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市场调查报告书
商品编码
1817991

2032 年基于人工智慧的物料输送系统市场预测：按设备类型、功能、最终用户和地区进行的全球分析

AI-based Material Handling System Market Forecasts to 2032 - Global Analysis By Equipment Type, Function, 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，全球基于人工智慧的物料输送系统市场预计在 2025 年达到 773.1 亿美元，到 2032 年将达到 1,646 亿美元，预测期内的复合年增长率为 11.4%。

基于人工智慧的物料输送系统将先进的人工智慧与物料输送流程相结合，以提高生产力、准确性和工作场所安全性。这些系统利用机器学习、机器人技术和电脑视觉，实现仓库和製造工厂中产品移动、分类和储存等功能的自动化。人工智慧透过分析即时数据来优化运输路线、预测设备维护并最大限度地减少人为错误，从而降低成本并加快工作流程。此外，这些系统能够快速适应需求变化，提供营运灵活性和扩充性。人工智慧在物料输送中的应用正在彻底改变工业物流，使其能够建立更智慧、更可靠、更有效率的供应链，以应对现代製造和分销挑战。

根据物料输送产业 (MHI) 2023 年度产业报告，60% 的供应链专业人士表示他们将在未来五年内在其营运中采用人工智慧技术，而目前这一比例仅为 12%。

提高仓储和製造的自动化程度

製造工厂和仓库营运中自动化技术的日益普及，推动了基于人工智慧的物料输送系统市场的成长。企业正在采用人工智慧机器人、自动导引车和智慧分类技术，以提高工作流程效率、减少对劳动力的依赖并减少错误。自动化可以加快物料运输速度、实现即时库存追踪并优化营运绩效。满足短交货期、控制营运成本和提高生产力的压力日益增大，这推动了对智慧搬运解决方案的需求。随着产业的扩张和供应链优化的重要性日益凸显，基于人工智慧的物料输送系统的应用正在加速，使其成为现代工业运作的重要组成部分。

初期投资成本高

阻碍基于人工智慧的物料输送系统市场成长的一大挑战是其所需的巨额初始投资。部署人工智慧机器人、自动导引车和智慧分类技术需要大量的资本投入，这使得中小企业 (SME) 难以采用这些技术。成本包括购置先进机械设备、整合人工智慧软体以及培训员工进行有效的操作和维护。由于回报週期可能较长，企业不愿投资此类系统。因此，高昂的初始投资成为阻碍，限制了基于人工智慧的物料输送方案的广泛应用，尤其是在资金紧张的地区和小型企业。

人工智慧和机器人技术的进步

人工智慧、机器人技术和机器学习领域的持续技术创新，为基于人工智慧的物料输送系统市场创造了巨大的成长机会。先进的人工智慧解决方案能够在仓库和製造环境中实现预测分析、智慧路由和自主营运决策。机器人和自动化技术可以减少人工劳动，同时提高准确性和处理速度。协作机器人、视觉引导车辆和智慧输送机系统等技术使企业能够有效率地扩展营运规模，并满足不断变化的供应链需求。这些技术发展可以提高生产力、降低营运成本并最大限度地减少错误。

快速的技术创新导致技术过时

人工智慧、机器人和自动化领域的技术创新日新月异，可能使现有技术过时，威胁到基于人工智慧的物料输送系统市场。目前已投资人工智慧解决方案的公司可能需要频繁升级才能保持竞争力，这会导致额外成本、营运停机和员工再培训。更新、更先进的系统取代现有系统的风险，可能会使公司不愿意进行长期投资。这种快速发展的技术格局带来了不确定性，阻碍了企业全面采用人工智慧主导的物料输送解决方案。

COVID-19的影响：

新冠疫情（COVID-19）大流行推动了仓库和生产设施中自动化和非接触式操作的采用，从而影响了基于人工智慧的物料输送系统市场。社交隔离通讯协定和劳动力短缺促使企业部署人工智慧机器人、自动导引车和智慧分类技术，以维持营运并减少人机互动。供应链中断凸显了即时库存管理和物料输送优化的需求。儘管经济不确定性导致一些公司暂时减少了投资，但疫情凸显了人工智慧主导的自动化在提升营运韧性和连续性方面的价值。

