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市場調查報告書
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2021753

人工智慧市場預測：供應鏈最佳化（2034 年）—按組件、技術、應用、最終用戶和地區分類的全球分析

AI in Supply Chain Optimization Market Forecasts to 2034 - Global Analysis By Component (Software, Hardware, and Services), Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 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預計將在 2026 年達到 125 億美元，到 2034 年達到 950 億美元，預測期內複合年成長率為 30%。

人工智慧在供應鏈最佳化的應用，運用先進的演算法、機器學習和數據分析，提升供應鏈營運的效率、準確性和應對力。這有助於需求預測、庫存管理、路線最佳化和即時決策。透過處理大量結構化和非結構化數據，人工智慧能夠降低營運成本、減少風險、簡化工作流程，進而提升整個供應鏈的整體績效和客戶滿意度。

全球供應鏈日益複雜，以及對即時視覺性的需求日益成長

現代供應鏈跨越多個地區，涉及眾多供應商、承運商和法規環境。這種複雜性導致資料孤島和決策延遲。利用人工智慧可以實現即時貨物追蹤、自動異常處理，並根據天氣和交通狀況動態調整路線。隨著客戶對更快交付和更透明的進度資訊的期望不斷提高，企業正在部署人工智慧驅動的控制塔和預測分析。這些工具提供端到端的可視性，幫助企業主動解決瓶頸問題並縮短前置作業時間。跨境電商交易量的不斷成長進一步增加了對智慧供應鏈調整的需求，使人工智慧成為在動盪的市場中保持競爭優勢的關鍵工具。

高昂的實施成本和資料整合挑戰

將人工智慧解決方案應用於供應鏈需要對物聯網感測器、邊緣設備、雲端基礎設施和熟練人員進行大量投資。由於缺乏標準化的資料格式，將人工智慧平台與眾多舊有系統整合既複雜又耗時。中小企業往往難以證明這些初始成本的合理性。此外，數據品質問題，例如記錄不完整或不一致，會導致預測不準確，並削弱人們對人工智慧輸出結果的信心。重新培訓員工以操作人工智慧驅動的系統也會增加成本。如果無法清楚證明投資報酬率，並且無法與現有的ERP和WMS平台無縫互通性，人工智慧的普及速度將依然緩慢，尤其是在傳統產業這種格局分散的領域。

擴展生成式人工智慧在自主供應鏈決策中的應用

生成式人工智慧透過實現場景模擬、自動化合約談判和動態補貨策略，為供應鏈最佳化開闢了新的可能性。與傳統的預測模型不同，生成式人工智慧能夠針對各種突發情況提案創新解決方案，例如替代採購路線和庫存重新分配計劃。數位雙胞胎技術的普及與生成式人工智慧的結合，使得企業能夠在虛擬環境中測試無數種「假設」情景，然後再將其應用於現實世界。此外，人工智慧聊天機器人正在改善與供應商的溝通和訂單追蹤。隨著雲端人工智慧平台的成本日益降低，中型物流供應商無需巨額資本投入即可獲得這些功能，從而為零售、製造和醫療保健行業創造了巨大的市場擴張機會。

網路安全漏洞和對黑盒模型的過度依賴

供應鏈最佳化中的人工智慧系統通常會聚合供應商定價、庫存水準和客戶位置等敏感數據，這使其成為網路攻擊的主要目標。一旦人工智慧模型遭到破壞，就可能導致需求預測不準確、配送路線錯誤，甚至庫存操縱。此外，許多先進的人工智慧演算法以「黑箱」的形式運行，其決策過程缺乏透明度。這種缺乏可解釋性的做法會引發供應鏈管理人員的信任危機，尤其是在監管審計或出現錯誤時。過度依賴人工智慧而缺乏人工監督會加劇系統性風險，例如多個地點同時出現缺貨。應對這些威脅需要強大的網路安全框架和可解釋的人工智慧技術。

新冠疫情的影響：

新冠疫情揭露了全球供應鏈的關鍵脆弱性，包括過度依賴單一供應商和缺乏即時可視性。封鎖和勞動力短缺擾亂了製造業和物流業，迫切需要採用人工智慧進行需求預測和風險監控。許多公司加快了對預測分析的投資，以應對消費行為的波動和原料供應的不確定性。疫情後，供應鏈韌性成為經營團隊的首要任務，推動了對人工智慧解決方案的持續需求。儘管在危機高峰期預算有限，但在復甦階段，基於雲端的人工智慧應用激增。疫情使人工智慧供應鏈市場受益，因為它永久地將焦點從單純的成本最佳化轉移到韌性和敏捷性。

