Artificial Intelligence in Supply Chain Market by Component (Services, Solutions), Technology Type (Deep Learning, Natural Language Processing, Robotics Process Automation), Deployment Mode, Organization Size, Application, End-User - Global Forecast 2025-
Description
The Artificial Intelligence in Supply Chain Market was valued at USD 6.68 billion in 2024 and is projected to grow to USD 8.01 billion in 2025, with a CAGR of 20.98%, reaching USD 30.68 billion by 2032.
Unveiling How Artificial Intelligence is Reshaping Supply Chains with Enhanced Visibility Efficiency and Resilience Across Global Industries
In today’s global marketplace, supply chains have grown in complexity and scale, challenging traditional management approaches. Organizations face unpredictable demand patterns, multi-tier supplier networks, and heightened customer expectations for speed and transparency. Against this backdrop, artificial intelligence emerges as a powerful catalyst for transformation, harnessing advanced algorithms and real-time data to elevate visibility, optimize workflows, and anticipate disruptions before they ripple through the end-to-end process.
This executive summary distills the critical insights from an extensive research initiative exploring the integration of AI within supply chain functions. It presents a cohesive narrative on how AI technologies are reshaping operational paradigms, driving strategic reinvention, and supporting resilience in the face of geopolitical and economic headwinds.
Structured to guide senior executives and decision-makers, the analysis unfolds across several domains. It begins with an overview of the most significant landscape shifts, examines the impact of new regulatory measures, unpacks segmentation nuances, and highlights regional dynamics. Further, it showcases leading companies’ strategies, offers actionable recommendations for industry leaders, outlines the research methodology underpinning these findings, and concludes with a forward-looking perspective on AI-driven supply chain excellence.
Examining Pivotal Technological Evolution and Operational Paradigm Shifts Driven by AI Innovations That Are Transforming Supply Chain Ecosystems Worldwide
In recent years, supply chains have undergone a profound metamorphosis as digitalization and automation converged with advanced analytics. Cutting-edge machine learning models now power demand forecasting engines, while computer vision and robotics streamline warehouse operations with unprecedented precision. Consequently, organizations are transitioning from reactive fire-fighting toward proactive orchestration, leveraging AI-enabled insights to anticipate disruptions and reallocate resources dynamically.
Simultaneously, the proliferation of edge computing and IoT sensors has created a continuous feedback loop, enabling adaptive supply chain architectures that can recalibrate in real time. This shift toward autonomous decision making is complemented by the rise of cognitive platforms, which integrate natural language processing to facilitate seamless human-machine collaboration.
Together, these developments constitute a paradigm shift in how supply chain stakeholders plan, execute, and monitor flow. Rather than viewing AI as a mere enhancement, leading enterprises embrace it as a strategic imperative, embedding intelligence at every node to foster agility, reduce waste, and unlock new revenue opportunities.
Analyzing the Comprehensive Repercussions of Newly Implemented United States Tariffs in 2025 on AI Driven Supply Chain Agility and Cost Structures
The introduction of new United States tariffs in 2025 has intensified cost pressures across global trade lanes, compelling companies to reassess sourcing strategies and routing protocols. In this climate, AI-driven automation and analytics have become critical levers for offsetting tariff-induced margin compression. By deploying advanced cost-to-serve models, organizations can dynamically evaluate supplier options and transit routes, balancing landed cost with service level requirements.
Moreover, the surge in duties on key components and raw materials has accelerated nearshoring and onshoring trends, prompting firms to redesign network footprints for greater proximity to end markets. AI-powered scenario planning tools allow planners to model tariff scenarios, simulate cross-border workflows, and identify resilient configurations that minimize exposure to sudden policy shifts.
Ultimately, the cumulative effect of these tariffs underscores the necessity of embedding robust risk management frameworks underpinned by real-time intelligence. Companies that adopt AI-infused visibility platforms and predictive analytics will be best positioned to navigate the evolving tariff landscape while safeguarding service continuity and fiscal health.
