查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2111220

人工智慧驅動的供應鏈編配市場預測至2034年：全球部署模式、產品/服務、服務類型、人工智慧技術、功能、應用、最終用戶和區域分析

AI-Based Supply Chain Orchestration Market Forecasts to 2034 - Global Analysis By Deployment (On-Premise, Cloud-Based, and Hybrid), Offering, Service Type, AI Technology, Function, 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，全球基於人工智慧的供應鏈編配市場預計將在 2026 年達到 44 億美元，並在預測期內以 15.2% 的複合年成長率成長，到 2034 年達到 137 億美元。

人工智慧驅動的供應鏈編配是指利用人工智慧技術即時協調和同步多個供應鏈職能部門的規劃、採購、物流和庫存決策的軟體平台。這些平台從供應商、運輸網路、倉庫和需求訊號中獲取數據，並應用機器學習演算法產生同步建議，以平衡供應計劃、運輸和訂單管理中相互衝突的目標。這使得團隊能夠動態應對複雜多層分銷網路中的中斷和需求波動。

應對供應鏈中斷的韌性

地緣政治緊張局勢和極端天氣事件等反覆出現的全球供應鏈中斷，迫使企業採用基於人工智慧的編配平台，以便動態調整配送路線並在整個分銷網路中重新分配庫存。供應鏈管理人員越來越依賴演算法推薦，以便在中斷期間尋找替代供應商和運輸路線。此外，透過即時了解多層供應商網路來縮短反應時間的能力，也推動了製造業和零售業對編配能力的持續投資。

系統間的資料孤島

許多公司由於採購、倉儲和運輸管理系統之間缺乏互聯互通，導致供應鏈數據分散，這使得整合式人工智慧編配平台的實施變得複雜。要實現供應商網路、內部系統和第三方物流供應商之間的無縫資料整合，需要對資料工程和持續的資料管治進行大量投資。同時，貿易夥伴之間缺乏統一的資料標準會降低演算法的準確性，從而可能延緩在複雜的多層供應鏈中編配平台。

利用生成式人工智慧進行場景規劃

將生成式人工智慧整合到供應鏈編配平台中，為自動化場景規劃創造了機會。這使得團隊能夠使用自然語言查詢而非複雜的建模工具來模擬應對突發事件的策略。供應商正擴大採用互動式介面，匯總成本、服務水準和風險之間的權衡取捨，從而降低了利用高級編配功能所需的技術專長，並將目標市場擴展到包括中型企業在內的廣大用戶。

對供應商鎖定問題的擔憂

隨著企業對專有人工智慧編配平台的依賴程度日益加深，對供應商鎖定問題的擔憂也日益加劇。這是因為將複雜的整合功能和歷史資料遷移到其他供應商的成本可能相當高。由於擔心長期定價權和平台柔軟性，企業可能對完全過渡到單一供應商的編配系統猶豫不決。此外，軟體供應商之間日益增強的整合可能會進一步縮小選擇範圍，並在供應鏈技術採購決策中帶來策略風險。

新型冠狀病毒（COVID-19）的影響：

疫情初期，工廠停工和運輸瓶頸擾亂了全球供應鏈，暴露出許多產業傳統規劃系統的脆弱性。疫情期間，企業加速採用人工智慧驅動的編配工具，以動態調整運輸路線並應對短缺。疫情後，供應鏈韌性已成為長期策略重點，全球企業紛紛將編配平台整合到其長期風險管理架構中。

在預測期內，基於雲端的細分市場預計將佔據最大的市場佔有率。

在預測期內，基於雲端的細分市場預計將佔據最大的市場佔有率。這是因為企業更傾向於採用基於訂閱的編配平台，這些平台能夠在無需大量前期基礎設施投資的情況下，快速擴展其全球供應商網路。雲端採用還有助於在地理位置分散的供應鏈合作夥伴之間實現即時資料共用和更快的功能更新。此外，更低的總體擁有成本 (TCO) 進一步鞏固了該細分市場在製造和零售供應鏈營運中的主導地位。

預計在預測期內，軟體領域將呈現最高的複合年成長率。

在預測期內，軟體領域預計將呈現最高的成長率。這主要得益於供應商不斷透過頻繁的功能發布和擴展許可層級，將先進的機器學習和生成式人工智慧功能整合到編配平台中。隨著演算法的複雜性成為關鍵的競爭優勢，企業越來越傾向於可擴展的軟體訂閱模式，這種模式允許他們逐步添加功能，而無需簽訂大規模服務契約，從而加速了整個行業軟體領域的收入成長。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率。這主要得益於美國龐大的零售和製造供應鏈基礎設施，以及企業對人工智慧驅動的編配平台的早期採用。包括SAP SE和Oracle Corporation在內的領先技術供應商在該地區保持著強大的市場地位，而數位化供應鏈轉型的巨額資本投資也進一步鞏固了北美在編配平台各個細分領域的領先地位。