预测期内，自主移动机器人 (AMR) 细分市场预计将成为最大的细分市场

自主移动机器人 (AMR) 领域预计将在预测期内占据最大的市场份额，这得益于其在仓库和生产设施中执行物料输送时所展现出的多功能性、高效性和适应性。与传统系统不同，AMR 可以自主导航、侦测和避开障碍物，并与人类安全协同运行，从而提高营运效率。 AMR 与仓库管理系统的无缝整合、对复杂任务的支援以及即时决策能力，使其成为寻求可扩展智慧自动化的企业的理想选择。对更快交付、更低人事费用和非接触式操作日益增长的需求，进一步强化了 AMR 的优势，使其成为现代人工智慧主导物料输送的关键解决方案。

拾取和放置部分预计在预测期内实现最高复合年增长率

由于仓库和生产设施对自动化的需求日益增长，预计在预测期内，拾取和放置细分市场将呈现最高成长率。该细分市场利用人工智慧机器人和系统来精准地选择、处理和定位产品，以最大限度地减少人为错误和手工劳动。电子商务的扩张、对更快配送的需求以及复杂的订单履行要求，正在推动自动化拾取和放置解决方案的采用。人工智慧与电脑视觉的整合提高了准确性、速度和营运效率。持续的技术创新，加上效率和生产力优势，使自动化拾取和放置成为物料输送行业成长最快的应用。

占比最大的地区：

在预测期内，北美预计将占据最大的市场份额，因为它拥有强大的工业基础、早期采用自动化技术以及关键技术提供者的存在。该地区的製造和物流行业越来越多地采用人工智慧机器人、自动驾驶汽车和智慧仓库解决方案，以提高生产力、降低人事费用并优化供应链营运。强有力的政府倡议、持续的技术进步以及对数位和智慧製造的高额投资正在进一步推动市场扩张。北美工业对营运效率、安全和数位转型的关注正在加速基于人工智慧的物料输送系统的采用，使该地区成为全球市场的关键贡献者。

复合年增长率最高的地区：

在预测期内，由于工业的快速成长、电子商务的兴起以及自动化技术的日益普及，亚太地区预计将呈现最高的复合年增长率。包括中国、日本和印度在内的主要国家正在大力投资人工智慧机器人、智慧仓库和先进的製造解决方案，以提高生产力并减少对人工的依赖。物流和製造业的成长，加上政府支持数位转型和工业4.0应用的倡议，正在推动市场扩张。对更快的订单履行、即时库存管理和灵活的物料输送方案的需求日益增长，正在加速该地区人工智慧主导系统的采用。

提供免费客製化：

此报告的订阅者可以使用以下免费自订选项之一：

  • 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
  • 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
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球以人工智慧为基础的物料输送系统市场（以设备类型）

  • 自主移动机器人（AMR）
  • 自动导引运输车（AGV）
  • 机械臂
  • 无人机
  • 自动化仓库系统（AS/RS）
  • 基于视觉的检测单元

6. 全球以人工智慧为基础的物料输送系统市场（按功能）

  • 运输
  • 贮存
  • 拾取和放置
  • 包装
  • 检验和品管

7. 全球以人工智慧为基础的物料输送系统市场（按最终用户）

  • 车
  • 电子商务履约
  • 饮食
  • 製药
  • 航太
  • 第三方物流（3PL）
  • 电子设备製造业

8. 全球以人工智慧为基础的物料输送系统市场（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

第九章：主要进展

  • 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

第十章：企业概况

  • Daifuku Co., Ltd.
  • KION Group AG
  • Toyota Industries Corporation
  • Honeywell International
  • SSI SCHAEFER
  • Amazon Robotics
  • Walmart
  • UPS
  • FedEx
  • Dematic
  • Vanderlande Industries
  • MHS Global
  • GreyOrange
  • Swisslog
  • Addverb Technologies
Product Code: SMRC30954

According to Stratistics MRC, the Global AI-based Material Handling System Market is accounted for $77.31 billion in 2025 and is expected to reach $164.60 billion by 2032 growing at a CAGR of 11.4% during the forecast period. AI-driven Material Handling Systems combine advanced artificial intelligence with conventional handling processes to improve productivity, precision, and operational safety. Leveraging machine learning, robotics, and computer vision, these systems automate functions like moving, sorting, and storing products in warehouses or manufacturing plants. AI analyzes live data to optimize transport routes, forecast equipment maintenance, and minimizes human mistakes, resulting in cost reductions and accelerated workflows. Additionally, these systems can quickly adjust to shifts in demand, offering operational flexibility and scalability. The adoption of AI in material handling is revolutionizing industrial logistics, enabling smarter, more dependable, and highly efficient supply chains that meet modern manufacturing and distribution challenges.

