Machine Learning in Supply Chain Management

Global Machine Learning in Supply Chain Management Market to Reach US$8.7 Billion by 2030

The global market for Machine Learning in Supply Chain Management estimated at US$2.1 Billion in the year 2024, is expected to reach US$8.7 Billion by 2030, growing at a CAGR of 26.9% over the analysis period 2024-2030. Software Component, one of the segments analyzed in the report, is expected to record a 23.8% CAGR and reach US$5.1 Billion by the end of the analysis period. Growth in the Services Component segment is estimated at 32.5% CAGR over the analysis period.

The U.S. Market is Estimated at US$549.2 Million While China is Forecast to Grow at 25.7% CAGR

The Machine Learning in Supply Chain Management market in the U.S. is estimated at US$549.2 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$1.3 Billion by the year 2030 trailing a CAGR of 25.7% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 24.2% and 23.6% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 18.9% CAGR.

Global Machine Learning in Supply Chain Management Market – Key Trends & Drivers Summarized

Why Is Machine Learning in Supply Chain Management Gaining Popularity?

Supply chain management is becoming increasingly complex due to global disruptions, fluctuating demand, and increasing customer expectations for fast and efficient deliveries. Machine learning is transforming supply chain operations by providing real-time insights, improving demand forecasting, and optimizing inventory levels. With the rise of e-commerce, just-in-time manufacturing, and multi-channel distribution models, businesses are leveraging AI-powered analytics to enhance supply chain visibility and minimize risks.

How Are Innovations Enhancing the Performance of Machine Learning in Supply Chain Management?

Advancements in AI-driven analytics, cloud computing, and automation are making supply chains more resilient and agile. Machine learning algorithms are being used for predictive analytics to anticipate demand fluctuations, detect anomalies, and optimize warehouse inventory levels. AI-driven route optimization tools are improving logistics efficiency, reducing transportation costs, and enhancing last-mile delivery accuracy. Blockchain-integrated AI solutions are also improving supply chain transparency, enabling real-time tracking of goods from manufacturers to end consumers.

What Are the Key Market Drivers?

The increasing complexity of global supply chains, rising consumer expectations for faster deliveries, and the need for improved risk management are key factors driving market growth. The COVID-19 pandemic has further accelerated the adoption of AI-powered supply chain solutions as companies seek to mitigate disruptions and enhance operational efficiency. Additionally, sustainability concerns and regulatory compliance requirements are pushing companies to adopt AI-driven solutions that optimize energy consumption and reduce waste.

What Challenges and Future Opportunities Exist?

Challenges include the high cost of AI implementation, data integration issues across supply chain networks, and cybersecurity risks. However, opportunities exist in the expansion of AI-driven supply chain risk management solutions, the development of self-learning algorithms for automated decision-making, and the adoption of AI-powered sustainability tracking tools. The continued evolution of AI in autonomous warehouse management, robotic supply chain operations, and AI-driven procurement strategies will further revolutionize supply chain management.

SCOPE OF STUDY:

The report analyzes the Machine Learning in Supply Chain Management 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 (Demand Forecasting Application, Supplier Relationship Management Application, Risk Management Application, Product Lifecycle Management Application, Sales & Operations Planning 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; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 48 Featured) -

• Accenture plc
• Aera Technology
• Amazon Web Services (AWS)
• Blue Yonder
• Coupa Software
• DataArt
• Flowspace
• FourKites
• IBM Corporation
• Lokad
• Microsoft Corporation
• Noodle.ai
• Oracle Corporation
• Osa Commerce
• Project44
• Samsara
• SAP SE
• Shipsy
• Shipwell
• Transmetrics

AI INTEGRATIONS
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TARIFF IMPACT FACTOR
Our new release incorporates impact of tariffs on geographical markets as we predict a shift in competitiveness of companies based on HQ country, manufacturing base, exports and imports (finished goods and OEM). This intricate and multifaceted market reality will impact competitors by increasing the Cost of Goods Sold (COGS), reducing profitability, reconfiguring supply chains, amongst other micro and macro market dynamics.

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