Machine Learning in Logistics

Global Machine Learning in Logistics Market to Reach US$10.7 Billion by 2030

The global market for Machine Learning in Logistics estimated at US$3.3 Billion in the year 2024, is expected to reach US$10.7 Billion by 2030, growing at a CAGR of 21.5% over the analysis period 2024-2030. Software Component, one of the segments analyzed in the report, is expected to record a 23.6% CAGR and reach US$7.8 Billion by the end of the analysis period. Growth in the Services Component segment is estimated at 16.9% CAGR over the analysis period.

The U.S. Market is Estimated at US$910.3 Million While China is Forecast to Grow at 29.1% CAGR

The Machine Learning in Logistics market in the U.S. is estimated at US$910.3 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$2.6 Billion by the year 2030 trailing a CAGR of 29.1% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 16.9% and 19.4% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 18.1% CAGR.

Global Machine Learning in Logistics Market – Key Trends & Drivers Summarized

Why Is Machine Learning in Logistics Gaining Popularity?

The logistics industry is undergoing a digital transformation, with machine learning playing a pivotal role in optimizing operations, reducing costs, and enhancing efficiency. Traditional supply chain management systems often struggle with real-time demand forecasting, route optimization, and inventory management. Machine learning algorithms, powered by big data analytics, are improving accuracy in logistics planning, helping companies minimize delays and optimize fleet utilization. As global e-commerce continues to expand and same-day delivery expectations rise, the demand for AI-driven logistics solutions is growing rapidly.

How Are Innovations Enhancing the Performance of Machine Learning in Logistics?

Recent advancements in AI, predictive analytics, and autonomous decision-making systems are revolutionizing logistics management. Machine learning models analyze historical shipping data, weather patterns, and traffic conditions to optimize delivery routes and reduce fuel consumption. AI-powered demand forecasting helps companies anticipate inventory needs, preventing stockouts and overstock situations. Additionally, robotic process automation (RPA) is streamlining warehouse operations by enhancing picking, packing, and sorting efficiency. Innovations in AI-driven chatbots and virtual assistants are also improving customer service by providing real-time shipment tracking updates and automated responses to inquiries.

What Are the Key Market Drivers?

The rapid growth of e-commerce, increasing adoption of cloud-based logistics platforms, and rising demand for cost-efficient supply chain solutions are driving market expansion. Logistics companies are also investing in AI-powered autonomous delivery vehicles and drone-based last-mile delivery solutions to improve operational efficiency. Additionally, sustainability concerns are pushing companies to use machine learning for optimizing fuel consumption and reducing carbon footprints, aligning with global environmental goals.

What Challenges and Future Opportunities Exist?

Challenges include the high cost of AI integration, data privacy concerns, and resistance to technological change among traditional logistics providers. However, future opportunities lie in expanding AI-driven predictive maintenance for fleet management, developing AI-powered blockchain logistics solutions for enhanced transparency, and integrating 5G-enabled smart logistics networks. The continued evolution of AI-powered autonomous warehouses and smart transportation systems will further redefine the logistics industry.

SCOPE OF STUDY:

The report analyzes the Machine Learning in Logistics market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Component (Software Component, Services Component); Deployment (Cloud-based Deployment, On-Premise Deployment); Organization (Large Enterprises, Small & Medium-Sized Enterprises); Application (Inventory Management Application, Supply Chain Planning Application, Transportation Management Application, Warehouse Management Application, Fleet Management Application, Risk & Security Application, Other Applications); End-User (Retail & E-commerce End-User, Manufacturing End-User, Healthcare End-User, Automotive End-User, Food & Beverage End-User, Consumer Goods End-User, Other End-Users)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.

Select Competitors (Total 43 Featured) -

• Aera Technology
• Amazon
• Blue Yonder
• Covariant
• DataArt
• Everstream Analytics
• Flowspace
• FourKites
• HERE Technologies
• Nauto
• Noodle.ai
• Nuro
• Orkestra SCS
• Osa Commerce
• Samsara
• Samsung SDS
• Shippeo
• Shipsy
• Shipwell
• Transmetrics

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TARIFF IMPACT FACTOR
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