockouts, lower holding costs, and higher service levels. Guided by AI's predictive capabilities, enterprises can also model various "what-if" supply chain scenarios, enhancing their preparedness and agility.

Threat:

Overreliance on AI systems

The increasing dependence on AI for decision-making in supply chain management introduces risks related to system failures and unforeseen data anomalies. Driven by automation of critical processes, overreliance on AI can diminish human oversight and problem-solving skills. Spurred by limitations in AI's ability to interpret context or respond to black-swan events, organizations may face disruptions during exceptional circumstances. Guided by these concerns, companies must strike a balance between AI-driven automation and human expertise to maintain resilient supply chains.

Covid-19 Impact:

The COVID-19 pandemic exposed severe vulnerabilities in global supply chains, prompting accelerated investment in AI-enabled optimization tools. Spurred by unpredictable demand patterns, shipping delays, and raw material shortages, AI helped companies reconfigure sourcing and distribution models on the fly. With the shift to remote work and cloud collaboration tools, AI platforms became more accessible and scalable during the pandemic. Motivated by lessons learned, enterprises are now embedding AI more deeply into their supply chain strategies for long-term resilience.

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

The machine learning segment is expected to account for the largest market share during the forecast period, owing to its versatility in addressing various supply chain challenges. Spurred by ongoing advancements in supervised and unsupervised learning models, this technology is increasingly embedded into enterprise supply chain software. With widespread applications across sourcing, distribution, logistics, and customer service, machine learning is being deployed across the supply chain spectrum. Guided by its scalability and integration potential, the segment is set to retain its dominant position throughout the forecast horizon.

The supply chain planning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the supply chain planning segment is predicted to witness the highest growth rate, impelled by the growing demand for real-time visibility and proactive decision-making. Spurred by disruptions from fluctuating consumer demand and geopolitical uncertainties, AI-driven planning tools are becoming indispensable. Driven by the integration of demand sensing, production scheduling, and resource allocation, AI-based planning offers a unified and dynamic approach. Motivated by competitive pressures and customer-centric logistics, the planning function is evolving into a core driver of AI-enabled supply chain transformation.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, driven by its role as a global manufacturing and logistics hub. Spurred by rapid digital transformation in countries like China, Japan, and India, AI implementation is scaling across industrial and retail supply chains. Backed by favorable government support, regional tech startups are increasingly offering AI-powered SCM platforms tailored to local market dynamics. Guided by its cost-competitive labor, vast distribution networks, and growing digital infrastructure, Asia Pacific dominates AI-driven supply chain adoption.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, spurred by demand from retail, automotive, and healthcare sectors. Real-time supply planning and predictive maintenance are key focus areas. Due to rising disruptions from global instability, North American firms are turning to AI for enhanced risk mitigation and scenario modelling. Backed by a strong ecosystem of AI developers, cloud service providers, and integrators, regional firms are at the forefront of AI deployment in logistics and procurement. Guided by data governance standards and innovation grants, the region continues to lead in supply chain transformation initiatives.

Key players in the market

Some of the key players in AI in Supply Chain Optimization Market include Oracle Corporation, Google LLC (Alphabet Inc.), Amazon Web Services, Inc., NVIDIA Corporation, Kinaxis Inc., Anaplan, Inc., Coupa Software Inc., Infor, O9 Solutions, Inc., Llamasoft, Inc., ToolsGroup, Manhattan Associates, Inc., ClearMetal, Project44, FusionOps, C3.ai, Inc., Blue Yonder Group, Inc., IBM Corporation, Microsoft Corporation, and SAP SE.

Key Developments:

In May 2025, Google LLC launched an AI tool on Google Cloud for real-time supply chain visibility. It optimizes logistics by providing actionable insights, reducing delays, and enhancing efficiency across global supply chain networks.

In April 2025, Amazon Web Services unveiled AWS Supply Chain AI for automated warehouse management. It optimizes delivery routes, reducing costs and improving efficiency with real-time data analytics for seamless logistics operations.

In February 2025, ToolsGroup introduced an AI-driven inventory optimization platform. It enables real-time stock management, reducing excess inventory and costs while ensuring product availability through predictive analytics.