Elucidating Strategic Segmentation Perspectives on Key Components Technologies Deployment Modes Organization Sizes Applications and End User Verticals
Component portfolios now bifurcate into specialized services and integrated solutions. Consulting services guide strategic roadmaps for AI adoption, while deployment and integration teams embed machine learning engines into legacy infrastructures. Support and maintenance functions ensure continuous optimization, leveraging feedback loops and performance monitoring to refine algorithms over time.
AI technologies themselves span deep learning for advanced pattern recognition, natural language processing to interpret unstructured data from documents and communications, and robotic process automation that accelerates transactional tasks and system interactions without human intervention.
Deployment modalities are equally diverse, with cloud-based architectures offering the flexibility of hybrid, private, or public cloud environments to match data sovereignty and performance requirements. In contexts demanding utmost control and latency guarantees, on-premise infrastructures-whether housed in dedicated data centers or local server configurations-remain prevalent.
Organizational scale further influences adoption profiles. Large enterprises tend to prioritize enterprise-grade AI suites that integrate across global operations, while small and medium enterprises favor modular, preconfigured offerings that deliver rapid time to value with minimal customization overhead.
Applications cut across the supply chain spectrum. Within fleet management, predictive maintenance models and route optimization engines work in tandem to reduce downtime and fuel consumption. Freight brokerage platforms use AI-powered matchmaking algorithms to streamline load planning, while risk management systems leverage compliance monitoring and supplier risk assessment modules to flag vulnerabilities. Supply chain planning benefits from demand forecasting and procurement optimization, feeding accurate projections into inventory policies. In warehouse management, inventory control algorithms and order fulfillment orchestrators drive throughput and accuracy to new heights.
End-users across aerospace and defense, automotive, chemicals, food and beverages, healthcare and pharmaceuticals, manufacturing, retail and e-commerce, and transportation and logistics sectors are investing in tailored AI capabilities to address unique complexity and regulatory demands within their operational ecosystems.
Exploring Regional Differentiators and AI Adoption Trajectories in the Americas Europe Middle East Africa and Asia Pacific Supply Chain Markets
The Americas lead in AI investment across supply chain functions, propelled by mature logistics infrastructures and a multitude of early use cases. Industry leaders in North America are piloting autonomous vehicles and smart warehouses, while Latin American players focus on improving cold-chain visibility through sensor networks and predictive alerts.
In Europe, Middle East and Africa, regulatory frameworks and a patchwork of national standards present both challenges and incentives for AI adoption. European Union initiatives around digital supply chains and sustainability reporting have accelerated deployment of AI-driven analytics, particularly in automotive and manufacturing hubs. Concurrently, emerging markets in the Middle East and Africa are embracing cloud-based solutions to leapfrog traditional infrastructure constraints.
Asia-Pacific remains the fastest-growing region for AI in supply chain, with leading economies such as China, Japan and South Korea investing heavily in robotics, digital twins and real-time control towers. The region’s manufacturing clusters are integrating AI into production planning and quality assurance, while Southeast Asian nations leverage mobile-first platforms for last-mile visibility and demand sensing in highly fragmented retail landscapes.
Highlighting Strategies Research Investments and Collaborative Innovations that Propel Leading Companies to the Forefront of AI Enabled Supply Chain Excellence
A cohort of leading technology providers has distinguished itself through strategic investments in research and development, high-profile acquisitions, and robust partner ecosystems. Several established enterprise resource planning vendors have acquired niche AI startups to embed predictive analytics directly into core modules, while cloud hyperscalers continue to forge alliances with logistics specialists to offer end-to-end intelligent supply chain suites.
Noteworthy companies have also expanded their footprints through industry consortia, co-innovating with key end-users to pilot next-generation applications such as autonomous port operations and AI-driven sustainability scorecards. In parallel, rising players are carving out specialized niches by focusing on open-source machine learning frameworks and no-code automation, making advanced AI capabilities accessible to small and medium enterprises.
