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市場調查報告書
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1865538

全球供應鏈人工智慧市場：未來預測（至2032年）－按產品、技術、應用、最終用戶和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2025 年，全球供應鏈人工智慧市場規模將達到 100.2 億美元，到 2032 年將達到 1,105.3 億美元，預測期內複合年成長率將達到 40.9%。

供應鏈人工智慧 (AI) 指的是利用先進的演算法、機器學習模型和數據驅動技術來提高供應鏈營運的效率、準確性和反應速度。透過分析海量的結構化和非結構化數據，人工智慧能夠實現需求預測、即時庫存管理、智慧物流最佳化和自動化決策。透過預測中斷並識別營運改善機會，它有助於降低風險、減少成本並提高客戶滿意度。將人工智慧整合到採購、生產、倉儲和配送流程中，可以將傳統的供應鏈轉變為敏捷、彈性且智慧的網路，從而能夠應對動態的市場需求和全球不確定性。

改進的庫存管理

企業利用人工智慧引擎預測需求、最佳化庫存水準並降低倉庫和配銷中心的持有成本。該平台支援即時追蹤、異常檢測以及利用歷史數據和外部變數進行自動補貨。與企業資源計劃 (ERP) 系統、物聯網感測器和物流網路的整合提高了可視性和應對力。零售、製造和醫療保健產業對預測性和自適應庫存管理的需求日益成長。這些趨勢正在推動以庫存為中心的供應鏈生態系統採用該平台。

熟練勞動力短缺

熟練勞動力短缺限制了人工智慧賦能供應鏈的平台擴充性和營運效率。人工智慧的應用需要資料科學、機器學習和供應鏈方面的專業知識，但這些人才在許多地區仍然短缺。企業在招募、培養和留住管理模型、解讀輸出結果和協調決策所需的人才方面面臨挑戰。缺乏標準化培訓和跨職能協作阻礙了平台的可靠性和業務影響力。這些限制因素持續阻礙中型企業和舊有系統供應鏈主導型企業採用人工智慧技術。

數據驅動決策

企業正利用人工智慧模擬各種場景、最佳化路線，並根據即時和歷史數據分配資源。平台支援動態定價、供應商評分以及全球網路中的中斷預測。與雲端基礎設施和分析儀表板的整合增強了透明度，並提升了經營團隊的決策一致性。採購營運和客戶互動中對擴充性的決策支援的需求日益成長。這些趨勢正在推動以洞察主導、數位化成熟的供應鏈生態系統的整體發展。

變革阻力與組織文化

傳統流程、職能孤島和規避風險的心態正在阻礙人工智慧的普及和跨職能協作。員工可能不信任演算法決策或擔心失業，導致人工智慧利用率低落和負面情緒。企業必須投資於變革管理、相關人員和管治框架，以確保目標一致和信任。經營團隊缺乏理解和文化準備仍然限制著平台性能和策略影響力。

新冠疫情的感染疾病：

疫情暴露了全球供應鏈的脆弱性，並加速了人工智慧在提升韌性和敏捷性方面的應用。企業利用人工智慧來應對供應鏈中斷、預測需求，並在動盪的市場環境下最佳化物流。各行各業對雲端原生平台、遠端監控和情境規劃的投資激增。消費者和政策制定者對供應鏈風險和數位轉型的認知度也顯著提高。後疫情時代的策略將人工智慧定位為供應鏈現代化和業務連續性的核心支柱。這些變化強化了對人工智慧基礎設施和決策支援系統的長期投資。

在預測期內，預測分析和機器學習領域將佔據最大的市場佔有率。

由於預測分析和機器學習在供應鏈營運的預測最佳化和異常檢測中發揮基礎性作用，預計在預測期內，該領域將佔據最大的市場佔有率。平台利用監督式和非監督式模型，實現高精度的需求預測、詐欺偵測和物流場景模擬。與即時資料來源、ERP系統和外部資料來源的整合，提高了應對力和決策靈活性。企業正在採用預測引擎來減少缺貨、最佳化運輸並預測供應商風險。供應商提供模組化引擎、API和視覺化工具，以促進跨部門應用和績效追蹤。零售、製造和醫療保健物流領域對擴充性、可解釋和可適應的人工智慧的需求日益成長。