在預測期內，軟體領域預計將成為規模最大的領域。

預計軟體領域將佔據最大的市場佔有率，這主要得益於人工智慧平台、倉庫管理系統 (WMS) 和需求預測工具的廣泛應用。這些軟體解決方案構成了智慧供應鏈的核心，能夠實現資料聚合、演算法執行和使用者友好的儀表板。與硬體不同，軟體具有擴充性和可隨時更新的特性，使其成為企業的理想選擇。機器學習庫和雲端供應鏈計畫套件的持續創新進一步鞏固了軟體的優勢。

在預測期內，邊緣運算設備細分市場預計將呈現最高的複合年成長率。

在供應鏈營運中，需要在靠近資料來源（例如倉庫、車輛和生產線）的地方進行即時處理，這使得邊緣運算設備有望展現出最高的成長速度。邊緣設備透過在本地分析RFID、攝影機和感測器數據，無需將所有數據發送到雲端，即可降低延遲和頻寬成本。自動堆高機、庫存無人機和智慧托盤的興起，正在加速對環境適應性強的邊緣硬體的需求。此外，5G的普及也提高了設備間的通訊速度。在低溫運輸監控和準時性至關重要的物流領域，邊緣運算能夠立即偵測異常情況，使其成為人工智慧驅動的供應鏈最佳化領域成長最快的硬體類別。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率。這主要得益於北美對先進技術的早期應用、AWS和微軟等主要雲端服務供應商的存在，以及競爭激烈的電子商務環境。美國在人工智慧驅動的倉庫自動化領域處於主導地位，亞馬遜和沃爾瑪等公司已樹立了行業標竿。大量創業投資湧入供應鏈人工智慧Start-Ups，以及成熟的物流基礎設施，進一步鞏固了這一優勢。此外，政府主導的旨在增強後疫情時代供應鏈韌性的舉措，推動了製造業和零售業對預測分析和數位雙胞胎的投資，從而鞏固了北美的主導地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的工業化進程、中國和印度電子商務的蓬勃發展以及不斷上漲的人事費用推動了自動化進程。日本、韓國和新加坡等國家正大力投資智慧工廠和人工智慧物流園區。該地區龐大的製造業基礎產生了大量數據，使其成為人工智慧驅動最佳化的理想之地。隨著後疫情時代供應鏈日益本地化，亞太企業正在尋求人工智慧解決方案，以平衡成本、速度和韌性，這推動了該地區的快速成長。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域細分
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 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
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球供應鏈最佳化中的人工智慧市場：按組件分類

  • 軟體
    • 人工智慧平台和分析工具
    • 倉庫管理系統（WMS）
    • 供應鏈計畫與執行軟體
    • 運輸管理系統（TMS）
    • 需求預測與庫存最佳化軟體
  • 硬體
    • 人工智慧晶片和處理器
    • 自動駕駛汽車和無人機
    • 物聯網感測器和射頻識別
    • 邊緣運算設備
  • 服務
    • 諮詢和策略服務
    • 託管服務
    • 整合和配置服務
    • 培訓和支援服務

第6章：全球供應鏈最佳化中的人工智慧市場：按技術分類

  • 機器學習（ML）
  • 人工智慧世代
  • 深度學習
  • 預測分析
  • 自然語言處理（NLP）
  • 強化學習
  • 電腦視覺

第7章：全球供應鏈最佳化中的人工智慧市場：按應用領域分類

  • 需求預測與規劃
  • 風險管理與韌性
  • 庫存最佳化
  • 供應商和採購管理
  • 倉庫自動化
  • 運輸和物流最佳化
  • 其他用途

第8章：全球供應鏈最佳化中的人工智慧市場：按最終用戶分類

  • 零售與電子商務
  • 製造業
  • 食品/飲料
  • 醫療和藥品
  • 車
  • 物流/運輸
  • 其他最終用戶

第9章：全球供應鏈最佳化中的人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第10章 戰略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第11章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第12章：公司簡介