These strategic approaches underscore the importance of collaborative innovation and rapid experimentation. By cultivating developer communities, sponsoring hackathons, and investing in scalable cloud infrastructures, leading companies are accelerating the time from proof of concept to wide-scale deployment, setting new benchmarks for integration, performance, and customer value.
Outlining Pragmatic Recommendations to Empower Industry Leaders in Driving AI Powered Supply Chain Innovation Enhanced Operational Efficiency and Growth
Industry leaders should prioritize the development of a unified data architecture that centralizes information from ERP systems, IoT sensors, and external market feeds. By establishing data governance protocols and talent capabilities around data science, organizations can unlock cohesive AI models that draw from consistent, high-quality inputs.
Next, adopting modular and interoperable AI solutions will enable rapid scaling. Organizations can begin with focused pilots-such as predictive maintenance in a single distribution center-and then expand to additional nodes without extensive reengineering or prolonged customization cycles.
Furthermore, fostering cross-functional collaboration between supply chain, IT, and analytics teams is essential. Embedding AI champions within operational units ensures that algorithmic insights are grounded in domain expertise and that end-users are primed to embrace new workflows.
Finally, forging strategic partnerships with technology vendors, academic institutions and industry consortia will accelerate access to emerging innovations. Co-investing in joint labs and proof-of-value engagements can de-risk adoption pathways and cultivate a culture of continuous learning and process improvement.
Demonstrating a Rigorous Mixed Methodology Blending Qualitative Expert Interviews Quantitative Data Analysis and Ecosystem Mapping for Validating Insights
The research methodology combined qualitative and quantitative approaches to produce balanced, actionable insights. In the qualitative phase, in-depth interviews were conducted with senior executives, technology architects and operational leaders across diverse industries to capture real-world use cases, deployment challenges and success factors.
Complementing this expertise, quantitative analysis of industry surveys, public financial statements and procurement data was performed to identify adoption trends, technology maturity curves and investment priorities. Statistical techniques were applied to validate the correlation between AI initiatives and operational performance metrics.
To contextualize these findings within the broader technology landscape, a detailed ecosystem mapping exercise charted solution providers, academic research labs, and standard bodies. This mapping revealed key innovation clusters, partnership networks and emerging disruptors, ensuring that the final report reflects a holistic view of the market trajectory and competitive dynamics.
Summarizing Core Findings and Strategic Conclusions on AI Integration Supply Chain Resilience and Competitive Advantage for Informed Decision Making
The synthesis of research findings confirms that artificial intelligence is no longer a peripheral enhancement but a foundational element of modern supply chain strategy. From predictive forecasting and intelligent routing to automated compliance monitoring and dynamic procurement, AI technologies are delivering measurable improvements in resilience, efficiency and customer satisfaction.
As organizations navigate a landscape marked by trade policy shifts, geopolitical uncertainty and accelerating digital transformation, the ability to orchestrate complex networks with minimal latency becomes a decisive competitive advantage. The integration of AI into core processes fosters agility by translating data into foresight, enabling rapid adaptation to emerging opportunities and disruptions.
In conclusion, the convergence of robust data infrastructures, modular AI offerings, and collaborative innovation models will define the next frontier of supply chain excellence. Companies that embrace this evolution with strategic clarity and operational rigor will secure sustainable growth and long-term market leadership.
Please Note: PDF & Excel + Online Access - 1 Year
Unveiling How Artificial Intelligence is Reshaping Supply Chains with Enhanced Visibility Efficiency and Resilience Across Global Industries
In today’s global marketplace, supply chains have grown in complexity and scale, challenging traditional management approaches. Organizations face unpredictable demand patterns, multi-tier supplier networks, and heightened customer expectations for speed and transparency. Against this backdrop, artificial intelligence emerges as a powerful catalyst for transformation, harnessing advanced algorithms and real-time data to elevate visibility, optimize workflows, and anticipate disruptions before they ripple through the end-to-end process.
