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세계의 공급망 관리용 머신러닝 시장 보고서(2025년)
Machine Learning in Supply Chain Management Global Market Report 2025
상품코드 : 1760170
리서치사 : The Business Research Company
발행일 : On Demand Report
페이지 정보 : 영문 175 Pages
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한글목차

공급망 관리용 머신러닝 시장 규모는 향후 수년간 비약적인 성장이 전망됩니다. 2029년의 연간 평균 성장률(CAGR)은 23.9%로 성장할 전망이며, 243억 9,000만 달러로 성장이 예측됩니다. 예측 기간 동안 성장은 디지털 전환 채택, 시장 변동성 확대, 지속가능성 중시, 전자상거래 확대, 데이터 주도 의사결정 증가, 물류에서의 자동화 및 데이터 주도 전략 요구 증가에 기인할 것으로 보입니다. 예측 기간 동안 주요 동향으로는 재고 관리 솔루션 개선, 실시간 공급망 가시화, AI 인사이트를 통한 위험 관리, 더 나은 협업을 위한 클라우드 기반 공급망 솔루션, 창고 업무 최적화를 위한 AI 주도형 솔루션 등이 있습니다.

물류 자동화의 시작은 향후 수년간 공급망 관리용 머신러닝 시장의 확대를 촉진합니다. 물류 자동화란 로봇공학, AI, 소프트웨어 시스템 등의 기술을 이용해 최소한의 인적 관여로 공급망 프로세스를 합리화 및 최적화하는 것을 말합니다. 이러한 자동화 성장은 효율성 향상, 비용 절감, 운영 확장성과 고객 만족도를 높이는 기술 활용을 통한 전자상거래 수요 증가에 대응하는 능력에서 비롯됩니다. 머신러닝은 예측 분석, 수요 예측, 실시간 의사 결정을 가능하게 함으로써 공급망 관리에 중요한 역할을 하고 있습니다. 또한 루트 최적화, 창고 로봇, 인텔리전트 재고 관리 등의 도구를 통해 로지스틱스의 자동화를 지원합니다. 예를 들어 2024년 9월 독일을 거점으로 하는 업계 단체인 국제로봇연맹(IFR)은 2023년 세계 공장에서 가동되는 로봇 대수가 428만 1,585대에 달했으며, 2022년 기록한 390만 4,000대에서 10% 증가했다고 보고했습니다. 그 결과, 물류의 자동화가 진행되어, 공급망 관리에서 머신러닝 시장의 성장에 기여하고 있습니다.

공급망 관리용 머신러닝 시장의 주요 기업은 의사결정을 최적화하고 업무를 개선하며 전반적인 효율성을 높이기 위해 공급망 관리를 위한 AI 기반 어시스턴트와 같은 고급 기술 솔루션 개발에 주력하고 있습니다. 공급망 관리용 AI 어시스턴트란 예측, 재고관리, 물류계획 등의 공급망 기능을 자동화하고 최적화하기 위해 인공지능을 사용하는 지능형 소프트웨어 도구입니다. 예를 들어, 2024년 2월, 미국에 거점을 둔 디지털 공급망 솔루션 공급업체인 One Network Enterprises사는 공급망 관리용으로 설계된 혁신적인 AI 도구인 NEO Assistant를 발표했습니다. 이 플랫폼은 AI와 머신러닝(ML) 기술을 모두 결합해 실시간 모니터링, 스마트 처방전, 인터랙티브 시각화를 제공한다. AI 주도 인사이트 및 ML 기반 예측 분석을 융합함으로써 NEO Assistant는 복잡한 물류 네트워크 전체의 의사결정과 업무 효율을 향상시킵니다. NEO 어시스턴트는 사용자에게 실용적인 제안과 간소화된 문제 해결 능력을 제공하여 역동적인 공급망 환경 관리에 매우 효과적입니다.