  • IBM Corporation
  • o9 Solutions, Inc.
  • Microsoft Corporation
  • Manhattan Associates
  • Google LLC
  • Coupa Software
  • Amazon Web Services（AWS）
  • C3.ai
  • Oracle Corporation
  • Kinaxis Inc.
  • SAP SE
  • Blue Yonder Group, Inc.
  • NVIDIA Corporation
  • Logility, Inc.
  • Intel Corporation
Product Code: SMRC35018

According to Stratistics MRC, the Global AI in Supply Chain Optimization Market is accounted for $12.5 billion in 2026 and is expected to reach $95.0 billion by 2034, growing at a CAGR of 30% during the forecast period. AI in supply chain optimization is the application of advanced algorithms, machine learning, and data analytics to improve the efficiency, accuracy, and responsiveness of supply chain operations. It supports demand forecasting, inventory management, route optimization, and real-time decision-making. By processing large volumes of structured and unstructured data, it helps reduce operational costs, mitigate risks, and streamline workflows, leading to enhanced overall performance and improved customer satisfaction across the supply chain.

Market Dynamics:

Driver:

Rising complexity of global supply chains and need for real-time visibility

Modern supply chains span multiple geographies, involving numerous suppliers, carriers, and regulatory environments. This complexity creates data silos and delays in decision-making. AI enables real-time tracking of shipments, automated exception handling, and dynamic rerouting based on weather or traffic conditions. With increasing customer expectations for faster deliveries and transparent updates, companies are adopting AI-driven control towers and predictive analytics. These tools provide end-to-end visibility, helping firms proactively address bottlenecks and reduce lead times. The growing volume of cross-border e-commerce further amplifies the need for intelligent supply chain orchestration, making AI an indispensable tool for maintaining competitive advantage in volatile markets.

Restraint:

High implementation costs and data integration challenges

Deploying AI solutions in supply chains requires substantial investment in IoT sensors, edge devices, cloud infrastructure, and skilled personnel. Many legacy systems lack standardized data formats, making integration with AI platforms complex and time-consuming. Small and medium-sized enterprises often struggle to justify these upfront costs. Additionally, data quality issues such as incomplete or inconsistent records can lead to inaccurate predictions, undermining trust in AI outputs. Retraining workforce to operate AI-driven systems also adds to expenses. Without clear ROI demonstration and seamless interoperability between existing ERP and WMS platforms, adoption remains slow, particularly in traditional industries with fragmented technology landscapes.

Opportunity:

Expansion of generative AI for autonomous supply chain decision-making

Generative AI is opening new frontiers in supply chain optimization by enabling scenario simulation, automated contract negotiation, and dynamic replenishment strategies. Unlike traditional predictive models, generative AI can propose novel solutions to disruptions, such as alternative sourcing routes or inventory redistribution plans. The growth of digital twins combined with generative AI allows companies to test countless "what-if" scenarios in virtual environments before real-world execution. Furthermore, AI-powered chatbots are improving supplier communication and order tracking. As cloud-based AI platforms become more affordable, mid-sized logistics providers can access these capabilities without massive capital expenditure, creating significant opportunities for market expansion across retail, manufacturing, and healthcare sectors.

Threat:

Cybersecurity vulnerabilities and over-reliance on black-box models

AI systems in supply chain optimization often aggregate sensitive data, including supplier pricing, inventory levels, and customer locations, making them attractive targets for cyberattacks. A compromised AI model could lead to false demand forecasts, misrouted shipments, or inventory manipulation. Additionally, many advanced AI algorithms operate as "black boxes," offering little transparency into how decisions are made. This lack of explainability creates trust issues among supply chain managers, especially during regulatory audits or when errors occur. Over-reliance on AI without human oversight can amplify systemic risks, such as simultaneous stockouts across multiple locations. Addressing these threats requires robust cybersecurity frameworks and explainable AI techniques.

Covid-19 Impact:

The COVID-19 pandemic exposed critical weaknesses in global supply chains, including over-reliance on single-source suppliers and lack of real-time visibility. Lockdowns and labor shortages disrupted manufacturing and logistics, prompting urgent adoption of AI for demand sensing and risk monitoring. Many companies accelerated investments in predictive analytics to manage volatile consumer behavior and raw material availability. Post-pandemic, supply chain resilience has become a board-level priority, driving sustained demand for AI solutions. While initial budgets were constrained during peak crisis, the recovery phase saw a surge in cloud-based AI deployments. The pandemic permanently shifted focus from cost-only optimization to resilience and agility, benefiting the AI supply chain market.