This executive summary distills the critical insights from an extensive research initiative exploring the integration of AI within supply chain functions. It presents a cohesive narrative on how AI technologies are reshaping operational paradigms, driving strategic reinvention, and supporting resilience in the face of geopolitical and economic headwinds.
Structured to guide senior executives and decision-makers, the analysis unfolds across several domains. It begins with an overview of the most significant landscape shifts, examines the impact of new regulatory measures, unpacks segmentation nuances, and highlights regional dynamics. Further, it showcases leading companies’ strategies, offers actionable recommendations for industry leaders, outlines the research methodology underpinning these findings, and concludes with a forward-looking perspective on AI-driven supply chain excellence.
Examining Pivotal Technological Evolution and Operational Paradigm Shifts Driven by AI Innovations That Are Transforming Supply Chain Ecosystems Worldwide
In recent years, supply chains have undergone a profound metamorphosis as digitalization and automation converged with advanced analytics. Cutting-edge machine learning models now power demand forecasting engines, while computer vision and robotics streamline warehouse operations with unprecedented precision. Consequently, organizations are transitioning from reactive fire-fighting toward proactive orchestration, leveraging AI-enabled insights to anticipate disruptions and reallocate resources dynamically.
Simultaneously, the proliferation of edge computing and IoT sensors has created a continuous feedback loop, enabling adaptive supply chain architectures that can recalibrate in real time. This shift toward autonomous decision making is complemented by the rise of cognitive platforms, which integrate natural language processing to facilitate seamless human-machine collaboration.
Together, these developments constitute a paradigm shift in how supply chain stakeholders plan, execute, and monitor flow. Rather than viewing AI as a mere enhancement, leading enterprises embrace it as a strategic imperative, embedding intelligence at every node to foster agility, reduce waste, and unlock new revenue opportunities.
Analyzing the Comprehensive Repercussions of Newly Implemented United States Tariffs in 2025 on AI Driven Supply Chain Agility and Cost Structures
The introduction of new United States tariffs in 2025 has intensified cost pressures across global trade lanes, compelling companies to reassess sourcing strategies and routing protocols. In this climate, AI-driven automation and analytics have become critical levers for offsetting tariff-induced margin compression. By deploying advanced cost-to-serve models, organizations can dynamically evaluate supplier options and transit routes, balancing landed cost with service level requirements.
Moreover, the surge in duties on key components and raw materials has accelerated nearshoring and onshoring trends, prompting firms to redesign network footprints for greater proximity to end markets. AI-powered scenario planning tools allow planners to model tariff scenarios, simulate cross-border workflows, and identify resilient configurations that minimize exposure to sudden policy shifts.
Ultimately, the cumulative effect of these tariffs underscores the necessity of embedding robust risk management frameworks underpinned by real-time intelligence. Companies that adopt AI-infused visibility platforms and predictive analytics will be best positioned to navigate the evolving tariff landscape while safeguarding service continuity and fiscal health.
Elucidating Strategic Segmentation Perspectives on Key Components Technologies Deployment Modes Organization Sizes Applications and End User Verticals
Component portfolios now bifurcate into specialized services and integrated solutions. Consulting services guide strategic roadmaps for AI adoption, while deployment and integration teams embed machine learning engines into legacy infrastructures. Support and maintenance functions ensure continuous optimization, leveraging feedback loops and performance monitoring to refine algorithms over time.
AI technologies themselves span deep learning for advanced pattern recognition, natural language processing to interpret unstructured data from documents and communications, and robotic process automation that accelerates transactional tasks and system interactions without human intervention.
Deployment modalities are equally diverse, with cloud-based architectures offering the flexibility of hybrid, private, or public cloud environments to match data sovereignty and performance requirements. In contexts demanding utmost control and latency guarantees, on-premise infrastructures-whether housed in dedicated data centers or local server configurations-remain prevalent.