在預測期內，醫療保健和生命科學產業的複合年成長率將最高。

在預測期內，醫療保健和生命科學領域預計將保持最高的成長率，這主要得益於人工智慧平台在醫藥物流、醫療供應鏈和以病人為中心的醫療服務模式中的應用。企業正在利用人工智慧來管理低溫運輸規性、最佳化庫存並預測醫院和分銷網路的需求。與電子健康記錄 (EHR) 系統、物聯網設備和法規結構的整合，增強了高價值和敏感貨物的可追溯性和風險緩解能力。疫苗分發、臨床試驗和個人化醫療工作流程正在推動對擴充性且合規的人工智慧基礎設施的需求。醫療服務提供者正在將供應鏈策略與病人安全、治療依從性和基於價值的醫療指標結合。這些趨勢正在推動醫療保健專用供應鏈平台和服務的快速成長。

佔比最大的地區：

由於企業對供應鏈技術、數位基礎設施和創新文化的大力投資，預計北美將在預測期內保持最大的市場佔有率。零售、製造、物流和醫療保健等行業的企業正在採用人工智慧平台，以最佳化營運並增強在動盪環境下的韌性。對雲端遷移、資料管治和人才培養的投資正在支持各行業的擴充性發展和合規性。主要供應商、研究機構和法規結構的存在正在推動生態系統的成熟和跨產業的應用。企業正在將人工智慧策略與環境、社會和治理 (ESG) 目標、客戶體驗以及供應鏈各環節的競爭優勢相結合。官民合作關係和聯邦政府主導的措施正在推動人工智慧在關鍵基礎設施和國家物流網路中的整合。

預計年複合成長率最高的地區：

預計亞太地區在預測期內將呈現最高的複合年成長率，這主要得益於工業數位化、電子商務的蓬勃發展以及醫療健康現代化在區域經濟中的融合。中國、印度、日本和韓國等國家正在製造業、物流和公共衛生供應鏈中推廣人工智慧平台。政府支持計畫正在推動人工智慧在供應鏈應用場景中的普及、基礎設施建設和Start-Ups孵化。本地供應商正在提供符合監管和營運需求、經濟高效且行動優先的本地化解決方案。消費者期望的不斷提高正在推動都市區和農村供應鏈網路中對擴充性、文化相容的人工智慧基礎設施的需求。企業正在將預測引擎與智慧倉庫管理、末端配送和跨境物流平台整合。

免費客製化服務

訂閱本報告的用戶可從以下免費自訂選項中選擇一項：

  • 公司簡介
    • 對最多三家其他公司進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域分類
    • 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
  • 競爭基準化分析
    • 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 分析方法
  • 分析材料
    • 原始研究資料
    • 二手研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 介紹
  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

5. 全球供應鏈人工智慧市場依產品/服務分類

  • 介紹
  • 硬體
    • 人工智慧感測器和物聯網設備
    • 自主機器人與無人機
    • 邊緣運算設備
  • 軟體
    • 基於人工智慧的供應鏈平台
    • 預測分析和最佳化工具
    • 庫存和需求管理系統
    • 運輸和車隊管理軟體
  • 服務
    • 諮詢及實施服務
    • 培訓和支持
    • 託管服務

6. 全球供應鏈人工智慧市場（按類型分類）

  • 介紹
  • 預測分析和機器學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 數位雙胞胎
  • 機器人流程自動化 (RPA)
  • 物聯網和邊緣人工智慧實現即時可視性
  • 用於需求規劃的生成式人工智慧
  • 其他技術

7. 全球供應鏈人工智慧市場（按應用分類）

  • 介紹
  • 需求預測
  • 庫存最佳化
  • 倉庫自動化
  • 車隊管理
  • 供應商關係管理
  • 風險與合規監控
  • 採購情報
  • 其他用途

8. 全球供應鏈人工智慧市場（按最終用戶分類）

  • 介紹
  • 車
  • 零售與電子商務
  • 製造業
  • 醫學與生命科​​學
  • 食品/飲料
  • 物流/運輸
  • 能源與公用事業
  • 其他最終用戶

9. 全球供應鏈人工智慧市場（按地區分類）

  • 介紹
  • 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
  • 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10章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第11章 公司簡介

  • 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.