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。這是因為中國、印度和東南亞製造業的快速擴張，以及跨境電子商務活動的成長，正在推動對人工智慧驅動的供應鏈協調工具的需求。政府支持數位貿易基礎設施和出口導向生產的舉措，正在促進對編配技術的資本投資，而該地區製造業日益複雜的現狀，也持續推動全部區域供應鏈可視性的需求。

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
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶要求，我們可以提供主要國家的市場估算和預測，以及複合年成長率（註：需經可行性確認）。
  • 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領先公司進行基準分析。

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
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧驅動的供應鏈編配市場：依部署方式分類

  • 現場
  • 基於雲端的
  • 混合

第6章：全球人工智慧驅動的供應鏈編配市場：依產品/服務分類

  • 軟體
  • 服務

第7章：全球人工智慧驅動的供應鏈編配市場：按服務類型分類

  • 諮詢
  • 執行
  • 支援與維護
  • 託管服務

第8章：全球人工智慧驅動的供應鏈編配市場：按人工智慧技術分類

  • 機器學習
  • 深度學習
  • 自然語言處理
  • 電腦視覺
  • 人工智慧世代

第9章：全球人工智慧驅動的供應鏈編配市場：按功能分類

  • 需求計劃
  • 庫存最佳化
  • 物流編配
  • 與供應商合作
  • 訂單管理
  • 採購最佳化

第10章：全球人工智慧驅動的供應鏈編配市場：按應用領域分類

  • 供應計劃
  • 運輸管理
  • 倉庫最佳化
  • 生產計畫
  • 風險管理
  • 最後一公里配送

第11章 全球人工智慧驅動的供應鏈編配市場：依最終用戶分類

  • 製造業
  • 零售
  • 衛生保健
  • 車
  • 食品/飲料
  • 消費品
  • 物流

第12章 全球人工智慧驅動的供應鏈編配市場：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13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14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14章：公司簡介

  • SAP SE
  • Oracle Corporation
  • Blue Yonder Group, Inc.
  • Kinaxis Inc.
  • o9 Solutions, Inc.
  • Manhattan Associates, Inc.
  • Infor Inc.
  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services, Inc.
  • Accenture plc
  • Infosys Limited
  • Wipro Limited
  • Schneider Electric SE
  • Siemens AG
  • Coupa Software Inc.
Product Code: SMRC38824

According to Stratistics MRC, the Global AI-Based Supply Chain Orchestration Market is accounted for $4.4 billion in 2026 and is expected to reach $13.7 billion by 2034 growing at a CAGR of 15.2% during the forecast period. AI-based supply chain orchestration refers to software platforms that apply artificial intelligence techniques to coordinate and synchronize planning, procurement, logistics, and inventory decisions across multiple supply chain functions in real time. These platforms ingest data from suppliers, transportation networks, warehouses, and demand signals, then apply machine learning algorithms to generate synchronized recommendations that balance competing objectives across supply planning, transportation, and order management, thereby enabling teams to respond dynamically to disruptions and demand fluctuations across complex, multi-tier distribution networks.

Market Dynamics:

Driver:

Supply Chain Disruption Resilience

Recurring global supply chain disruptions, including geopolitical tensions and extreme weather events, are driving enterprises to adopt AI-based orchestration platforms capable of dynamically rerouting shipments and reallocating inventory across distribution networks. Supply chain leaders increasingly rely on algorithmic recommendations to identify alternative suppliers and transportation routes during disruptions, while real-time visibility into multi-tier supplier networks reduces response times, driving sustained investment in orchestration capabilities across manufacturing and retail sectors.

Restraint:

Cross-System Data Silos

Many enterprises continue to operate fragmented supply chain data across disconnected procurement, warehouse, and transportation management systems that complicate the deployment of unified AI orchestration platforms. Achieving seamless data integration across supplier networks, internal enterprise systems, and third-party logistics providers requires substantial data engineering investment and ongoing governance, while inconsistent data standards across trading partners can undermine algorithmic accuracy, thereby slowing enterprise-wide orchestration platform adoption across complex multi-tier supply chains.