The software segment is expected to be the largest during the forecast period

The software segment is projected to hold the largest market share, driven by widespread adoption of AI platforms, warehouse management systems (WMS), and demand forecasting tools. These software solutions form the brain of intelligent supply chains, enabling data aggregation, algorithm execution, and user-friendly dashboards. Unlike hardware, software offers scalability and regular over-the-air updates, making it attractive for enterprises. Continuous innovation in machine learning libraries and cloud-based supply chain planning suites further cements software dominance.

The edge computing devices segment is expected to have the highest CAGR during the forecast period

The edge computing devices are anticipated to witness the highest growth rate, as supply chain operations require real-time processing closer to data sources like warehouses, vehicles, and production lines. Edge devices reduce latency and bandwidth costs by analyzing RFID, camera, and sensor data locally without sending everything to the cloud. The rise of autonomous forklifts, drones for inventory counting, and smart pallets accelerates demand for ruggedized edge hardware. Additionally, 5G expansion enables faster device-to-device communication. For cold chain monitoring and time-sensitive logistics, edge computing ensures immediate anomaly detection, making it the fastest-growing hardware category within AI supply chain optimization.

Region with largest share:

During the forecast period, North America is expected to hold the largest market share, driven by early adoption of advanced technologies, presence of major cloud providers like AWS and Microsoft, and a highly competitive e-commerce landscape. The United States leads in AI-driven warehouse automation with companies like Amazon and Walmart setting benchmarks. Strong venture capital funding for supply chain AI startups and mature logistics infrastructure further support dominance. Additionally, government initiatives for supply chain resilience post-pandemic encourage investments in predictive analytics and digital twins across manufacturing and retail sectors, solidifying North America's leading position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid industrialization, booming e-commerce in China and India, and increasing labor costs pushing automation. Countries like Japan, South Korea, and Singapore are investing heavily in smart factories and AI-powered logistics parks. The region's vast manufacturing base generates massive data volumes, ideal for AI optimization. As supply chains become more regionalized post-pandemic, APAC companies seek AI solutions to balance cost, speed, and resilience, driving the fastest growth.

Key players in the market

Some of the key players in AI in Supply Chain Optimization Market include IBM Corporation, o9 Solutions, Inc., Microsoft Corporation, Manhattan Associates, Google LLC, Coupa Software, Amazon Web Services (AWS), C3.ai, Oracle Corporation, Kinaxis Inc., SAP SE, Blue Yonder Group, Inc., NVIDIA Corporation, Logility, Inc., and Intel Corporation.

Key Developments:

In April 2026, IBM announced a strategic collaboration with Arm to develop new dual-architecture hardware that helps enterprises run future AI and data intensive workloads with greater flexibility, reliability, and security. IBM's leadership in system design, from silicon to software and security, has helped enterprises adopt emerging technologies with the scale and reliability required for mission-critical workloads.

In March 2026, Oracle announced the latest updates to Oracle AI Agent Studio for Fusion Applications, a complete development platform for building, connecting, and running AI automation and agentic applications. The latest updates to Oracle AI Agent Studio include a new agentic applications builder as well as new capabilities that support workflow orchestration, content intelligence, contextual memory, and ROI measurement.

Components Covered:

  • Software
  • Hardware
  • Services

Technologies Covered:

  • Machine Learning (ML)
  • Generative AI
  • Deep Learning
  • Predictive Analytics
  • Natural Language Processing (NLP)
  • Reinforcement Learning
  • Computer Vision

Applications Covered:

  • Demand Forecasting & Planning
  • Risk Management & Resilience
  • Inventory Optimization
  • Supplier & Procurement Management
  • Warehouse Automation
  • Transportation & Logistics Optimization
  • Other Applications

End Users Covered:

  • Retail & E-commerce
  • Manufacturing
  • Food & Beverage
  • Healthcare & Pharmaceuticals
  • Automotive
  • Logistics & Transportation
  • 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 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Supply Chain Optimization Market, By Component

