2026 Global: Artificial Intelligence (Ai) In Supply Chain Market-Competitive Review (2032) report
Description
The 2026 Global: Artificial Intelligence (Ai) In Supply Chain Market-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in supply chain market by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in supply chain market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
IBM, Microsoft, Amazon Web Services (AWS), SAP, Oracle, Blue Yonder, Kinaxis, Manhattan Associates, C3.ai, and NVIDIA are among the ten major companies shaping the Artificial Intelligence (AI) in Supply Chain market through integrated platforms, cloud services, and specialized AI applications. IBM applies its Watson AI and hybrid cloud to drive predictive analytics, intelligent automation, and real-time visibility across demand forecasting, procurement, and incident response, positioning itself as an enterprise AI integrator for complex supply networks. Microsoft leverages Azure, Dynamics 365 and a broad partner ecosystem to embed machine learning, digital twins, and AI agents into planning, inventory optimization, and logistics orchestration, enabling scalable, secure deployments for large enterprises. AWS offers a portfolio of cloud-native ML services and dedicated supply-chain capabilities—such as AWS Supply Chain and Amazon Q—that provide end-to-end visibility, scenario simulation, and generative-AI assistants to summarise risks and trade-offs for planners and operations teams. SAP combines its Integrated Business Planning (IBP) and Digital Supply Chain suite with AI-driven demand and supply planning, adaptive logistics and embedded analytics to help enterprises automate complex processes and maintain continuity under disruption. Oracle delivers AI-enabled features within Oracle Fusion Cloud Supply Chain & Manufacturing to automate forecasting, inventory management and risk mitigation while integrating procurement and logistics into a single enterprise platform.
Blue Yonder (now closely partnered with Microsoft) focuses on AI-driven retail and logistics solutions—cognitive planning, demand forecasting and warehouse automation—offering deep industry-tailored algorithms for inventory and fulfillment optimization. Kinaxis provides the Maestro concurrent planning platform that uses AI and scenario modeling to accelerate decision-making, enabling rapid response to supply shocks and collaborative cross-functional planning for manufacturers and distributors. Manhattan Associates specializes in AI-enhanced warehouse management, transportation and omnichannel fulfillment software that embeds optimization engines and machine learning to increase throughput, reduce labor friction and improve service levels in distribution networks. C3.ai offers an enterprise-grade AI platform and a Supply Chain Suite focused on predictive applications—sourcing optimization, granular demand forecasting, production scheduling and inventory optimization—that aim to convert data into proactive decisions across sourcing, production and logistics. NVIDIA, while known for hardware, is a foundational player enabling supply-chain AI by supplying GPUs, accelerated computing platforms and software stacks used for large-scale machine learning, computer vision for robotics and simulation for logistics planning, thereby underpinning the performance of many AI supply-chain solutions.
Collectively these ten companies span cloud infrastructure, enterprise applications, specialized AI suites and enabling hardware; their offerings overlap in demand forecasting, inventory optimization, warehouse automation, transportation management and risk analytics while differing in industry focus, deployment model and level of AI integration, shaping how organizations adopt AI to increase resilience, reduce costs and accelerate decision velocity across global supply networks.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in supply chain market by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in supply chain market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
IBM, Microsoft, Amazon Web Services (AWS), SAP, Oracle, Blue Yonder, Kinaxis, Manhattan Associates, C3.ai, and NVIDIA are among the ten major companies shaping the Artificial Intelligence (AI) in Supply Chain market through integrated platforms, cloud services, and specialized AI applications. IBM applies its Watson AI and hybrid cloud to drive predictive analytics, intelligent automation, and real-time visibility across demand forecasting, procurement, and incident response, positioning itself as an enterprise AI integrator for complex supply networks. Microsoft leverages Azure, Dynamics 365 and a broad partner ecosystem to embed machine learning, digital twins, and AI agents into planning, inventory optimization, and logistics orchestration, enabling scalable, secure deployments for large enterprises. AWS offers a portfolio of cloud-native ML services and dedicated supply-chain capabilities—such as AWS Supply Chain and Amazon Q—that provide end-to-end visibility, scenario simulation, and generative-AI assistants to summarise risks and trade-offs for planners and operations teams. SAP combines its Integrated Business Planning (IBP) and Digital Supply Chain suite with AI-driven demand and supply planning, adaptive logistics and embedded analytics to help enterprises automate complex processes and maintain continuity under disruption. Oracle delivers AI-enabled features within Oracle Fusion Cloud Supply Chain & Manufacturing to automate forecasting, inventory management and risk mitigation while integrating procurement and logistics into a single enterprise platform.
Blue Yonder (now closely partnered with Microsoft) focuses on AI-driven retail and logistics solutions—cognitive planning, demand forecasting and warehouse automation—offering deep industry-tailored algorithms for inventory and fulfillment optimization. Kinaxis provides the Maestro concurrent planning platform that uses AI and scenario modeling to accelerate decision-making, enabling rapid response to supply shocks and collaborative cross-functional planning for manufacturers and distributors. Manhattan Associates specializes in AI-enhanced warehouse management, transportation and omnichannel fulfillment software that embeds optimization engines and machine learning to increase throughput, reduce labor friction and improve service levels in distribution networks. C3.ai offers an enterprise-grade AI platform and a Supply Chain Suite focused on predictive applications—sourcing optimization, granular demand forecasting, production scheduling and inventory optimization—that aim to convert data into proactive decisions across sourcing, production and logistics. NVIDIA, while known for hardware, is a foundational player enabling supply-chain AI by supplying GPUs, accelerated computing platforms and software stacks used for large-scale machine learning, computer vision for robotics and simulation for logistics planning, thereby underpinning the performance of many AI supply-chain solutions.
Collectively these ten companies span cloud infrastructure, enterprise applications, specialized AI suites and enabling hardware; their offerings overlap in demand forecasting, inventory optimization, warehouse automation, transportation management and risk analytics while differing in industry focus, deployment model and level of AI integration, shaping how organizations adopt AI to increase resilience, reduce costs and accelerate decision velocity across global supply networks.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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