The global market for Machine Learning in Supply Chain Management was estimated at US$2.1 Billion in 2024 and is projected to reach US$8.7 Billion by 2030, growing at a CAGR of 26.9% from 2024 to 2030. This comprehensive report provides an in-depth analysis of market trends, drivers, and forecasts, helping you make informed business decisions. The report includes the most recent global tariff developments and how they impact the Machine Learning in Supply Chain Management market.
Segments: Component (Software Component, Services Component); Deployment (Cloud-based Deployment, On-Premise Deployment); Organization (Large Enterprises, Small & Medium-Sized Enterprises); Application (Demand Forecasting Application, Supplier Relationship Management Application, Risk Management Application, Product Lifecycle Management Application, Sales & Operations Planning Application, Other Applications); End-User (Retail & E-commerce End-User, Manufacturing End-User, Healthcare End-User, Automotive End-User, Food & Beverage End-User, Consumer Goods End-User, Other End-Users)
Geographic Regions/Countries: World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.
The analysts continuously track trade developments worldwide, drawing insights from leading global economists and over 200 industry and policy institutions, including think tanks, trade organizations, and national economic advisory bodies. This intelligence is integrated into forecasting models to provide timely, data-driven analysis of emerging risks and opportunities.
Global Machine Learning in Supply Chain Management Market - Key Trends & Drivers Summarized
Why Is Machine Learning in Supply Chain Management Gaining Popularity?
Supply chain management is becoming increasingly complex due to global disruptions, fluctuating demand, and increasing customer expectations for fast and efficient deliveries. Machine learning is transforming supply chain operations by providing real-time insights, improving demand forecasting, and optimizing inventory levels. With the rise of e-commerce, just-in-time manufacturing, and multi-channel distribution models, businesses are leveraging AI-powered analytics to enhance supply chain visibility and minimize risks.How Are Innovations Enhancing the Performance of Machine Learning in Supply Chain Management?
Advancements in AI-driven analytics, cloud computing, and automation are making supply chains more resilient and agile. Machine learning algorithms are being used for predictive analytics to anticipate demand fluctuations, detect anomalies, and optimize warehouse inventory levels. AI-driven route optimization tools are improving logistics efficiency, reducing transportation costs, and enhancing last-mile delivery accuracy. Blockchain-integrated AI solutions are also improving supply chain transparency, enabling real-time tracking of goods from manufacturers to end consumers.What Are the Key Market Drivers?
The increasing complexity of global supply chains, rising consumer expectations for faster deliveries, and the need for improved risk management are key factors driving market growth. The COVID-19 pandemic has further accelerated the adoption of AI-powered supply chain solutions as companies seek to mitigate disruptions and enhance operational efficiency. Additionally, sustainability concerns and regulatory compliance requirements are pushing companies to adopt AI-driven solutions that optimize energy consumption and reduce waste.What Challenges and Future Opportunities Exist?
Challenges include the high cost of AI implementation, data integration issues across supply chain networks, and cybersecurity risks. However, opportunities exist in the expansion of AI-driven supply chain risk management solutions, the development of self-learning algorithms for automated decision-making, and the adoption of AI-powered sustainability tracking tools. The continued evolution of AI in autonomous warehouse management, robotic supply chain operations, and AI-driven procurement strategies will further revolutionize supply chain management.Report Scope
The report analyzes the Machine Learning in Supply Chain Management market, presented in terms of market value (US$ Thousand). The analysis covers the key segments and geographic regions outlined below.Segments: Component (Software Component, Services Component); Deployment (Cloud-based Deployment, On-Premise Deployment); Organization (Large Enterprises, Small & Medium-Sized Enterprises); Application (Demand Forecasting Application, Supplier Relationship Management Application, Risk Management Application, Product Lifecycle Management Application, Sales & Operations Planning Application, Other Applications); End-User (Retail & E-commerce End-User, Manufacturing End-User, Healthcare End-User, Automotive End-User, Food & Beverage End-User, Consumer Goods End-User, Other End-Users)
Geographic Regions/Countries: World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.
Key Insights:
- Market Growth: Understand the significant growth trajectory of the Software Component segment, which is expected to reach US$5.1 Billion by 2030 with a CAGR of a 23.8%. The Services Component segment is also set to grow at 32.5% CAGR over the analysis period.