Opportunity:

Generative AI Scenario Planning

The integration of generative artificial intelligence into supply chain orchestration platforms is creating opportunities for automated scenario planning that allows teams to simulate disruption responses using natural language queries rather than complex modeling tools. Vendors are increasingly embedding conversational interfaces that summarize trade-offs across cost, service level, and risk dimensions, while this lowers the technical expertise required to leverage advanced orchestration capabilities, expanding the addressable market across mid-sized enterprises.

Threat:

Vendor Lock-In Concerns

Growing enterprise dependency on proprietary AI orchestration platforms raises concerns regarding vendor lock-in, as switching costs associated with migrating complex integrations and historical data to alternative providers can become substantial. Enterprises may hesitate to fully commit to single-vendor orchestration ecosystems due to concerns about long-term pricing power and platform flexibility, while consolidation among software vendors could further reduce alternatives, creating strategic risk for supply chain technology procurement decisions.

Covid-19 Impact:

The pandemic initially disrupted global supply chains through factory closures and transportation bottlenecks, exposing vulnerabilities in traditional planning systems across many industries. Mid-pandemic, enterprises accelerated adoption of AI-driven orchestration tools to dynamically reroute shipments and manage shortages. Post-pandemic, supply chain resilience became a permanent strategic priority, with enterprises embedding orchestration platforms into long-term risk management frameworks worldwide.

The cloud-based segment is expected to be the largest during the forecast period

The cloud-based segment is expected to account for the largest market share during the forecast period, due to enterprises favoring subscription-based orchestration platforms that enable rapid scaling across global supplier networks without substantial upfront infrastructure investment. Cloud deployment also facilitates real-time data sharing across geographically dispersed supply chain partners and faster feature updates, while lower total cost of ownership continues to reinforce this segment's leading position across manufacturing and retail supply chain operations.

The software segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the software segment is predicted to witness the highest growth rate, driven by vendors continuously embedding advanced machine learning and generative AI capabilities into orchestration platforms through frequent feature releases and expanded licensing tiers. Enterprises increasingly favor scalable software subscriptions that allow incremental capability additions without extensive service engagements, as algorithmic sophistication becomes a key competitive differentiator, which in turn accelerates software segment revenue growth across the industry.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the United States possessing extensive retail and manufacturing supply chain infrastructure combined with early enterprise adoption of AI-driven orchestration platforms. Leading technology vendors including SAP SE and Oracle Corporation maintain substantial regional presence, while significant capital investment in digital supply chain transformation continues to reinforce North America's dominant position across orchestration platform segments.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to rapid manufacturing expansion and growing cross-border e-commerce activity across China, India, and Southeast Asia driving demand for AI-powered supply chain coordination tools. Government initiatives supporting digital trade infrastructure and export-oriented production are encouraging capital investment in orchestration technology, while rising regional manufacturing complexity continues to fuel demand for synchronized supply chain visibility across the region.

Key players in the market

Some of the key players in AI-Based Supply Chain Orchestration Market include SAP SE, Oracle Corporation, Blue Yonder Group, Inc., Kinaxis Inc., o9 Solutions, Inc., Manhattan Associates, Inc., Infor Inc., IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services, Inc., Accenture plc, Infosys Limited, Wipro Limited, Schneider Electric SE, Siemens AG and Coupa Software Inc.

Key Developments:

In June 2026, Kinaxis Inc. launched an updated concurrent planning module featuring generative AI scenario simulation, enabling supply chain teams to evaluate disruption responses across multiple network configurations simultaneously and rapidly.

In May 2026, o9 Solutions, Inc. expanded its orchestration platform with enhanced supplier risk scoring capabilities, helping enterprises identify vulnerable nodes across multi-tier supply networks before disruptions materialize into costly operations.

In April 2026, SAP SE integrated advanced machine learning forecasting models into its supply chain orchestration suite, improving demand prediction accuracy for enterprises managing complex, high-variability product portfolios across global regions.

Deployments Covered:

  • On-Premise
  • Cloud-Based
  • Hybrid

Offerings Covered:

  • Software
  • Services

Service Types Covered:

  • Consulting
  • Implementation
  • Support and Maintenance
  • Managed Services

AI Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Generative AI

Functions Covered:

  • Demand Planning
  • Inventory Optimization
  • Logistics Orchestration
  • Supplier Collaboration
  • Order Management
  • Procurement Optimization

Applications Covered:

  • Supply Planning
  • Transportation Management
  • Warehouse Optimization
  • Production Planning
  • Risk Management
  • Last-Mile Delivery

End Users Covered:

  • Manufacturing
  • Retail
  • Healthcare
  • Automotive
  • Food and Beverage
  • Consumer Goods
  • Logistics

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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, 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-Based Supply Chain Orchestration Market, By Deployment

