Austin, July 28, 2025 (GLOBE NEWSWIRE) -- Machine Learning in Supply Chain Management Market Size Analysis
The SNS Insider report indicates that the Machine Learning in Supply Chain Management Market was valued at USD 3.44 billion in 2023 and is projected to reach USD 30.16 billion by 2032, growing at a compound annual growth rate (CAGR) of 31.2% during 2024–2032.
The U.S. machine learning in supply chain management market is buoyed by early-stage AI adoption, solid technology infrastructure, and extensive application in retail, manufacturing, and logistics. The market was valued at USD 0.89 billion in 2024 and is projected to reach USD 8.46 billion by 2032, growing at a CAGR of 32.55% during 2025–2032.
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Key Machine Learning in Supply Chain Management Companies Profiled in the Report
- Blue Yonder Group Inc.
- C.H. Robinson Worldwide Inc.
- Coupa Software Inc.
- DHL Supply Chain
- FedEx Corporation
- Google LLC
- IBM Corporation
- Manhattan Associates Inc.
- Microsoft Corporation
- Oracle Corporation and others.
Machine Learning in Supply Chain Management Market Report Scope
| Report Attributes | Details |
| Market Size in 2024 | US$ 3.44 billion |
| Market Size by 2032 | US$ 30.16 billion |
| CAGR | CAGR of 31.2% From 2025 to 2032 |
| Base Year | 2024 |
| Forecast Period | 2025-2032 |
| Historical Data | 2021-2023 |
| Regional Analysis | North America (US, Canada, Mexico), Europe (Germany, France, UK, Italy, Spain, Poland, Turkey, Rest of Europe), Asia Pacific (China, India, Japan, South Korea, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (UAE, Saudi Arabia, Qatar, South Africa, Rest of Middle East & Africa), Latin America (Brazil, Argentina, Rest of Latin America) |
| Key Growth Drivers | Increased integration of AI and machine learning algorithms across the supply chain landscape, helping organizations enhance forecast accuracy, reduce operational costs, optimize inventory, and streamline logistics and distribution. |
By Component: Software Dominates, Services Grow Fastest
The software segment dominated the Machine Learning in Supply Chain Management Market in 2024, accounting for 56.27% of the revenue share, providing strong platforms for forecasting, automation, and process optimization. The integration of these platforms to the existing ERP and WMS system is seamless, which also comes with the necessary tools for inventory planning, route optimization, and supplier analysis among others. With an increased number of companies looking to invest in their own data science capabilities, demand for ML software tool solutions to can be tailored is growing.
The services segment is poised for the fastest CAGR during 2024–2032, owing to the increasing demand for consulting, implementation, and managed analytics services. That is, most organizations, in particular SMEs, do not have in-house expertise and depend on external service providers to integrate ML workflows into their business supply chains. Because of the rapid evolution of ML, continuous support and model refinement are essential, and therefore, the demand for specialized services is accelerating.
By Technique: Supervised Learning Leads, Unsupervised Learning Grows Fastest
Supervised learning dominated the market in 2024 and accounted for 68.50% of revenue share, owing to the deployment of demanding system prediction, inventory management and quality control, where labeled data sets are easily obtainable. Retail and manufacturing industries utilize historical data to train predictive models that enable better operational efficiency and customer satisfaction.
Unsupervised learning is projected to experience the fastest CAGR, due to its application in anomaly detection, customer segmentation, and discovering unknown patterns without pre-defined labels. In an age when supply chains are, get this, global, multi-tiered (i.e., think second and third tier suppliers), and highly decentralized, the need to discover latent relationships in real-time data has become a must-have across enterprises and their entire supplier eco-systems.
By Organization Size: Large Enterprises Dominate, SMEs See Fastest Growth
Large enterprises dominated the market in 2024 and accounted for 69.33% of revenue share, as they use ML to optimize their multilayered supply chains that span across global enterprises. Such organizations have the scale and the capital to invest in advanced analytics, predictive modeling, and digital twin technologies.
Small and Medium-sized Enterprises (SMEs) are registering the fastest CAGR as cloud-based machine learning (ML) platforms become more affordable and accessible. Such solutions provide flexibility and scalable options for demand planning, order management, and supplier evaluation without extensive capital outlay. With ever-increasing competition, SMEs are leveraging ML provides an IF to remain agile yet cost-competitive across the supply chain.
By Deployment Model: Cloud-Based Leads, On-Premises Expands Fastest
Cloud-based ML platforms dominated the deployment model segment in 2024 and accounted for 69.33% of revenue share, driven by scalability, real-time processing, and quick integration with other SaaS applications. Due to their capabilities to support distributed teams, remote operations, and flexible pricing, they become attractive across organizational sizes.
On-premises deployments are expected to register the fastest CAGR, it is primarily on account of data sovereignty and security in highly regulated sectors such as pharmaceuticals and defense. So organizations with legacy infrastructure mostly chose up-prem solutions, it gives them the control with the infrastructure they upgrade their digital capabilities with.
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By Region: North America Dominates, Asia-Pacific Grows Fastest
In 2024, North America led the market with a massive revenue share attributed to early AI adoption, established IT infrastructure, and a large number of tech giants that offer application supply chain ML solutions. In the US, the R&D and integration of AI in logistics are led by e-commerce, automotive, and pharmaceutical companies, which use predictive models heavily.
The Asia-Pacific region is forecast to register the fastest CAGR owing to rapidly growing manufacturing, rising e-commerce penetration, and pro-government initiatives in countries such as China, India, and Japan. Smart supply chains are becoming a necessity across multiple industries as regional enterprises digitalize their operations and converge IoT with ML.
Machine Learning in Supply Chain Management Market Segmentation
By Component
- Software
- Services
By Technique
- Supervised learning
- Unsupervised learning
By Organization Size
- Large enterprises
- Small and Medium-sized enterprises (SME)
By Deployment Model
- Cloud-based
- On-premises
By Application
- Demand forecasting
- Supplier Relationship Management (SRM)
- Risk management
- Product lifecycle management
- Sales and Operations Planning (S&OP)
- Others
By End-user
- Retail and e-commerce
- Manufacturing
- Healthcare
- Automotive
- Food & beverage
- Consumer goods
- Others
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Table of Contents – Major Key Points
1. Introduction
2. Executive Summary
3. Market Overview
4. Statistical Insights & Trends Reporting
5. Machine Learning in Supply Chain Management Market Segmental Analysis & Forecast, By Component
6. Machine Learning in Supply Chain Management Market Segmental Analysis & Forecast, By Technique
7. Machine Learning in Supply Chain Management Market Segmental Analysis & Forecast, By Organization Size
8. Machine Learning in Supply Chain Management Market Segmental Analysis & Forecast, By Deployment Model
9. Machine Learning in Supply Chain Management Market Segmental Analysis & Forecast, By Application
10. Machine Learning in Supply Chain Management Market Segmental Analysis & Forecast, By End-user
11. Machine Learning in Supply Chain Management Market Segmental Analysis & Forecast, By Region
12. Competitive Landscape
13. Analyst Recommendations
14. Assumptions
15. Disclaimer
16. Appendix
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