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1817991

2032 年基於人工智慧的物料輸送系統市場預測：按設備類型、功能、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球基於人工智慧的物料輸送系統市場預計在 2025 年達到 773.1 億美元，到 2032 年將達到 1,646 億美元，預測期內的複合年成長率為 11.4%。

基於人工智慧的物料輸送系統將先進的人工智慧與物料輸送流程相結合，以提高生產力、準確性和工作場所安全性。這些系統利用機器學習、機器人技術和電腦視覺，實現倉庫和製造工廠中產品移動、分類和儲存等功能的自動化。人工智慧透過分析即時數據來最佳化運輸路線、預測設備維護並最大限度地減少人為錯誤，從而降低成本並加快工作流程。此外，這些系統能夠快速適應需求變化，提供營運靈活性和擴充性。人工智慧在物料輸送中的應用正在徹底改變工業物流，使其能夠建立更智慧、更可靠、更有效率的供應鏈，以應對現代製造和分銷挑戰。

根據物料輸送產業 (MHI) 2023 年度產業報告，60% 的供應鏈專業人士表示他們將在未來五年內在其營運中採用人工智慧技術，而目前這一比例僅為 12%。

提高倉儲和製造的自動化程度

製造工廠和倉庫營運中自動化技術的日益普及，推動了基於人工智慧的物料輸送系統市場的成長。企業正在採用人工智慧機器人、自動導引車和智慧分類技術，以提高工作流程效率、減少對勞動力的依賴並減少錯誤。自動化可以加快物料運輸速度、實現即時庫存追蹤並最佳化營運績效。滿足短交貨期、控制營運成本和提高生產力的壓力日益增大，這推動了對智慧搬運解決方案的需求。隨著產業的擴張和供應鏈最佳化的重要性日益凸顯，基於人工智慧的物料輸送系統的應用正在加速，使其成為現代工業運作的重要組成部分。

初期投資成本高

阻礙基於人工智慧的物料輸送系統市場成長的一大挑戰是其所需的巨額初始投資。部署人工智慧機器人、自動導引車和智慧分類技術需要大量的資本投入，這使得中小企業 (SME) 難以採用這些技術。成本包括購置先進機械設備、整合人工智慧軟體以及培訓員工進行有效的操作和維護。由於回報週期可能較長，企業不願投資此類系統。因此，高昂的初始投資成為阻礙，限制了基於人工智慧的物料輸送方案的廣泛應用，尤其是在資金緊張的地區和小型企業。

人工智慧和機器人技術的進步

人工智慧、機器人技術和機器學習領域的持續技術創新，為基於人工智慧的物料輸送系統市場創造了巨大的成長機會。先進的人工智慧解決方案能夠在倉庫和製造環境中實現預測分析、智慧路由和自主營運決策。機器人和自動化技術可以減少人工勞動，同時提高準確性和處理速度。協作機器人、視覺引導車輛和智慧輸送機系統等技術使企業能夠有效率地擴展營運規模，並滿足不斷變化的供應鏈需求。這些技術發展可以提高生產力、降低營運成本並最大限度地減少錯誤。

快速的技術創新導致技術過時

人工智慧、機器人和自動化領域的技術創新日新月異，可能使現有技術過時，威脅到基於人工智慧的物料輸送系統市場。目前已投資人工智慧解決方案的公司可能需要頻繁升級才能保持競爭力，這會導致額外成本、營運停機和員工再培訓。更新、更先進的系統取代現有系統的風險，可能會使公司不願意進行長期投資。這種快速發展的技術格局帶來了不確定性，阻礙了企業全面採用人工智慧主導的物料輸送解決方案。

COVID-19的影響：

新冠疫情（COVID-19）大流行推動了倉庫和生產設施中自動化和非接觸式操作的採用，從而影響了基於人工智慧的物料輸送系統市場。社交隔離通訊協定和勞動力短缺促使企業部署人工智慧機器人、自動導引車和智慧分類技術，以維持營運並減少人機互動。供應鏈中斷凸顯了即時庫存管理和物料輸送最佳化的需求。儘管經濟不確定性導致一些公司暫時減少了投資，但疫情凸顯了人工智慧主導的自動化在提升營運韌性和連續性方面的價值。

預測期內，自主移動機器人 (AMR) 細分市場預計將成為最大的細分市場

自主移動機器人 (AMR) 領域預計將在預測期內佔據最大的市場佔有率，這得益於其在倉庫和生產設施中執行物料輸送時所展現出的多功能性、高效性和適應性。與傳統系統不同，AMR 可以自主導航、偵測和避開障礙物，並與人類安全協同運行，從而提高營運效率。 AMR 與倉庫管理系統的無縫整合、對複雜任務的支援以及即時決策能力，使其成為尋求可擴展智慧自動化的企業的理想選擇。對更快交付、更低人事費用和非接觸式操作日益成長的需求，進一步強化了 AMR 的優勢，使其成為現代人工智慧主導物料輸送的關鍵解決方案。

拾取和放置部分預計在預測期內實現最高複合年成長率

由於倉庫和生產設施對自動化的需求日益成長，預計在預測期內，拾取和放置細分市場將呈現最高成長率。該細分市場利用人工智慧機器人和系統來精準地選擇、處理和定位產品，以最大限度地減少人為錯誤和手工勞動。電子商務的擴張、對更快配送的需求以及複雜的訂單履行要求，正在推動自動化拾取和放置解決方案的採用。人工智慧與電腦視覺的整合提高了準確性、速度和營運效率。持續的技術創新，加上效率和生產力優勢，使自動化拾取和放置成為物料輸送行業成長最快的應用。

佔比最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，因為它擁有強大的工業基礎、早期採用自動化技術以及關鍵技術提供者的存在。該地區的製造和物流行業擴大採用人工智慧機器人、自動駕駛汽車和智慧倉庫解決方案，以提高生產力、降低人事費用並最佳化供應鏈營運。強力的政府舉措、持續的技術進步以及對數位和智慧製造的高額投資正在進一步推動市場擴張。北美工業對營運效率、安全和數位轉型的關注正在加速基於人工智慧的物料輸送系統的採用，使該地區成為全球市場的關鍵貢獻者。

複合年成長率最高的地區：

在預測期內，由於工業的快速成長、電子商務的興起以及自動化技術的日益普及，亞太地區預計將呈現最高的複合年成長率。包括中國、日本和印度在內的主要國家正在大力投資人工智慧機器人、智慧倉庫和先進的製造解決方案，以提高生產力並減少對人工的依賴。物流和製造業的成長，加上政府支持數位轉型和工業4.0應用的舉措，正在推動市場擴張。對更快的訂單履行、即時庫存管理和靈活的物料輸送方案的需求日益成長，正在加速該地區人工智慧主導系統的採用。

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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球以人工智慧為基礎的物料輸送系統市場（以設備類型）

  • 自主移動機器人（AMR）
  • 自動導引運輸車（AGV）
  • 機械臂
  • 無人機
  • 自動化倉庫系統（AS/RS）
  • 基於視覺的檢測單元

6. 全球以人工智慧為基礎的物料輸送系統市場（按功能）

  • 運輸
  • 貯存
  • 拾取和放置
  • 包裝
  • 檢驗和品管

7. 全球以人工智慧為基礎的物料輸送系統市場（按最終用戶）

  • 車
  • 電子商務履約
  • 飲食
  • 製藥
  • 航太
  • 第三方物流（3PL）
  • 電子設備製造業

8. 全球以人工智慧為基礎的物料輸送系統市場（按地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第9章：主要進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第10章：企業概況

  • 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.