Organizational scale further influences adoption profiles. Large enterprises tend to prioritize enterprise-grade AI suites that integrate across global operations, while small and medium enterprises favor modular, preconfigured offerings that deliver rapid time to value with minimal customization overhead.
Applications cut across the supply chain spectrum. Within fleet management, predictive maintenance models and route optimization engines work in tandem to reduce downtime and fuel consumption. Freight brokerage platforms use AI-powered matchmaking algorithms to streamline load planning, while risk management systems leverage compliance monitoring and supplier risk assessment modules to flag vulnerabilities. Supply chain planning benefits from demand forecasting and procurement optimization, feeding accurate projections into inventory policies. In warehouse management, inventory control algorithms and order fulfillment orchestrators drive throughput and accuracy to new heights.
End-users across aerospace and defense, automotive, chemicals, food and beverages, healthcare and pharmaceuticals, manufacturing, retail and e-commerce, and transportation and logistics sectors are investing in tailored AI capabilities to address unique complexity and regulatory demands within their operational ecosystems.
Exploring Regional Differentiators and AI Adoption Trajectories in the Americas Europe Middle East Africa and Asia Pacific Supply Chain Markets
The Americas lead in AI investment across supply chain functions, propelled by mature logistics infrastructures and a multitude of early use cases. Industry leaders in North America are piloting autonomous vehicles and smart warehouses, while Latin American players focus on improving cold-chain visibility through sensor networks and predictive alerts.
In Europe, Middle East and Africa, regulatory frameworks and a patchwork of national standards present both challenges and incentives for AI adoption. European Union initiatives around digital supply chains and sustainability reporting have accelerated deployment of AI-driven analytics, particularly in automotive and manufacturing hubs. Concurrently, emerging markets in the Middle East and Africa are embracing cloud-based solutions to leapfrog traditional infrastructure constraints.
Asia-Pacific remains the fastest-growing region for AI in supply chain, with leading economies such as China, Japan and South Korea investing heavily in robotics, digital twins and real-time control towers. The region’s manufacturing clusters are integrating AI into production planning and quality assurance, while Southeast Asian nations leverage mobile-first platforms for last-mile visibility and demand sensing in highly fragmented retail landscapes.
Highlighting Strategies Research Investments and Collaborative Innovations that Propel Leading Companies to the Forefront of AI Enabled Supply Chain Excellence
A cohort of leading technology providers has distinguished itself through strategic investments in research and development, high-profile acquisitions, and robust partner ecosystems. Several established enterprise resource planning vendors have acquired niche AI startups to embed predictive analytics directly into core modules, while cloud hyperscalers continue to forge alliances with logistics specialists to offer end-to-end intelligent supply chain suites.
Noteworthy companies have also expanded their footprints through industry consortia, co-innovating with key end-users to pilot next-generation applications such as autonomous port operations and AI-driven sustainability scorecards. In parallel, rising players are carving out specialized niches by focusing on open-source machine learning frameworks and no-code automation, making advanced AI capabilities accessible to small and medium enterprises.
These strategic approaches underscore the importance of collaborative innovation and rapid experimentation. By cultivating developer communities, sponsoring hackathons, and investing in scalable cloud infrastructures, leading companies are accelerating the time from proof of concept to wide-scale deployment, setting new benchmarks for integration, performance, and customer value.
Outlining Pragmatic Recommendations to Empower Industry Leaders in Driving AI Powered Supply Chain Innovation Enhanced Operational Efficiency and Growth
Industry leaders should prioritize the development of a unified data architecture that centralizes information from ERP systems, IoT sensors, and external market feeds. By establishing data governance protocols and talent capabilities around data science, organizations can unlock cohesive AI models that draw from consistent, high-quality inputs.
Next, adopting modular and interoperable AI solutions will enable rapid scaling. Organizations can begin with focused pilots-such as predictive maintenance in a single distribution center-and then expand to additional nodes without extensive reengineering or prolonged customization cycles.