  • 5.1 On-Premise
  • 5.2 Cloud-Based
  • 5.3 Hybrid

6 Global AI-Based Supply Chain Orchestration Market, By Offering

  • 6.1 Software
  • 6.2 Services

7 Global AI-Based Supply Chain Orchestration Market, By Service Type

  • 7.1 Consulting
  • 7.2 Implementation
  • 7.3 Support and Maintenance
  • 7.4 Managed Services

8 Global AI-Based Supply Chain Orchestration Market, By AI Technology

  • 8.1 Machine Learning
  • 8.2 Deep Learning
  • 8.3 Natural Language Processing
  • 8.4 Computer Vision
  • 8.5 Generative AI

9 Global AI-Based Supply Chain Orchestration Market, By Function

  • 9.1 Demand Planning
  • 9.2 Inventory Optimization
  • 9.3 Logistics Orchestration
  • 9.4 Supplier Collaboration
  • 9.5 Order Management
  • 9.6 Procurement Optimization

10 Global AI-Based Supply Chain Orchestration Market, By Application

  • 10.1 Supply Planning
  • 10.2 Transportation Management
  • 10.3 Warehouse Optimization
  • 10.4 Production Planning
  • 10.5 Risk Management
  • 10.6 Last-Mile Delivery

11 Global AI-Based Supply Chain Orchestration Market, By End User

  • 11.1 Manufacturing
  • 11.2 Retail
  • 11.3 Healthcare
  • 11.4 Automotive
  • 11.5 Food and Beverage
  • 11.6 Consumer Goods
  • 11.7 Logistics

12 Global AI-Based Supply Chain Orchestration Market, By Geography

  • 12.1 North America
    • 12.1.1 United States
    • 12.1.2 Canada
    • 12.1.3 Mexico
  • 12.2 Europe
    • 12.2.1 United Kingdom
    • 12.2.2 Germany
    • 12.2.3 France
    • 12.2.4 Italy
    • 12.2.5 Spain
    • 12.2.6 Netherlands
    • 12.2.7 Belgium
    • 12.2.8 Sweden
    • 12.2.9 Switzerland
    • 12.2.10 Poland
    • 12.2.11 Rest of Europe
  • 12.3 Asia Pacific
    • 12.3.1 China
    • 12.3.2 Japan
    • 12.3.3 India
    • 12.3.4 South Korea
    • 12.3.5 Australia
    • 12.3.6 Indonesia
    • 12.3.7 Thailand
    • 12.3.8 Malaysia
    • 12.3.9 Singapore
    • 12.3.10 Vietnam
    • 12.3.11 Rest of Asia Pacific
  • 12.4 South America
    • 12.4.1 Brazil
    • 12.4.2 Argentina
    • 12.4.3 Colombia
    • 12.4.4 Chile
    • 12.4.5 Peru
    • 12.4.6 Rest of South America
  • 12.5 Rest of the World (RoW)
    • 12.5.1 Middle East
      • 12.5.1.1 Saudi Arabia
      • 12.5.1.2 United Arab Emirates
      • 12.5.1.3 Qatar
      • 12.5.1.4 Israel
      • 12.5.1.5 Rest of Middle East
    • 12.5.2 Africa
      • 12.5.2.1 South Africa
      • 12.5.2.2 Egypt
      • 12.5.2.3 Morocco
      • 12.5.2.4 Rest of Africa

13 Strategic Market Intelligence

  • 13.1 Industry Value Network and Supply Chain Assessment
  • 13.2 White-Space and Opportunity Mapping
  • 13.3 Product Evolution and Market Life Cycle Analysis
  • 13.4 Channel, Distributor, and Go-to-Market Assessment

14 Industry Developments and Strategic Initiatives

  • 14.1 Mergers and Acquisitions
  • 14.2 Partnerships, Alliances, and Joint Ventures
  • 14.3 New Product Launches and Certifications
  • 14.4 Capacity Expansion and Investments
  • 14.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 SAP SE
  • 14.2 Oracle Corporation
  • 14.3 Blue Yonder Group, Inc.
  • 14.4 Kinaxis Inc.
  • 14.5 o9 Solutions, Inc.
  • 14.6 Manhattan Associates, Inc.
  • 14.7 Infor Inc.
  • 14.8 IBM Corporation
  • 14.9 Microsoft Corporation
  • 14.10 Google LLC
  • 14.11 Amazon Web Services, Inc.
  • 14.12 Accenture plc
  • 14.13 Infosys Limited
  • 14.14 Wipro Limited
  • 14.15 Schneider Electric SE
  • 14.16 Siemens AG
  • 14.17 Coupa Software Inc.