목차

제1장 주요 요약

제2장 시장 특징

제3장 시장 동향 및 전략

제4장 시장-금리, 인플레이션, 지정학, 무역전쟁 및 관세, 코로나 시기 및 회복이 시장에 미치는 영향을 포함한 거시경제 시나리오

제5장 세계의 성장 분석 및 전략 분석 프레임워크

제6장 시장 세분화

제7장 지역별 및 국가별 분석

제8장 아시아태평양 시장

제9장 중국 시장

제10장 인도 시장

제11장 일본 시장

제12장 호주 시장

제13장 인도네시아 시장

제14장 한국 시장

제15장 서유럽 시장

제16장 영국 시장

제17장 독일 시장

제18장 프랑스 시장

제19장 이탈리아 시장

제20장 스페인 시장

제21장 동유럽 시장

제22장 러시아 시장

제23장 북미 시장

제24장 미국 시장

제25장 캐나다 시장

제26장 남미 시장

제27장 브라질 시장

제28장 중동 시장

제29장 아프리카 시장

제30장 경쟁 구도와 기업 프로파일

제31장 기타 주요 기업 및 혁신 기업

제32장 세계 시장 경쟁 벤치마킹 및 대시보드

제33장 주요 인수합병(M&A)

제34장 최근 시장 동향

제35장 시장의 잠재력이 높은 국가, 부문 및 전략

제36장 부록

AJY
영문 목차

영문목차

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.

Reasons to Purchase

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.

Scope

Table of Contents

1. Executive Summary

2. Machine Learning In Supply Chain Management Market Characteristics

3. Machine Learning In Supply Chain Management Market Trends And Strategies

4. Machine Learning In Supply Chain Management Market - Macro Economic Scenario Including The Impact Of Interest Rates, Inflation, Geopolitics, Trade Wars and Tariffs, And Covid And Recovery On The Market

5. Global Machine Learning In Supply Chain Management Growth Analysis And Strategic Analysis Framework

6. Machine Learning In Supply Chain Management Market Segmentation

7. Machine Learning In Supply Chain Management Market Regional And Country Analysis

8. Asia-Pacific Machine Learning In Supply Chain Management Market

9. China Machine Learning In Supply Chain Management Market

10. India Machine Learning In Supply Chain Management Market

11. Japan Machine Learning In Supply Chain Management Market

12. Australia Machine Learning In Supply Chain Management Market

13. Indonesia Machine Learning In Supply Chain Management Market

14. South Korea Machine Learning In Supply Chain Management Market

15. Western Europe Machine Learning In Supply Chain Management Market

16. UK Machine Learning In Supply Chain Management Market

17. Germany Machine Learning In Supply Chain Management Market

18. France Machine Learning In Supply Chain Management Market

19. Italy Machine Learning In Supply Chain Management Market

20. Spain Machine Learning In Supply Chain Management Market

21. Eastern Europe Machine Learning In Supply Chain Management Market

22. Russia Machine Learning In Supply Chain Management Market

23. North America Machine Learning In Supply Chain Management Market

24. USA Machine Learning In Supply Chain Management Market

25. Canada Machine Learning In Supply Chain Management Market

26. South America Machine Learning In Supply Chain Management Market

27. Brazil Machine Learning In Supply Chain Management Market

28. Middle East Machine Learning In Supply Chain Management Market

29. Africa Machine Learning In Supply Chain Management Market

30. Machine Learning In Supply Chain Management Market Competitive Landscape And Company Profiles

31. Machine Learning In Supply Chain Management Market Other Major And Innovative Companies

32. Global Machine Learning In Supply Chain Management Market Competitive Benchmarking And Dashboard

33. Key Mergers And Acquisitions In The Machine Learning In Supply Chain Management Market

34. Recent Developments In The Machine Learning In Supply Chain Management Market

35. Machine Learning In Supply Chain Management Market High Potential Countries, Segments and Strategies

36. Appendix

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