PUBLISHER: The Business Research Company | PRODUCT CODE: 1760170
PUBLISHER: The Business Research Company | PRODUCT CODE: 1760170
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. By leveraging data-driven insights and automation, machine learning transforms traditional supply chain operations, improving efficiency, reducing costs, and enhancing customer satisfaction.
The main components of machine learning in supply chain management include software and services. The software refers to a suite of digital tools and platforms that utilize machine learning algorithms to enhance various supply chain functions. These tools incorporate technologies such as artificial intelligence, deep learning, natural language processing, and predictive analytics, and can be deployed in both cloud-based and on-premises environments. Applications of machine learning in supply chain management include demand forecasting, inventory management, supplier selection, logistics optimization, and risk management. These solutions cater to end users across various industries, including retail and e-commerce, manufacturing, healthcare, automotive, food and beverage, consumer goods, and more.
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 machine learning in supply chain management market statistics, including machine learning in supply chain management industry global market size, regional shares, competitors with a machine learning in supply chain management market share, detailed machine learning in supply chain management market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning in supply chain management industry. This machine learning in supply chain management market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.
The machine learning in supply chain management market size has grown exponentially in recent years. It will grow from$8.32 billion in 2024 to $10.34 billion in 2025 at a compound annual growth rate (CAGR) of 24.2%. The growth during the historical period can be attributed to enhanced operational efficiency, the rising demand for predictive analytics, increased automation in logistics, the surge in data-driven decision-making, and improved accuracy in demand forecasting.
The machine learning in supply chain management market size is expected to see exponential growth in the next few years. It will grow to$24.39 billion in 2029 at a compound annual growth rate (CAGR) of 23.9%. The growth during the forecast period can be attributed to the adoption of digital transformation, greater market volatility, a focus on sustainability, the expansion of e-commerce, the rise in data-driven decision-making, and the growing need for automation and data-driven strategies in logistics. Key trends in the forecast period include improved inventory management solutions, real-time supply chain visibility, risk management through AI insights, cloud-based supply chain solutions for better collaboration, and AI-driven solutions for optimizing warehouse operations.
The rising automation in logistics is set to drive the expansion of the machine learning in supply chain management market in the coming years. Logistics automation refers to the use of technologies such as robotics, AI, and software systems to streamline and optimize supply chain processes with minimal human involvement. This growth in automation is driven by its ability to improve efficiency, lower costs, and meet the increasing demand for e-commerce by utilizing technology to boost operational scalability and customer satisfaction. Machine learning plays a crucial role in supply chain management by enabling predictive analytics, demand forecasting, and real-time decision-making. It also supports logistics automation with tools such as route optimization, warehouse robotics, and intelligent inventory control. For example, in September 2024, the International Federation of Robotics (IFR), a Germany-based industry association, reported that the number of robots operating in factories worldwide reached 4,281,585 units in 2023, a 10% increase from the 3,904,000 units recorded in 2022. As a result, the rise in logistics automation is contributing to the growth of the machine learning in supply chain management market.
Leading companies in the machine learning in supply chain management market are focusing on developing advanced technological solutions, such as AI-powered assistants for supply chain management, to optimize decision-making, improve operations, and boost overall efficiency. An AI assistant for supply chain management is an intelligent software tool that uses artificial intelligence to automate and optimize supply chain functions 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, an innovative AI tool designed for supply chain management. This platform combines both AI and machine learning (ML) technologies to offer real-time monitoring, smart prescriptions, and interactive visualizations. By merging AI-driven insights with ML-based predictive analytics, NEO Assistant enhances decision-making and operational efficiency across complex logistics networks. It provides users with actionable recommendations and simplified problem-solving capabilities, making it highly effective for managing dynamic supply chain environments.
In September 2023, Logility, a US-based software company, acquired Garvis for an undisclosed amount. With this acquisition, Logility aims to bolster its supply chain planning capabilities by integrating Garvis' AI-driven demand forecasting technology, utilizing generative AI and machine learning to enhance forecast accuracy and streamline supply chain operations. Garvis, a Belgium-based SaaS company, specializes in AI-driven demand forecasting and machine learning-powered supply chain solutions.
Major players in the machine learning in supply chain management market are Amazon.com Inc., Microsoft Corporation, Deutsche Post AG, FedEx Corporation, Maersk 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 Diseno Textil S.A., FourKites Inc., Noodle.ai Inc., Lokad SAS, Garvis Inc., and Logility Inc.
North America was the largest region in the machine learning in supply chain management market in 2024. The regions covered in machine learning in supply chain management report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa.
The countries covered in the machine learning in supply chain management market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
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.
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.
Machine Learning in Supply Chain Management Global Market Report 2025 from The Business Research Company provides strategists, marketers and senior management with the critical information they need to assess the market.
This report focuses on machine learning in supply chain management market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.
Where is the largest and fastest growing market for machine learning in supply chain management ? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward? The machine learning in supply chain management market global report from the Business Research Company answers all these questions and many more.
The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market's historic and forecast market growth by geography.
The forecasts are made after considering the major factors currently impacting the market. These include the Russia-Ukraine war, rising inflation, higher interest rates, and the legacy of the COVID-19 pandemic.