Global AI-based Raw Material Sourcing Optimization Market 2025 by Company, Regions, Type and Application, Forecast to 2031
Description
According to our latest research, the global AI-based Raw Material Sourcing Optimization market size will reach USD 3403 million in 2031, growing at a CAGR of 9.7% over the analysis period.
Artificial intelligence (AI)-based raw material procurement optimization is defined as the use of AI technology and algorithms to achieve intelligent decision-making and optimization in the raw material procurement process by analyzing historical data, market demand, supplier information, inventory levels and other multi-dimensional factors. This optimization aims to improve procurement efficiency, reduce costs, reduce inventory risks, and enhance the transparency and collaboration of the supply chain.
This report is a detailed and comprehensive analysis for global AI-based Raw Material Sourcing Optimization market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
Key Features:
Global AI-based Raw Material Sourcing Optimization market size and forecasts, in consumption value ($ Million), 2020-2031
Global AI-based Raw Material Sourcing Optimization market size and forecasts by region and country, in consumption value ($ Million), 2020-2031
Global AI-based Raw Material Sourcing Optimization market size and forecasts, by Type and by Application, in consumption value ($ Million), 2020-2031
Global AI-based Raw Material Sourcing Optimization market shares of main players, in revenue ($ Million), 2020-2025
The Primary Objectives in This Report Are:
To determine the size of the total market opportunity of global and key countries
To assess the growth potential for AI-based Raw Material Sourcing Optimization
To forecast future growth in each product and end-use market
To assess competitive factors affecting the marketplace
This report profiles key players in the global AI-based Raw Material Sourcing Optimization market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include Amazon, IBM, FedEx, American Software, Inc., Kinaxis, Blue Yonder, SAP, Intellinum, Alpha Augmented Services AG, ToolsGroup, etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Market segmentation
AI-based Raw Material Sourcing Optimization market is split by Type and by Application. For the period 2020-2031, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segment by Type
Demand Forecasting and Inventory Management
Supplier Selection and Evaluation
Logistics and Transportation Optimization
Resource Allocation and Quality Control
Automated Procurement Process
Others
Market segment by Application
Metals and Mining
Manufacturing
Agriculture
Food Processing
Textiles and Clothing
Pharmaceuticals
Other
Market segment by players, this report covers
Amazon
IBM
FedEx
American Software, Inc.
Kinaxis
Blue Yonder
SAP
Intellinum
Alpha Augmented Services AG
ToolsGroup
RightChain Healthcare
QAD
Inspur Information
Neo Tangent
Market segment by regions, regional analysis covers
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia, Italy and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)
South America (Brazil, Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe AI-based Raw Material Sourcing Optimization product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of AI-based Raw Material Sourcing Optimization, with revenue, gross margin, and global market share of AI-based Raw Material Sourcing Optimization from 2020 to 2025.
Chapter 3, the AI-based Raw Material Sourcing Optimization competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2020 to 2031
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2020 to 2025.and AI-based Raw Material Sourcing Optimization market forecast, by regions, by Type and by Application, with consumption value, from 2026 to 2031.
Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of AI-based Raw Material Sourcing Optimization.
Chapter 13, to describe AI-based Raw Material Sourcing Optimization research findings and conclusion.
Artificial intelligence (AI)-based raw material procurement optimization is defined as the use of AI technology and algorithms to achieve intelligent decision-making and optimization in the raw material procurement process by analyzing historical data, market demand, supplier information, inventory levels and other multi-dimensional factors. This optimization aims to improve procurement efficiency, reduce costs, reduce inventory risks, and enhance the transparency and collaboration of the supply chain.
This report is a detailed and comprehensive analysis for global AI-based Raw Material Sourcing Optimization market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
Key Features:
Global AI-based Raw Material Sourcing Optimization market size and forecasts, in consumption value ($ Million), 2020-2031
Global AI-based Raw Material Sourcing Optimization market size and forecasts by region and country, in consumption value ($ Million), 2020-2031
Global AI-based Raw Material Sourcing Optimization market size and forecasts, by Type and by Application, in consumption value ($ Million), 2020-2031
Global AI-based Raw Material Sourcing Optimization market shares of main players, in revenue ($ Million), 2020-2025
The Primary Objectives in This Report Are:
To determine the size of the total market opportunity of global and key countries
To assess the growth potential for AI-based Raw Material Sourcing Optimization
To forecast future growth in each product and end-use market
To assess competitive factors affecting the marketplace
This report profiles key players in the global AI-based Raw Material Sourcing Optimization market based on the following parameters - company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include Amazon, IBM, FedEx, American Software, Inc., Kinaxis, Blue Yonder, SAP, Intellinum, Alpha Augmented Services AG, ToolsGroup, etc.
This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals.
Market segmentation
AI-based Raw Material Sourcing Optimization market is split by Type and by Application. For the period 2020-2031, the growth among segments provides accurate calculations and forecasts for Consumption Value by Type and by Application. This analysis can help you expand your business by targeting qualified niche markets.
Market segment by Type
Demand Forecasting and Inventory Management
Supplier Selection and Evaluation
Logistics and Transportation Optimization
Resource Allocation and Quality Control
Automated Procurement Process
Others
Market segment by Application
Metals and Mining
Manufacturing
Agriculture
Food Processing
Textiles and Clothing
Pharmaceuticals
Other
Market segment by players, this report covers
Amazon
IBM
FedEx
American Software, Inc.
Kinaxis
Blue Yonder
SAP
Intellinum
Alpha Augmented Services AG
ToolsGroup
RightChain Healthcare
QAD
Inspur Information
Neo Tangent
Market segment by regions, regional analysis covers
North America (United States, Canada and Mexico)
Europe (Germany, France, UK, Russia, Italy and Rest of Europe)
Asia-Pacific (China, Japan, South Korea, India, Southeast Asia and Rest of Asia-Pacific)
South America (Brazil, Rest of South America)
Middle East & Africa (Turkey, Saudi Arabia, UAE, Rest of Middle East & Africa)
The content of the study subjects, includes a total of 13 chapters:
Chapter 1, to describe AI-based Raw Material Sourcing Optimization product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top players of AI-based Raw Material Sourcing Optimization, with revenue, gross margin, and global market share of AI-based Raw Material Sourcing Optimization from 2020 to 2025.
Chapter 3, the AI-based Raw Material Sourcing Optimization competitive situation, revenue, and global market share of top players are analyzed emphatically by landscape contrast.
Chapter 4 and 5, to segment the market size by Type and by Application, with consumption value and growth rate by Type, by Application, from 2020 to 2031
Chapter 6, 7, 8, 9, and 10, to break the market size data at the country level, with revenue and market share for key countries in the world, from 2020 to 2025.and AI-based Raw Material Sourcing Optimization market forecast, by regions, by Type and by Application, with consumption value, from 2026 to 2031.
Chapter 11, market dynamics, drivers, restraints, trends, Porters Five Forces analysis.
Chapter 12, the key raw materials and key suppliers, and industry chain of AI-based Raw Material Sourcing Optimization.
Chapter 13, to describe AI-based Raw Material Sourcing Optimization research findings and conclusion.
Table of Contents
112 Pages
- 1 Market Overview
- 2 Company Profiles
- 3 Market Competition, by Players
- 4 Market Size Segment by Type
- 5 Market Size Segment by Application
- 6 North America
- 7 Europe
- 8 Asia-Pacific
- 9 South America
- 10 Middle East & Africa
- 11 Market Dynamics
- 12 Industry Chain Analysis
- 13 Research Findings and Conclusion
- 14 Appendix
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