- Regional Analysis: Gain insights into the U.S. market, estimated at $549.2 Million in 2024, and China, forecasted to grow at an impressive 25.7% CAGR to reach $1.3 Billion by 2030. Discover growth trends in other key regions, including Japan, Canada, Germany, and the Asia-Pacific.
Why You Should Buy This Report:
- Detailed Market Analysis: Access a thorough analysis of the Global Machine Learning in Supply Chain Management Market, covering all major geographic regions and market segments.
- Competitive Insights: Get an overview of the competitive landscape, including the market presence of major players across different geographies.
- Future Trends and Drivers: Understand the key trends and drivers shaping the future of the Global Machine Learning in Supply Chain Management Market.
- Actionable Insights: Benefit from actionable insights that can help you identify new revenue opportunities and make strategic business decisions.
Key Questions Answered:
- How is the Global Machine Learning in Supply Chain Management Market expected to evolve by 2030?
- What are the main drivers and restraints affecting the market?
- Which market segments will grow the most over the forecast period?
- How will market shares for different regions and segments change by 2030?
- Who are the leading players in the market, and what are their prospects?
Report Features:
- Comprehensive Market Data: Independent analysis of annual sales and market forecasts in US$ Million from 2024 to 2030.
- In-Depth Regional Analysis: Detailed insights into key markets, including the U.S., China, Japan, Canada, Europe, Asia-Pacific, Latin America, Middle East, and Africa.
- Company Profiles: Coverage of players such as Accenture plc, Aera Technology, Amazon Web Services (AWS), Blue Yonder, Coupa Software and more.
- Complimentary Updates: Receive free report updates for one year to keep you informed of the latest market developments.
Select Competitors (Total 48 Featured):
- Accenture plc
- Aera Technology
- Amazon Web Services (AWS)
- Blue Yonder
- Coupa Software
- DataArt
- Flowspace
- FourKites
- IBM Corporation
- Lokad
- Microsoft Corporation
- Noodle.ai
- Oracle Corporation
- Osa Commerce
- Project44
- Samsara
- SAP SE
- Shipsy
- Shipwell
- Transmetrics
Tariff Impact Analysis: Key Insights for 2025
Global tariff negotiations across 180+ countries are reshaping supply chains, costs, and competitiveness. This report reflects the latest developments as of April 2025 and incorporates forward-looking insights into the market outlook.The analysts continuously track trade developments worldwide, drawing insights from leading global economists and over 200 industry and policy institutions, including think tanks, trade organizations, and national economic advisory bodies. This intelligence is integrated into forecasting models to provide timely, data-driven analysis of emerging risks and opportunities.
What’s Included in This Edition:
- Tariff-adjusted market forecasts by region and segment
- Analysis of cost and supply chain implications by sourcing and trade exposure
- Strategic insights into geographic shifts
Buyers receive a free July 2025 update with:
- Finalized tariff impacts and new trade agreement effects
- Updated projections reflecting global sourcing and cost shifts
- Expanded country-specific coverage across the industry
Table of Contents
I. METHODOLOGYII. EXECUTIVE SUMMARY2. FOCUS ON SELECT PLAYERSIV. COMPETITION
1. MARKET OVERVIEW
3. MARKET TRENDS & DRIVERS
4. GLOBAL MARKET PERSPECTIVE
III. MARKET ANALYSIS
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Accenture plc
- Aera Technology
- Amazon Web Services (AWS)
- Blue Yonder
- Coupa Software
- DataArt
- Flowspace
- FourKites
- IBM Corporation
- Lokad
- Microsoft Corporation
- Noodle.ai
- Oracle Corporation
- Osa Commerce
- Project44
- Samsara
- SAP SE
- Shipsy
- Shipwell
- Transmetrics
Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 339 |
| Published | June 2025 |
| Forecast Period | 2024 - 2030 |
| Estimated Market Value ( USD | $ 2.1 Billion |
| Forecasted Market Value ( USD | $ 8.7 Billion |
| Compound Annual Growth Rate | 26.9% |
| Regions Covered | Global |
