Machine Learning in Supply Chain Management Market Report 2026

Machine Learning in Supply Chain Management Market Overview

• Machine Learning in Supply Chain Management market size has reached to $10.26 billion in 2025 • Expected to grow to $29.53 billion in 2030 at a compound annual growth rate (CAGR) of 23.5% • Growth Driver: Growing Automation In Logistics Fueling The Growth Of The Market Due To Increasing Efficiency And Cost Reduction • Market Trend: AI-Powered Assistants Revolutionizing Supply Chain Management • North America was the largest region in 2025.

What Is Covered Under Machine Learning in Supply Chain Management Market?

Machine learning in supply chain management refers to the application of advanced algorithms and artificial intelligence (AI) techniques to analyze large volumes of data, predict outcomes, and make informed decisions across various aspects of the supply chain. It transforms traditional supply chain operations by leveraging data-driven insights and automation to improve efficiency, reduce costs, and enhance customer satisfaction. The main components of machine learning in supply chain management are software and services. Software in machine learning in supply chain management refers to the suite of digital tools and platforms that leverage machine learning algorithms to enhance various supply chain functions. It uses various technologies such as artificial intelligence, deep learning, natural language processing, and predictive analytics on various deployment modes, including cloud-based and on-premises. It is used for various applications, including demand forecasting, inventory management, supplier selection, logistics optimization, and risk management, catering to end users such as retail and e-commerce, manufacturing, healthcare, automotive, food and beverage, consumer goods, and others.

What Is The Machine Learning in Supply Chain Management Market Growth Forecast?

The machine learning in supply chain management market size is expected to see exponential growth in the next few years. It will grow to $29.53 billion in 2030 at a compound annual growth rate (CAGR) of 23.5%. The growth in the forecast period can be attributed to integration of autonomous supply chain systems, expansion of AI-powered warehouse automation, adoption of predictive logistics platforms, growth of real-time data analytics, rising investment in smart logistics. Major trends in the forecast period include predictive demand forecasting, AI-based inventory optimization, automated logistics planning, real-time supply chain visibility, risk analytics integration.

Global Machine Learning in Supply Chain Management Market Segmentation

1) By Component: Software, Services 2) By Technology: Artificial Intelligence, Deep Learning, Natural Language Processing, Predictive Analytics 3) By Deployment Mode: Cloud-Based, On-Premises 4) By Application: Demand Forecasting, Inventory Management, Supplier Selection, Logistics Optimization, Risk Management 5) By End-User: Retail And E-Commerce, Manufacturing, Healthcare, Automotive, Food And Beverage, Consumer Goods, Other End-Users Subsegments: 1) By Software: Demand Forecasting Software, Warehouse Management Software (WMS), Transportation Management Systems (TMS), Inventory Optimization Software, Procurement And Sourcing Analytics Tools, Supply Chain Planning Software, Risk Management And Compliance Software 2) By Services: Managed Services, Professional Services, Consulting Services, Training And Support Services

What Is The Driver Of The Machine Learning in Supply Chain Management Market?

The growing automation in logistics is expected to propel the growth of the machine learning in supply chain management market going forward. Automation in logistics refers to the use of technology, such as robotics, AI, and software systems, to streamline and optimize supply chain processes with minimal human intervention. Automation in logistics is growing due to its ability to enhance efficiency, reduce costs, and meet rising e-commerce demands by leveraging technologies to improve operational scalability and customer satisfaction. Machine learning enhances supply chain management by enabling predictive analytics, demand forecasting, and real-time decision-making. It also drives logistics automation through route optimization, warehouse robotics, and intelligent inventory control. For instance, in September 2024, according to the International Federation of Robotics (IFR), a Germany-based industry association, in 2023, the number of robots operating in factories worldwide reached 4,281,585 units, marking a 10% increase from the 3,904,000 units recorded in 2022. Therefore, growing automation in logistics is driving the growth of the machine learning in supply chain management industry.

Key Players In The Global Machine Learning in Supply Chain Management Market

Major companies operating in the machine learning in supply chain management market are Amazon.com Inc., Microsoft Corporation, Deutsche Post AG, FedEx Corporation, Mærsk A/S, Siemens AG, International Business Machines Corporation, Oracle Corporation, SAP SE, Ferguson Enterprises LLC, Zoetop Business Co. Ltd., H&M Hennes & Mauritz AB, J. C. Penney Corporation Inc., ALTANA AG, Koch Industries Inc., Industria de Diseño Textil S.A., FourKites Inc., Noodle.AI Inc., Lokad SAS, Garvis Inc., Logility Inc.

Global Machine Learning in Supply Chain Management Market Trends and Insights

Major companies operating in the machine learning in supply chain management market are focusing on developing technologically advanced solutions, such as artificial intelligence (AI) assistants for supply chain management, to enhance decision-making, optimize operations, and improve overall efficiency. An AI assistant for supply chain management refers to an intelligent software tool that uses artificial intelligence to automate and optimize supply chain processes such as forecasting, inventory management, and logistics planning. For instance, in February 2024, One Network Enterprises, a US-based provider of digital supply chain solutions, introduced NEO Assistant, a groundbreaking AI tool tailored for supply chain management. This innovative platform leverages both AI and machine learning (ML) technologies to provide real-time monitoring, smart prescriptions, and interactive visualizations. By combining AI-driven insights with ML-driven predictive analytics, NEO Assistant enhances decision-making and operational efficiency across complex logistics networks. The system offers users actionable recommendations and streamlined problem-solving capabilities, making it highly effective in navigating dynamic supply chain environments.

What Are Latest Mergers And Acquisitions In The Machine Learning in Supply Chain Management Market?

In September 2023, Logility, a US-based software company, acquired Garvis for an undisclosed amount. With this acquisition, Logility aims to enhance its supply chain planning capabilities by integrating Garvis' artificial intelligence (AI)-driven demand forecasting technology, leveraging generative AI and machine learning to improve forecast accuracy and streamline supply chain operations. Garvis is a Belgium-based SaaS company offering AI-driven demand forecasting and machine learning-powered supply chain solutions.

Regional Insights

North America was the largest region in the machine learning in supply chain management market in 2025. The regions covered in this market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in this market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

What Defines the Machine Learning in Supply Chain Management Market?

The machine learning in supply chain management market consists of revenues earned by entities by providing services such as demand forecasting, inventory optimization, supply chain risk management, intelligent procurement, and predictive maintenance. The market value includes the value of related goods sold by the service provider or included within the service offering. The machine learning in supply chain management market also includes sales of software solutions, AI-powered platforms, supply chain control towers, and data analytics tools. Values in this market are ‘factory gate’ values, that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

How is Market Value Defined and Measured?

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified). The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

What Key Data and Analysis Are Included in the Machine Learning in Supply Chain Management Market Report 2026?

The machine learning in supply chain management market research report is one of a series of new reports from The Business Research Company that provides market statistics, including industry global market size, regional shares, competitors with the market share, detailed market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning in supply chain management industry. The market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future state of the industry.