Offerings Covered:

  • Hardware
  • Software
  • Services

Technologies Covered:

  • Machine Learning
  • Computer Vision
  • Natural Language Processing
  • Context-Aware Computing
  • Other Technologies

Applications Covered:

  • Supply Chain Planning
  • Warehouse Management
  • Fleet Management
  • Virtual Assistant
  • Risk Management
  • Inventory Management
  • Planning & Logistics

End Users Covered:

  • Manufacturing
  • Food & Beverages
  • Healthcare
  • Automotive
  • Aerospace
  • Retail
  • Consumer-Packaged Goods
  • 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 Optimization Market, By Offering

  • 5.1 Introduction
  • 5.2 Hardware
  • 5.3 Software
  • 5.4 Services

6 Global AI in Supply Chain Optimization Market, By Technology

  • 6.1 Introduction
  • 6.2 Machine Learning
  • 6.3 Computer Vision
  • 6.4 Natural Language Processing
  • 6.5 Context-Aware Computing
  • 6.6 Other Technologies

7 Global AI in Supply Chain Optimization Market, By Application

  • 7.1 Introduction
  • 7.2 Supply Chain Planning
  • 7.3 Warehouse Management
  • 7.4 Fleet Management
  • 7.5 Virtual Assistant
  • 7.6 Risk Management
  • 7.7 Inventory Management
  • 7.8 Planning & Logistics

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

  • 8.1 Introduction
  • 8.2 Manufacturing
  • 8.3 Food & Beverages
  • 8.4 Healthcare
  • 8.5 Automotive
  • 8.6 Aerospace
  • 8.7 Retail
  • 8.8 Consumer-Packaged Goods
  • 8.9 Other End Users

9 Global AI in Supply Chain Optimization 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 Oracle Corporation
  • 11.2 Google LLC (Alphabet Inc.)
  • 11.3 Amazon Web Services, Inc.
  • 11.4 NVIDIA Corporation
  • 11.5 Kinaxis Inc.
  • 11.6 Anaplan, Inc.
  • 11.7 Coupa Software Inc.
  • 11.8 Infor
  • 11.9 O9 Solutions, Inc.
  • 11.10 Llamasoft, Inc.
  • 11.11 ToolsGroup
  • 11.12 Manhattan Associates, Inc.
  • 11.13 ClearMetal
  • 11.14 Project44
  • 11.15 FusionOps
  • 11.16 C3.ai, Inc.
  • 11.17 Blue Yonder Group, Inc.
  • 11.18 IBM Corporation
  • 11.19 Microsoft Corporation
  • 11.20 SAP SE

List of Tables

  • Table 1 Global AI in Supply Chain Optimization Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Supply Chain Optimization Market Outlook, By Offering (2024-2032)
  • Table 3 Global AI in Supply Chain Optimization Market Outlook, By Hardware (2024-2032)
  • Table 4 Global AI in Supply Chain Optimization Market Outlook, By Software (2024-2032)
  • Table 5 Global AI in Supply Chain Optimization Market Outlook, By Services (2024-2032)
  • Table 6 Global AI in Supply Chain Optimization Market Outlook, By Technology (2024-2032)
  • Table 7 Global AI in Supply Chain Optimization Market Outlook, By Machine Learning (2024-2032)
  • Table 8 Global AI in Supply Chain Optimization Market Outlook, By Computer Vision (2024-2032)
  • Table 9 Global AI in Supply Chain Optimization Market Outlook, By Natural Language Processing (2024-2032)
  • Table 10 Global AI in Supply Chain Optimization Market Outlook, By Context-Aware Computing (2024-2032)
  • Table 11 Global AI in Supply Chain Optimization Market Outlook, By Other Technologies (2024-2032)
  • Table 12 Global AI in Supply Chain Optimization Market Outlook, By Application (2024-2032)
  • Table 13 Global AI in Supply Chain Optimization Market Outlook, By Supply Chain Planning (2024-2032)
  • Table 14 Global AI in Supply Chain Optimization Market Outlook, By Warehouse Management (2024-2032)
  • Table 15 Global AI in Supply Chain Optimization Market Outlook, By Fleet Management (2024-2032)
  • Table 16 Global AI in Supply Chain Optimization Market Outlook, By Virtual Assistant (2024-2032)
  • Table 17 Global AI in Supply Chain Optimization Market Outlook, By Risk Management (2024-2032)
  • Table 18 Global AI in Supply Chain Optimization Market Outlook, By Inventory Management (2024-2032)
  • Table 19 Global AI in Supply Chain Optimization Market Outlook, By Planning & Logistics (2024-2032)
  • Table 20 Global AI in Supply Chain Optimization Market Outlook, By End User (2024-2032)
  • Table 21 Global AI in Supply Chain Optimization Market Outlook, By Manufacturing (2024-2032)
  • Table 22 Global AI in Supply Chain Optimization Market Outlook, By Food & Beverages (2024-2032)
  • Table 23 Global AI in Supply Chain Optimization Market Outlook, By Healthcare (2024-2032)
  • Table 24 Global AI in Supply Chain Optimization Market Outlook, By Automotive (2024-2032)
  • Table 25 Global AI in Supply Chain Optimization Market Outlook, By Aerospace (2024-2032)
  • Table 26 Global AI in Supply Chain Optimization Market Outlook, By Retail (2024-2032)
  • Table 27 Global AI in Supply Chain Optimization Market Outlook, By Consumer-Packaged Goods (2024-2032)
  • Table 28 Global AI in Supply Chain Optimization Market Outlook, By Other End Users (2024-2032)

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