According to the Material Handling Industry (MHI), in the 2023 MHI Annual Industry Report, 60% of supply chain professionals said they expect to adopt AI technologies in their operations within the next five years, up from just 12% currently.

Market Dynamics:

Driver:

Increasing automation in warehouses and manufacturing

The rising implementation of automation in manufacturing plants and warehouse operations is fueling the growth of the AI-based Material Handling System market. Businesses are adopting AI-enabled robots, automated guided vehicles, and smart sorting technologies to improve workflow efficiency, lower labor dependency, and reduce errors. Automation enables faster material transport, real-time inventory tracking, and optimized operational performance. The increasing pressure to meet quick delivery timelines, control operational costs, and boost productivity drives the demand for intelligent handling solutions. As industries expand and supply chain optimization becomes critical, AI-based automated material handling systems are witnessing accelerated adoption, becoming an essential element of modern industrial operations.

Restraint:

High initial investment costs

A key challenge hindering the growth of the AI-based Material Handling System market is the substantial upfront investment required. Deploying AI-powered robots, automated guided vehicles, and smart sorting technologies involves considerable capital spending, often making it difficult for small and medium enterprises to adopt. Costs include acquiring sophisticated machinery, integrating AI software, and training staff for effective operation and maintenance. The potentially extended period before realizing returns can deter companies from investing in these systems. As a result, the high initial expenditure acts as a barrier, limiting the broader implementation of AI-based material handling solutions, especially in financially constrained regions or smaller-scale operations.

Opportunity:

Technological advancements in AI and robotics

Ongoing innovations in artificial intelligence, robotics, and machine learning are opening significant growth opportunities for the AI-based Material Handling System market. Advanced AI solutions enable predictive analytics, intelligent routing, and autonomous operational decisions in warehouses and manufacturing environments. Robotics and automation reduce manual labor while enhancing accuracy and processing speed. Technologies like collaborative robots, vision-guided vehicles, and smart conveyor systems allow organizations to efficiently scale operations and respond to changing supply chain requirements. These technological developments improve productivity, reduce operational costs, and minimize errors.

Threat:

Rapid technological changes leading to obsolescence

The rapid pace of innovation in AI, robotics, and automation threatens the AI-based Material Handling System market by potentially rendering current technologies outdated. Companies that invest in present-day AI solutions may need frequent upgrades to stay competitive, incurring additional costs, operational downtime, and staff retraining. The risk of newer, more advanced systems superseding existing ones can make businesses reluctant to commit to long-term investments. This fast-evolving technological landscape introduces uncertainty, discouraging organizations from fully adopting AI-driven material handling solutions.

Covid-19 Impact:

The COVID-19 pandemic influenced the AI-based Material Handling System market by boosting the implementation of automation and contactless operations in warehouses and production facilities. Social distancing protocols and workforce shortages prompted companies to deploy AI-powered robots, automated guided vehicles, and smart sorting technologies to sustain operations and reduce human interaction. Disruptions in supply chains emphasized the necessity of real-time inventory management and optimized material handling. Although some businesses temporarily reduced investments due to economic uncertainty, the pandemic underscored the value of AI-driven automation for operational resilience and continuity.

The autonomous mobile robots (AMRs) segment is expected to be the largest during the forecast period

The autonomous mobile robots (AMRs) segment is expected to account for the largest market share during the forecast period because of their versatility, efficiency, and adaptability in handling materials across warehouses and production facilities. Unlike conventional systems, AMRs can navigate autonomously, detect and avoid obstacles, and operate safely alongside humans, boosting operational efficiency. Their seamless integration with warehouse management systems, support for complex tasks, and real-time decision-making capabilities make them highly desirable for companies aiming for scalable and intelligent automation. The rising need for faster deliveries, labor cost reduction, and contactless operations have reinforced AMRs' dominance, positioning them as a key solution in modern AI-driven material handling operations.