Furthermore, fostering cross-functional collaboration between supply chain, IT, and analytics teams is essential. Embedding AI champions within operational units ensures that algorithmic insights are grounded in domain expertise and that end-users are primed to embrace new workflows.
Finally, forging strategic partnerships with technology vendors, academic institutions and industry consortia will accelerate access to emerging innovations. Co-investing in joint labs and proof-of-value engagements can de-risk adoption pathways and cultivate a culture of continuous learning and process improvement.
Demonstrating a Rigorous Mixed Methodology Blending Qualitative Expert Interviews Quantitative Data Analysis and Ecosystem Mapping for Validating Insights
The research methodology combined qualitative and quantitative approaches to produce balanced, actionable insights. In the qualitative phase, in-depth interviews were conducted with senior executives, technology architects and operational leaders across diverse industries to capture real-world use cases, deployment challenges and success factors.
Complementing this expertise, quantitative analysis of industry surveys, public financial statements and procurement data was performed to identify adoption trends, technology maturity curves and investment priorities. Statistical techniques were applied to validate the correlation between AI initiatives and operational performance metrics.
To contextualize these findings within the broader technology landscape, a detailed ecosystem mapping exercise charted solution providers, academic research labs, and standard bodies. This mapping revealed key innovation clusters, partnership networks and emerging disruptors, ensuring that the final report reflects a holistic view of the market trajectory and competitive dynamics.
Summarizing Core Findings and Strategic Conclusions on AI Integration Supply Chain Resilience and Competitive Advantage for Informed Decision Making
The synthesis of research findings confirms that artificial intelligence is no longer a peripheral enhancement but a foundational element of modern supply chain strategy. From predictive forecasting and intelligent routing to automated compliance monitoring and dynamic procurement, AI technologies are delivering measurable improvements in resilience, efficiency and customer satisfaction.
As organizations navigate a landscape marked by trade policy shifts, geopolitical uncertainty and accelerating digital transformation, the ability to orchestrate complex networks with minimal latency becomes a decisive competitive advantage. The integration of AI into core processes fosters agility by translating data into foresight, enabling rapid adaptation to emerging opportunities and disruptions.
In conclusion, the convergence of robust data infrastructures, modular AI offerings, and collaborative innovation models will define the next frontier of supply chain excellence. Companies that embrace this evolution with strategic clarity and operational rigor will secure sustainable growth and long-term market leadership.
Please Note: PDF & Excel + Online Access - 1 Year
Table of Contents
185 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Advancement in machine learning algorithms enhacing supplier selection and performance evaluation processes
- 5.2. Increasing impact of AI on optimizing last-mile delivery and logistics in supply chain operations
- 5.3. Advancements in natural language processing enabling smarter supplier communication and management
- 5.4. Development of AI-enabled digital twins to simulate and optimize complex supply chain networks
- 5.5. Advanced predictive analytics powered by AI optimizing demand forecasting and inventory management
