The machine learning in supply chain management market size has grown exponentially in recent years. It will grow from $10.26 billion in 2025 to $12.71 billion in 2026 at a compound annual growth rate (CAGR) of 23.8%. The growth in the historic period can be attributed to growth in global trade networks, expansion of e-commerce logistics, adoption of cloud supply chain platforms, rising demand for operational efficiency, digital transformation of warehouses.
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.
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 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.
North America was the largest region in the machine learning in supply chain management market in 2025. The regions covered in the machine learning in supply chain management market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning in supply chain management market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
Tariffs have significantly impacted the machine learning supply chain market by increasing costs of imported hardware, logistics equipment, and global transportation services. These effects are most visible in Asia-Pacific and North American manufacturing corridors. Higher trade costs have accelerated adoption of AI-driven supply chain optimization tools. At the same time, tariffs are encouraging regional sourcing strategies and localized manufacturing, improving resilience and data-driven operational planning.
The machine learning in supply chain management market research report is one of a series of new reports 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.
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 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.
This product will be delivered within 1-3 business days.
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.
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 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.
North America was the largest region in the machine learning in supply chain management market in 2025. The regions covered in the machine learning in supply chain management market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning in supply chain management market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
Tariffs have significantly impacted the machine learning supply chain market by increasing costs of imported hardware, logistics equipment, and global transportation services. These effects are most visible in Asia-Pacific and North American manufacturing corridors. Higher trade costs have accelerated adoption of AI-driven supply chain optimization tools. At the same time, tariffs are encouraging regional sourcing strategies and localized manufacturing, improving resilience and data-driven operational planning.
The machine learning in supply chain management market research report is one of a series of new reports 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.
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 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.
This product will be delivered within 1-3 business days.
Table of Contents
1. Executive Summary
2. Machine Learning in Supply Chain Management Market Characteristics
3. Machine Learning in Supply Chain Management Market Supply Chain Analysis
4. Global Machine Learning in Supply Chain Management Market Trends and Strategies
5. Machine Learning in Supply Chain Management Market Analysis of End Use Industries
7. Global Machine Learning in Supply Chain Management Strategic Analysis Framework, Current Market Size, Market Comparisons and Growth Rate Analysis
8. Global Machine Learning in Supply Chain Management Total Addressable Market (TAM) Analysis for the Market
9. Machine Learning in Supply Chain Management Market Segmentation
10. Machine Learning in Supply Chain Management Market, Industry Metrics by Country
11. Machine Learning in Supply Chain Management Market Regional and Country Analysis
12. Asia-Pacific Machine Learning in Supply Chain Management Market
13. China Machine Learning in Supply Chain Management Market
14. India Machine Learning in Supply Chain Management Market
15. Japan Machine Learning in Supply Chain Management Market
16. Australia Machine Learning in Supply Chain Management Market
17. Indonesia Machine Learning in Supply Chain Management Market
18. South Korea Machine Learning in Supply Chain Management Market
19. Taiwan Machine Learning in Supply Chain Management Market
20. South East Asia Machine Learning in Supply Chain Management Market
21. Western Europe Machine Learning in Supply Chain Management Market
22. UK Machine Learning in Supply Chain Management Market
23. Germany Machine Learning in Supply Chain Management Market
24. France Machine Learning in Supply Chain Management Market
25. Italy Machine Learning in Supply Chain Management Market
26. Spain Machine Learning in Supply Chain Management Market
27. Eastern Europe Machine Learning in Supply Chain Management Market
28. Russia Machine Learning in Supply Chain Management Market
29. North America Machine Learning in Supply Chain Management Market
30. USA Machine Learning in Supply Chain Management Market
31. Canada Machine Learning in Supply Chain Management Market
32. South America Machine Learning in Supply Chain Management Market
33. Brazil Machine Learning in Supply Chain Management Market
34. Middle East Machine Learning in Supply Chain Management Market
35. Africa Machine Learning in Supply Chain Management Market
37. Machine Learning in Supply Chain Management Market Competitive Landscape and Company Profiles
38. Machine Learning in Supply Chain Management Market Other Major and Innovative Companies
41. Machine Learning in Supply Chain Management Market High Potential Countries, Segments and Strategies
42. Appendix
Executive Summary
Machine Learning in Supply Chain Management Market Global Report 2026 provides strategists, marketers and senior management with the critical information they need to assess the market.This report focuses 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.
Reasons to Purchase:
- Gain a truly global perspective with the most comprehensive report available on this market covering 16 geographies.