List of Tables

  • Table 1 Global AI-Based Supply Chain Orchestration Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Based Supply Chain Orchestration Market Outlook, By Deployment (2023-2034) ($MN)
  • Table 3 Global AI-Based Supply Chain Orchestration Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 4 Global AI-Based Supply Chain Orchestration Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 5 Global AI-Based Supply Chain Orchestration Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 6 Global AI-Based Supply Chain Orchestration Market Outlook, By Offering (2023-2034) ($MN)
  • Table 7 Global AI-Based Supply Chain Orchestration Market Outlook, By Software (2023-2034) ($MN)
  • Table 8 Global AI-Based Supply Chain Orchestration Market Outlook, By Services (2023-2034) ($MN)
  • Table 9 Global AI-Based Supply Chain Orchestration Market Outlook, By Service Type (2023-2034) ($MN)
  • Table 10 Global AI-Based Supply Chain Orchestration Market Outlook, By Consulting (2023-2034) ($MN)
  • Table 11 Global AI-Based Supply Chain Orchestration Market Outlook, By Implementation (2023-2034) ($MN)
  • Table 12 Global AI-Based Supply Chain Orchestration Market Outlook, By Support and Maintenance (2023-2034) ($MN)
  • Table 13 Global AI-Based Supply Chain Orchestration Market Outlook, By Managed Services (2023-2034) ($MN)
  • Table 14 Global AI-Based Supply Chain Orchestration Market Outlook, By AI Technology (2023-2034) ($MN)
  • Table 15 Global AI-Based Supply Chain Orchestration Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 16 Global AI-Based Supply Chain Orchestration Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 17 Global AI-Based Supply Chain Orchestration Market Outlook, By Natural Language Processing (2023-2034) ($MN)
  • Table 18 Global AI-Based Supply Chain Orchestration Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 19 Global AI-Based Supply Chain Orchestration Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 20 Global AI-Based Supply Chain Orchestration Market Outlook, By Function (2023-2034) ($MN)
  • Table 21 Global AI-Based Supply Chain Orchestration Market Outlook, By Demand Planning (2023-2034) ($MN)
  • Table 22 Global AI-Based Supply Chain Orchestration Market Outlook, By Inventory Optimization (2023-2034) ($MN)
  • Table 23 Global AI-Based Supply Chain Orchestration Market Outlook, By Logistics Orchestration (2023-2034) ($MN)
  • Table 24 Global AI-Based Supply Chain Orchestration Market Outlook, By Supplier Collaboration (2023-2034) ($MN)
  • Table 25 Global AI-Based Supply Chain Orchestration Market Outlook, By Order Management (2023-2034) ($MN)
  • Table 26 Global AI-Based Supply Chain Orchestration Market Outlook, By Procurement Optimization (2023-2034) ($MN)
  • Table 27 Global AI-Based Supply Chain Orchestration Market Outlook, By Application (2023-2034) ($MN)
  • Table 28 Global AI-Based Supply Chain Orchestration Market Outlook, By Supply Planning (2023-2034) ($MN)
  • Table 29 Global AI-Based Supply Chain Orchestration Market Outlook, By Transportation Management (2023-2034) ($MN)
  • Table 30 Global AI-Based Supply Chain Orchestration Market Outlook, By Warehouse Optimization (2023-2034) ($MN)
  • Table 31 Global AI-Based Supply Chain Orchestration Market Outlook, By Production Planning (2023-2034) ($MN)
  • Table 32 Global AI-Based Supply Chain Orchestration Market Outlook, By Risk Management (2023-2034) ($MN)
  • Table 33 Global AI-Based Supply Chain Orchestration Market Outlook, By Last-Mile Delivery (2023-2034) ($MN)
  • Table 34 Global AI-Based Supply Chain Orchestration Market Outlook, By End User (2023-2034) ($MN)
  • Table 35 Global AI-Based Supply Chain Orchestration Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 36 Global AI-Based Supply Chain Orchestration Market Outlook, By Retail (2023-2034) ($MN)
  • Table 37 Global AI-Based Supply Chain Orchestration Market Outlook, By Healthcare (2023-2034) ($MN)
  • Table 38 Global AI-Based Supply Chain Orchestration Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 39 Global AI-Based Supply Chain Orchestration Market Outlook, By Food and Beverage (2023-2034) ($MN)
  • Table 40 Global AI-Based Supply Chain Orchestration Market Outlook, By Consumer Goods (2023-2034) ($MN)
  • Table 41 Global AI-Based Supply Chain Orchestration Market Outlook, By Logistics (2023-2034) ($MN)

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