The picking & placing segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the picking & placing segment is predicted to witness the highest growth rate, driven by increasing automation needs in warehouses and production facilities. This segment utilizes AI-enabled robots and systems to accurately select, handle, and position products, minimizing human errors and manual labor. The expansion of e-commerce, demand for faster deliveries, and complex order fulfillment requirements are fueling adoption of automated picking and placing solutions. Integration of AI and computer vision enhances accuracy, speed, and operational efficiency. Continuous technological innovation, combined with efficiency and productivity benefits, positions automated picking and placing as the fastest-growing application within the material handling industry.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, owing to its robust industrial base, early adoption of automation, and the presence of major technology providers. The region's manufacturing and logistics sectors are increasingly implementing AI-driven robots, automated guided vehicles, and smart warehouse solutions to improve productivity, cut labor costs, and optimize supply chain operations. Strong government initiatives, continuous technological advancements, and high investment in digital and smart manufacturing further propel market expansion. North American industries focus on operational efficiency, safety, and digital transformation, which accelerate the adoption of AI-based material handling systems, positioning the region as a leading contributor to the global market.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by rapid industrial growth, the rise of e-commerce, and increasing use of automation technologies. Key countries, including China, Japan, and India, are investing significantly in AI-powered robots, smart warehouses, and advanced manufacturing solutions to improve productivity and reduce reliance on manual labor. The growth of logistics and manufacturing sectors, coupled with government initiatives supporting digital transformation and Industry 4.0 adoption, fuels market expansion. Increasing requirements for faster order fulfillment, real-time inventory control, and flexible material handling solutions are accelerating the adoption of AI-driven systems in the region.

Key players in the market

Some of the key players in AI-based Material Handling System Market include Daifuku Co., Ltd., KION Group AG, Toyota Industries Corporation, Honeywell International, SSI SCHAEFER, Amazon Robotics, Walmart, UPS, FedEx, Dematic, Vanderlande Industries, MHS Global, GreyOrange, Swisslog and Addverb Technologies.

Key Developments:

In May 2025, FedEx and Amazon strike large-package delivery deal. The agreement marks a rekindling of the two parties' relationship nearly six years after FedEx announced it wouldn't renew its Ground and Express domestic shipping contracts with Amazon. At the time, FedEx said it wanted to focus on the broader e-commerce market.

In October 2024, KION Group has entered into a strategic partnership with Eurofork S.p.A., a leading manufacturer of pallet shuttle systems. The two companies have signed a cooperation agreement at KION GROUP AG. Under the agreement, Eurofork's E4CUBE(R) solution will be distributed through the sales and service networks of the KION brands in the Industrial Trucks & Services segment in the EMEA region with immediate effect.

In October 2024, Toyota Motor Corporation and Nippon Telegraph and Telephone Corporation have agreed to a joint initiative in the field of mobility and AI/telecommunications with the aim of realizing a society with zero traffic accidents. Through their previous collaborations, the two companies have confirmed that they share common values, such as contributing to society through technological and industrial development, a people-centered approach, and global contributions that start in Japan.

Equipment Types Covered:

  • Autonomous Mobile Robots (AMRs)
  • Automated Guided Vehicles (AGVs)
  • Robotic Arms
  • Drones
  • Automated Storage and Retrieval Systems (AS/RS)
  • Vision-Based Inspection Units

Functions Covered:

  • Transporting
  • Storing
  • Picking & Placing
  • Packaging
  • Inspection & Quality Control

End Users Covered:

  • Automotive
  • E-commerce Fulfillment
  • Food & Beverage
  • Pharmaceuticals
  • Aerospace
  • Third-Party Logistics (3PL)
  • Electronics Manufacturing

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 End User Analysis
  • 3.7 Emerging Markets
  • 3.8 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-based Material Handling System Market, By Equipment Type

  • 5.1 Introduction
  • 5.2 Autonomous Mobile Robots (AMRs)
  • 5.3 Automated Guided Vehicles (AGVs)
  • 5.4 Robotic Arms
  • 5.5 Drones
  • 5.6 Automated Storage and Retrieval Systems (AS/RS)
  • 5.7 Vision-Based Inspection Units

6 Global AI-based Material Handling System Market, By Function

  • 6.1 Introduction
  • 6.2 Transporting
  • 6.3 Storing
  • 6.4 Picking & Placing
  • 6.5 Packaging
  • 6.6 Inspection & Quality Control

7 Global AI-based Material Handling System Market, By End User

  • 7.1 Introduction
  • 7.2 Automotive
  • 7.3 E-commerce Fulfillment
  • 7.4 Food & Beverage
  • 7.5 Pharmaceuticals
  • 7.6 Aerospace
  • 7.7 Third-Party Logistics (3PL)
  • 7.8 Electronics Manufacturing

