查看購物車
  • Simplified Chinese
  • English
  • Japanese
封面
市場調查報告書
商品編碼
1776776

2032 年供應鏈最佳化市場人工智慧預測：按產品、技術、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球供應鏈最佳化人工智慧市場規模預計在 2025 年達到 99 億美元，到 2032 年將達到 1,050 億美元，預測期內的複合年成長率為 40.1%。

供應鏈最佳化中的人工智慧 (AI) 涉及利用人工智慧來增強物流和營運。人工智慧演算法可以分析數據並簡化庫存管理、需求預測和運輸路線等流程。它們透過預測中斷並最佳化整個供應鏈的資源分配，來提高效率、降低成本並增強決策能力。

據麥肯錫稱，在供應鏈中使用人工智慧的公司已經將物流成本降低了 12.7%，庫存水準降低了 20.3%，節省了數十億美元。

電子商務和全球貿易的成長

電商平台的激增和供應網路的全球化正在加速人工智慧供應鏈解決方案的採用。在消費者對即時交付和透明度的期望推動下，企業正在使用人工智慧來最佳化庫存、路線和履約業務。在管理海量產品和多層級供應商生態系統的需求推動下，人工智慧提供了端到端的可視性和回應能力。在成本最佳化需求的推動下，人工智慧正迅速成為提高現代供應鏈效率和韌性的策略工具。

資料整合和互通性問題

儘管人工智慧的能力日益增強，但將其整合到現有的供應鏈基礎設施中仍面臨重大挑戰。由於IT系統碎片化以及跨部門和合作夥伴的資料孤島，實現無縫互通性往往舉步維艱。由於缺乏即時資料處理能力的遺留舊有系統，許多公司仍未充分挖掘人工智慧的潛力。在這些限制因素的驅動下，統一的數位架構和強大的數據標準對於人工智慧主導的供應鏈充分發揮其潛力至關重要。

提高需求預測準確性

人工智慧能夠改善需求預測，進而改變供應鏈的效率和回應能力。基於歷史資料、天氣趨勢、市場情緒和社會經濟指標訓練的機器學習模型，使預測更加動態和精細。預測誤差的減少，使企業能夠最大限度地減少缺貨、降低持有成本並提升服務水準。在人工智慧預測能力的指導下，企業還可以模擬各種「假設」供應鏈場景，從而提高準備度和敏捷性。

過度依賴人工智慧系統

供應鏈管理決策日益依賴人工智慧，這會帶來與系統故障和意外資料異常相關的風險。在關鍵流程自動化的推動下，過度依賴人工智慧可能會降低人類的監督和解決問題的能力。人工智慧解讀背景和應對「黑天鵝」事件的能力可能會被推到極限，導致組織在特殊情況下容易受到干擾。這些擔憂促使企業必須在人工智慧自動化和人類專業知識之間取得平衡，以維護具有韌性的供應鏈。

COVID-19的影響：

新冠疫情暴露了全球供應鏈的嚴重漏洞，並加速了對人工智慧最佳化工具的投資。在不可預測的需求模式、運輸延誤和原料短缺的刺激下，人工智慧幫助企業快速重構了採購和分銷模式。隨著向遠端辦公和雲端協作工具的轉變，人工智慧平台在疫情期間變得更加易於存取和可擴展。受這些經驗教訓的啟發，企業現在正將人工智慧更深入地嵌入其供應鏈策略中，以確保長期的韌性。

機器學習領域預計將成為預測期內最大的領域

機器學習領域預計將在預測期內佔據最大的市場佔有率，因為它能夠靈活應對各種供應鏈挑戰。在監督學習和無監督學習模式不斷進步的推動下，這項技術正擴大被納入企業供應鏈軟體。隨著機器學習在採購、分銷、物流和客戶服務領域的廣泛應用，它正被整體採用。由於其擴充性和整合潛力，預計該領域將在整個預測期內保持主導地位。

預計供應鏈規劃部門在預測期內將以最高複合年成長率成長

預計供應鏈規劃領域將在預測期內實現最高成長率，這得益於對即時可視性和主動決策日益成長的需求。消費者需求波動和地緣政治不確定性造成的干擾，使得人工智慧主導的規劃工具變得至關重要。基於人工智慧的規劃，由需求感知、生產調度和資源分配的整合所驅動，提供了一種統一且動態的方法。在競爭壓力和以客戶為中心的物流的推動下，規劃功能正在發展成為人工智慧驅動的供應鏈轉型的核心參與者。

比最大的地區

亞太地區預計將在預測期內佔據最大的市場佔有率，這得益於其作為全球製造和物流中心的地位。在中國、日本和印度等國家快速數位轉型的推動下，人工智慧的應用正在工業和零售供應鏈中不斷擴展。在政府的大力支持下，該地區的新興企業擴大提供針對區域市場動態客製化的人工智慧供應鏈管理平台。在具有成本競爭力的勞動力、龐大的分銷網路和不斷發展的數位基礎設施的推動下，亞太地區在人工智慧主導的供應鏈應用方面佔據主導地位。

複合年成長率最高的地區：

在預測期內，北美預計將呈現最高的複合年成長率，這主要得益於零售、汽車和醫療保健行業的需求。即時供應計劃和預測性維護是重點關注領域。隨著全球動盪加劇，北美企業正轉向人工智慧來降低風險並增強情境建模。在由人工智慧開發者、雲端服務供應商和整合商組成的強大生態系統的支援下，該地區的企業在物流和採購領域應用人工智慧方面處於領先地位。在數據管治標準和創新津貼的指導下，該地區將繼續引領供應鏈轉型。

提供免費客製化：

此報告的訂閱者可以從以下免費自訂選項中選擇一項：

  • 公司簡介
    • 對最多三家其他市場公司進行全面分析
    • 主要企業的SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 根據產品系列、地理分佈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 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
  • 技術分析
  • 應用分析
  • 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
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球供應鏈最佳化人工智慧市場（按產品提供）

  • 硬體
  • 軟體
  • 服務

6. 全球供應鏈最佳化人工智慧市場（按技術）

  • 機器學習
  • 電腦視覺
  • 自然語言處理
  • 情境感知計算
  • 其他技術

7. 全球供應鏈最佳化人工智慧市場（按應用）

  • 供應鏈計劃
  • 倉庫管理
  • 車隊管理
  • 虛擬助手
  • 風險管理
  • 庫存管理
  • 規劃與物流

8. 全球供應鏈最佳化人工智慧市場（按最終用戶）

  • 製造業
  • 食品/飲料
  • 衛生保健
  • 車
  • 航太
  • 零售
  • 消費品
  • 其他最終用戶

9. 全球供應鏈最佳化人工智慧市場（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10章：主要發展

  • 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11章 公司概況

  • 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.