AI in Supply Chain Market Forecasts to 2032 – Global Analysis By Offering (Hardware, Software and Services), Technology, Application, End User and By Geography
Description
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 AmericaUSCanadaMexico
• EuropeGermanyUKItalyFranceSpainRest of Europe
• Asia PacificJapan China India Australia New ZealandSouth KoreaRest of Asia Pacific
• South AmericaArgentinaBrazilChileRest of South America
• Middle East & Africa Saudi ArabiaUAEQatarSouth AfricaRest 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
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 AmericaUSCanadaMexico
• EuropeGermanyUKItalyFranceSpainRest of Europe
• Asia PacificJapan China India Australia New ZealandSouth KoreaRest of Asia Pacific
• South AmericaArgentinaBrazilChileRest of South America
• Middle East & Africa Saudi ArabiaUAEQatarSouth AfricaRest 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
Table of Contents
200 Pages
- 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.
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