  • 5.1 Software
    • 5.1.1 AI Platforms & Analytics Tools
    • 5.1.2 Warehouse Management Systems (WMS)
    • 5.1.3 Supply Chain Planning & Execution Software
    • 5.1.4 Transportation Management Systems (TMS)
    • 5.1.5 Demand Forecasting & Inventory Optimization Software
  • 5.2 Hardware
    • 5.2.1 AI Chips & Processors
    • 5.2.2 Autonomous Vehicles & Drones
    • 5.2.3 IoT Sensors & RFID
    • 5.2.4 Edge Computing Devices
  • 5.3 Services
    • 5.3.1 Consulting & Strategy Services
    • 5.3.2 Managed Services
    • 5.3.3 Integration & Deployment Services
    • 5.3.4 Training & Support Services

6 Global AI in Supply Chain Optimization Market, By Technology

  • 6.1 Machine Learning (ML)
  • 6.2 Generative AI
  • 6.3 Deep Learning
  • 6.4 Predictive Analytics
  • 6.5 Natural Language Processing (NLP)
  • 6.6 Reinforcement Learning
  • 6.7 Computer Vision

7 Global AI in Supply Chain Optimization Market, By Application

  • 7.1 Demand Forecasting & Planning
  • 7.2 Risk Management & Resilience
  • 7.3 Inventory Optimization
  • 7.4 Supplier & Procurement Management
  • 7.5 Warehouse Automation
  • 7.6 Transportation & Logistics Optimization
  • 7.7 Other Applications

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

  • 8.1 Retail & E-commerce
  • 8.2 Manufacturing
  • 8.3 Food & Beverage
  • 8.4 Healthcare & Pharmaceuticals
  • 8.5 Automotive
  • 8.6 Logistics & Transportation
  • 8.7 Other End Users

9 Global AI in Supply Chain Optimization Market, By Geography

  • 9.1 North America
    • 9.1.1 United States
    • 9.1.2 Canada
    • 9.1.3 Mexico
  • 9.2 Europe
    • 9.2.1 United Kingdom
    • 9.2.2 Germany
    • 9.2.3 France
    • 9.2.4 Italy
    • 9.2.5 Spain
    • 9.2.6 Netherlands
    • 9.2.7 Belgium
    • 9.2.8 Sweden
    • 9.2.9 Switzerland
    • 9.2.10 Poland
    • 9.2.11 Rest of Europe
  • 9.3 Asia Pacific
    • 9.3.1 China
    • 9.3.2 Japan
    • 9.3.3 India
    • 9.3.4 South Korea
    • 9.3.5 Australia
    • 9.3.6 Indonesia
    • 9.3.7 Thailand
    • 9.3.8 Malaysia
    • 9.3.9 Singapore
    • 9.3.10 Vietnam
    • 9.3.11 Rest of Asia Pacific
  • 9.4 South America
    • 9.4.1 Brazil
    • 9.4.2 Argentina
    • 9.4.3 Colombia
    • 9.4.4 Chile
    • 9.4.5 Peru
    • 9.4.6 Rest of South America
  • 9.5 Rest of the World (RoW)
    • 9.5.1 Middle East
      • 9.5.1.1 Saudi Arabia
      • 9.5.1.2 United Arab Emirates
      • 9.5.1.3 Qatar
      • 9.5.1.4 Israel
      • 9.5.1.5 Rest of Middle East
    • 9.5.2 Africa
      • 9.5.2.1 South Africa
      • 9.5.2.2 Egypt
      • 9.5.2.3 Morocco
      • 9.5.2.4 Rest of Africa

10 Strategic Market Intelligence

  • 10.1 Industry Value Network and Supply Chain Assessment
  • 10.2 White-Space and Opportunity Mapping
  • 10.3 Product Evolution and Market Life Cycle Analysis
  • 10.4 Channel, Distributor, and Go-to-Market Assessment

11 Industry Developments and Strategic Initiatives

  • 11.1 Mergers and Acquisitions
  • 11.2 Partnerships, Alliances, and Joint Ventures
  • 11.3 New Product Launches and Certifications
  • 11.4 Capacity Expansion and Investments
  • 11.5 Other Strategic Initiatives

12 Company Profiles

  • 12.1 IBM Corporation
  • 12.2 o9 Solutions, Inc.
  • 12.3 Microsoft Corporation
  • 12.4 Manhattan Associates
  • 12.5 Google LLC
  • 12.6 Coupa Software
  • 12.7 Amazon Web Services (AWS)
  • 12.8 C3.ai
  • 12.9 Oracle Corporation
  • 12.10 Kinaxis Inc.
  • 12.11 SAP SE
  • 12.12 Blue Yonder Group, Inc.
  • 12.13 NVIDIA Corporation
  • 12.14 Logility, Inc.
  • 12.15 Intel Corporation