- Assess the impact of key macro factors such as geopolitical conflicts, trade policies and tariffs, inflation and interest rate fluctuations, and evolving regulatory landscapes.
- Create regional and country strategies on the basis of local data and analysis.
- Identify growth segments for investment.
- Outperform competitors using forecast data and the drivers and trends shaping the market.
- Understand customers based on end user analysis.
- Benchmark performance against key competitors based on market share, innovation, and brand strength.
- Evaluate the total addressable market (TAM) and market attractiveness scoring to measure market potential.
- Suitable for supporting your internal and external presentations with reliable high-quality data and analysis
- Report will be updated with the latest data and delivered to you along with an Excel data sheet for easy data extraction and analysis.
- All data from the report will also be delivered in an excel dashboard format.
Description
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, including technological disruption, regulatory shifts, and changing consumer preferences? The machine learning in supply chain management market global report answers all these questions and many more.The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, total addressable market (TAM), market attractiveness score (MAS), competitive landscape, market shares, company scoring matrix, trends and strategies for this market. It traces the market’s historic and forecast market growth by geography.
- The market characteristics section of the report defines and explains the market. This section also examines key products and services offered in the market, evaluates brand-level differentiation, compares product features, and highlights major innovation and product development trends.
- The supply chain analysis section provides an overview of the entire value chain, including key raw materials, resources, and supplier analysis. It also provides a list competitor at each level of the supply chain.
- The updated trends and strategies section analyses the shape of the market as it evolves and highlights emerging technology trends such as digital transformation, automation, sustainability initiatives, and AI-driven innovation. It suggests how companies can leverage these advancements to strengthen their market position and achieve competitive differentiation.
- The regulatory and investment landscape section provides an overview of the key regulatory frameworks, regularity bodies, associations, and government policies influencing the market. It also examines major investment flows, incentives, and funding trends shaping industry growth and innovation.
- The market size section gives the market size ($b) covering both the historic growth of the market, and forecasting its development.
- The forecasts are made after considering the major factors currently impacting the market. These include the technological advancements such as AI and automation, Russia-Ukraine war, trade tariffs (government-imposed import/export duties), elevated inflation and interest rates.
- The total addressable market (TAM) analysis section defines and estimates the market potential compares it with the current market size, and provides strategic insights and growth opportunities based on this evaluation.
- The market attractiveness scoring section evaluates the market based on a quantitative scoring framework that considers growth potential, competitive dynamics, strategic fit, and risk profile. It also provides interpretive insights and strategic implications for decision-makers.
- Market segmentations break down the market into sub markets.
- The regional and country breakdowns section gives an analysis of the market in each geography and the size of the market by geography and compares their historic and forecast growth.
- Expanded geographical coverage includes Taiwan and Southeast Asia, reflecting recent supply chain realignments and manufacturing shifts in the region. This section analyzes how these markets are becoming increasingly important hubs in the global value chain.
- The competitive landscape chapter gives a description of the competitive nature of the market, market shares, and a description of the leading companies. Key financial deals which have shaped the market in recent years are identified.
- The company scoring matrix section evaluates and ranks leading companies based on a multi-parameter framework that includes market share or revenues, product innovation, and brand recognition.
Report Scope
Markets Covered:
1) By Component: Software; Services2) 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 Software2) By Services: Managed Services; Professional Services; Consulting Services; Training And Support Services
Companies Mentioned: 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.
Countries: Australia; Brazil; China; France; Germany; India; Indonesia; Japan; Taiwan; Russia; South Korea; UK; USA; Canada; Italy; Spain.
Regions: Asia-Pacific; South East Asia; Western Europe; Eastern Europe; North America; South America; Middle East; Africa
Time Series: Five years historic and ten years forecast.
Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita.
Data Segmentation: Country and regional historic and forecast data, market share of competitors, market segments.
Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.
Delivery Format: Word, PDF or Interactive Report + Excel Dashboard
Added Benefits:
- Bi-Annual Data Update
- Customisation
- Expert Consultant Support
Companies Mentioned
The companies featured in this Machine Learning in Supply Chain Management market report include:- 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.
Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 250 |
| Published | February 2026 |
| Forecast Period | 2026 - 2030 |
| Estimated Market Value ( USD | $ 12.71 Billion |
| Forecasted Market Value ( USD | $ 29.53 Billion |
| Compound Annual Growth Rate | 23.5% |
| Regions Covered | Global |
| No. of Companies Mentioned | 22 |