8 Global AI-based Material Handling System Market, By Geography

  • 8.1 Introduction
  • 8.2 North America
    • 8.2.1 US
    • 8.2.2 Canada
    • 8.2.3 Mexico
  • 8.3 Europe
    • 8.3.1 Germany
    • 8.3.2 UK
    • 8.3.3 Italy
    • 8.3.4 France
    • 8.3.5 Spain
    • 8.3.6 Rest of Europe
  • 8.4 Asia Pacific
    • 8.4.1 Japan
    • 8.4.2 China
    • 8.4.3 India
    • 8.4.4 Australia
    • 8.4.5 New Zealand
    • 8.4.6 South Korea
    • 8.4.7 Rest of Asia Pacific
  • 8.5 South America
    • 8.5.1 Argentina
    • 8.5.2 Brazil
    • 8.5.3 Chile
    • 8.5.4 Rest of South America
  • 8.6 Middle East & Africa
    • 8.6.1 Saudi Arabia
    • 8.6.2 UAE
    • 8.6.3 Qatar
    • 8.6.4 South Africa
    • 8.6.5 Rest of Middle East & Africa

9 Key Developments

  • 9.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 9.2 Acquisitions & Mergers
  • 9.3 New Product Launch
  • 9.4 Expansions
  • 9.5 Other Key Strategies

10 Company Profiling

  • 10.1 Daifuku Co., Ltd.
  • 10.2 KION Group AG
  • 10.3 Toyota Industries Corporation
  • 10.4 Honeywell International
  • 10.5 SSI SCHAEFER
  • 10.6 Amazon Robotics
  • 10.7 Walmart
  • 10.8 UPS
  • 10.9 FedEx
  • 10.10 Dematic
  • 10.11 Vanderlande Industries
  • 10.12 MHS Global
  • 10.13 GreyOrange
  • 10.14 Swisslog
  • 10.15 Addverb Technologies

List of Tables

  • Table 1 Global AI-based Material Handling System Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-based Material Handling System Market Outlook, By Equipment Type (2024-2032) ($MN)
  • Table 3 Global AI-based Material Handling System Market Outlook, By Autonomous Mobile Robots (AMRs) (2024-2032) ($MN)
  • Table 4 Global AI-based Material Handling System Market Outlook, By Automated Guided Vehicles (AGVs) (2024-2032) ($MN)
  • Table 5 Global AI-based Material Handling System Market Outlook, By Robotic Arms (2024-2032) ($MN)
  • Table 6 Global AI-based Material Handling System Market Outlook, By Drones (2024-2032) ($MN)
  • Table 7 Global AI-based Material Handling System Market Outlook, By Automated Storage and Retrieval Systems (AS/RS) (2024-2032) ($MN)
  • Table 8 Global AI-based Material Handling System Market Outlook, By Vision-Based Inspection Units (2024-2032) ($MN)
  • Table 9 Global AI-based Material Handling System Market Outlook, By Function (2024-2032) ($MN)
  • Table 10 Global AI-based Material Handling System Market Outlook, By Transporting (2024-2032) ($MN)
  • Table 11 Global AI-based Material Handling System Market Outlook, By Storing (2024-2032) ($MN)
  • Table 12 Global AI-based Material Handling System Market Outlook, By Picking & Placing (2024-2032) ($MN)
  • Table 13 Global AI-based Material Handling System Market Outlook, By Packaging (2024-2032) ($MN)
  • Table 14 Global AI-based Material Handling System Market Outlook, By Inspection & Quality Control (2024-2032) ($MN)
  • Table 15 Global AI-based Material Handling System Market Outlook, By End User (2024-2032) ($MN)
  • Table 16 Global AI-based Material Handling System Market Outlook, By Automotive (2024-2032) ($MN)
  • Table 17 Global AI-based Material Handling System Market Outlook, By E-commerce Fulfillment (2024-2032) ($MN)
  • Table 18 Global AI-based Material Handling System Market Outlook, By Food & Beverage (2024-2032) ($MN)
  • Table 19 Global AI-based Material Handling System Market Outlook, By Pharmaceuticals (2024-2032) ($MN)
  • Table 20 Global AI-based Material Handling System Market Outlook, By Aerospace (2024-2032) ($MN)
  • Table 21 Global AI-based Material Handling System Market Outlook, By Third-Party Logistics (3PL) (2024-2032) ($MN)
  • Table 22 Global AI-based Material Handling System Market Outlook, By Electronics Manufacturing (2024-2032) ($MN)

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