List of Tables

  • Table 1 Global AI in Supply Chain Optimization Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Supply Chain Optimization Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in Supply Chain Optimization Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI in Supply Chain Optimization Market Outlook, By AI Platforms & Analytics Tools (2023-2034) ($MN)
  • Table 5 Global AI in Supply Chain Optimization Market Outlook, By Warehouse Management Systems (WMS) (2023-2034) ($MN)
  • Table 6 Global AI in Supply Chain Optimization Market Outlook, By Supply Chain Planning & Execution Software (2023-2034) ($MN)
  • Table 7 Global AI in Supply Chain Optimization Market Outlook, By Transportation Management Systems (TMS) (2023-2034) ($MN)
  • Table 8 Global AI in Supply Chain Optimization Market Outlook, By Demand Forecasting & Inventory Optimization Software (2023-2034) ($MN)
  • Table 9 Global AI in Supply Chain Optimization Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 10 Global AI in Supply Chain Optimization Market Outlook, By AI Chips & Processors (2023-2034) ($MN)
  • Table 11 Global AI in Supply Chain Optimization Market Outlook, By Autonomous Vehicles & Drones (2023-2034) ($MN)
  • Table 12 Global AI in Supply Chain Optimization Market Outlook, By IoT Sensors & RFID (2023-2034) ($MN)
  • Table 13 Global AI in Supply Chain Optimization Market Outlook, By Edge Computing Devices (2023-2034) ($MN)
  • Table 14 Global AI in Supply Chain Optimization Market Outlook, By Services (2023-2034) ($MN)
  • Table 15 Global AI in Supply Chain Optimization Market Outlook, By Consulting & Strategy Services (2023-2034) ($MN)
  • Table 16 Global AI in Supply Chain Optimization Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 17 Global AI in Supply Chain Optimization Market Outlook, By Integration & Deployment Services (2023-2034) ($MN)
  • Table 18 Global AI in Supply Chain Optimization Market Outlook, By Training & Support Services (2023-2034) ($MN)
  • Table 19 Global AI in Supply Chain Optimization Market Outlook, By Technology (2023-2034) ($MN)
  • Table 20 Global AI in Supply Chain Optimization Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 21 Global AI in Supply Chain Optimization Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 22 Global AI in Supply Chain Optimization Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 23 Global AI in Supply Chain Optimization Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 24 Global AI in Supply Chain Optimization Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 25 Global AI in Supply Chain Optimization Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 26 Global AI in Supply Chain Optimization Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 27 Global AI in Supply Chain Optimization Market Outlook, By Application (2023-2034) ($MN)
  • Table 28 Global AI in Supply Chain Optimization Market Outlook, By Demand Forecasting & Planning (2023-2034) ($MN)
  • Table 29 Global AI in Supply Chain Optimization Market Outlook, By Risk Management & Resilience (2023-2034) ($MN)
  • Table 30 Global AI in Supply Chain Optimization Market Outlook, By Inventory Optimization (2023-2034) ($MN)
  • Table 31 Global AI in Supply Chain Optimization Market Outlook, By Supplier & Procurement Management (2023-2034) ($MN)
  • Table 32 Global AI in Supply Chain Optimization Market Outlook, By Warehouse Automation (2023-2034) ($MN)
  • Table 33 Global AI in Supply Chain Optimization Market Outlook, By Transportation & Logistics Optimization (2023-2034) ($MN)
  • Table 34 Global AI in Supply Chain Optimization Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 35 Global AI in Supply Chain Optimization Market Outlook, By End User (2023-2034) ($MN)
  • Table 36 Global AI in Supply Chain Optimization Market Outlook, By Retail & E-commerce (2023-2034) ($MN)
  • Table 37 Global AI in Supply Chain Optimization Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 38 Global AI in Supply Chain Optimization Market Outlook, By Food & Beverage (2023-2034) ($MN)
  • Table 39 Global AI in Supply Chain Optimization Market Outlook, By Healthcare & Pharmaceuticals (2023-2034) ($MN)
  • Table 40 Global AI in Supply Chain Optimization Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 41 Global AI in Supply Chain Optimization Market Outlook, By Logistics & Transportation (2023-2034) ($MN)
  • Table 42 Global AI in Supply Chain Optimization Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.