- 5.6. Implementing AI-enabled blockchain for transparent and secure supply chain transactions
- 5.7. Developing AI-supported sustainability initiatives for greener and more ethical supply chains
- 5.8. Utilizing AI for improved quality control and defect detection in manufacturing supply chains
- 5.9. AI integration in real-time supply chain monitoring for enhanced risk management and disruption response
- 5.10. Adoption of AI-driven automation in warehouse management and asset tracking
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Artificial Intelligence in Supply Chain Market, by Component
- 8.1. Services
- 8.1.1. Consulting Services
- 8.1.2. Deployment & Integration
- 8.1.3. Support & Maintenance
- 8.2. Solutions
- 9. Artificial Intelligence in Supply Chain Market, by Technology Type
- 9.1. Deep Learning
- 9.2. Natural Language Processing
- 9.3. Robotics Process Automation
- 10. Artificial Intelligence in Supply Chain Market, by Deployment Mode
- 10.1. Cloud-Based
- 10.1.1. Hybrid Cloud
- 10.1.2. Private Cloud
- 10.1.3. Public Cloud
- 10.2. On-Premise
- 10.2.1. Data Centers
- 10.2.2. Local Servers
- 11. Artificial Intelligence in Supply Chain Market, by Organization Size
- 11.1. Large Enterprises
- 11.2. Small & Medium Enterprises
- 12. Artificial Intelligence in Supply Chain Market, by Application
- 12.1. Fleet Management
- 12.1.1. Predictive Maintenance
- 12.1.2. Route Optimization
- 12.2. Freight Brokerage
- 12.3. Risk Management
- 12.3.1. Compliance Monitoring
- 12.3.2. Supplier Risk Assessment
- 12.4. Supply Chain Planning
- 12.4.1. Demand Forecasting
- 12.4.2. Procurement Optimization
- 12.5. Warehouse Management
- 12.5.1. Inventory Management
- 12.5.2. Order Fulfillment
- 13. Artificial Intelligence in Supply Chain Market, by End-User
- 13.1. Aerospace & Defense
- 13.2. Automotive
- 13.3. Chemicals
- 13.4. Food & Beverages
- 13.5. Healthcare and Pharmaceuticals
- 13.6. Manufacturing
- 13.7. Retail & E-Commerce
- 13.8. Transportation & Logistics
- 14. Artificial Intelligence in Supply Chain Market, by Region
- 14.1. Americas
- 14.1.1. North America
- 14.1.2. Latin America
- 14.2. Europe, Middle East & Africa
- 14.2.1. Europe
- 14.2.2. Middle East
- 14.2.3. Africa
- 14.3. Asia-Pacific
- 15. Artificial Intelligence in Supply Chain Market, by Group
- 15.1. ASEAN
- 15.2. GCC
- 15.3. European Union
- 15.4. BRICS
- 15.5. G7
- 15.6. NATO
- 16. Artificial Intelligence in Supply Chain Market, by Country
- 16.1. United States
- 16.2. Canada
- 16.3. Mexico
- 16.4. Brazil
- 16.5. United Kingdom
- 16.6. Germany
- 16.7. France
- 16.8. Russia
- 16.9. Italy
- 16.10. Spain
- 16.11. China
- 16.12. India
- 16.13. Japan
- 16.14. Australia
- 16.15. South Korea
- 17. Competitive Landscape
- 17.1. Market Share Analysis, 2024
- 17.2. FPNV Positioning Matrix, 2024
- 17.3. Competitive Analysis
- 17.3.1. Accenture PLC
- 17.3.2. Alibaba Group
- 17.3.3. Amazon Web Services, Inc.
- 17.3.4. Berkshire Grey, Inc.
- 17.3.5. Blue Yonder Group, Inc.
- 17.3.6. Capgemini SE
- 17.3.7. Cisco Systems, Inc.
- 17.3.8. Coupa Software Inc.
- 17.3.9. Deutsche Post AG
- 17.3.10. e2open, LLC
- 17.3.11. Genpact Limited
- 17.3.12. Google LLC by Alphabet Inc.
- 17.3.13. Infosys Limited
- 17.3.14. Intel Corporation
- 17.3.15. International Business Machines Corporation
- 17.3.16. Kinaxis Inc.
- 17.3.17. Logility Supply Chain Solutions, Inc.
- 17.3.18. Manhattan Associates, Inc.
- 17.3.19. Micron Technology, Inc.
- 17.3.20. Microsoft Corporation
- 17.3.21. NVIDIA Corporation
- 17.3.22. Oracle Corporation
- 17.3.23. RELEX Oy
- 17.3.24. Robert Bosch GmbH
- 17.3.25. Salesforce, Inc.
- 17.3.26. SAP SE
- 17.3.27. Siemens AG
- 17.3.28. Tata Consultancy Services Limited
- 17.3.29. UiPath, Inc.
- 17.3.30. Workday, Inc.
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