Kings Research today announced the release of its latest market intelligence study, “Global Machine Learning in Manufacturing Market: Size, Share, Trends & Forecast 2023–2030.” The report provides an in-depth analysis of market size, growth drivers, technological trends, regional outlook, and competitive dynamics.
According to Kings Research, the global machine learning in manufacturing market was USD 921.3 million in 2022 and is projected to reach USD 8,776.7 million by 2030 at a CAGR of 33.35%. The market is driven by increasing adoption of IoT devices, AI, and machine learning algorithms to optimize production processes, improve product quality, enable predictive maintenance, and enhance operational efficiency across manufacturing operations.
Machine learning in manufacturing leverages data from sensors, machines, and production lines to optimize processes, predict equipment failures, improve quality control, and enhance supply chain efficiency. These solutions enable manufacturers to reduce downtime, minimize waste, and maintain safety, while designing products aligned with market demand.
Kings Research identifies the following drivers for the machine learning in manufacturing market
- IoT and Industry 4.0 Adoption: Integration of IoT, cloud computing, and AI in manufacturing operations enhances efficiency, predictive maintenance, and real-time decision-making.
- Predictive Maintenance: Machine learning algorithms analyze equipment data to prevent failures, reducing maintenance costs and downtime.
- Quality Control Enhancement: AI-driven analysis of sensor data detects defects early, ensuring consistent product quality.
- R&D and Product Design: Machine learning assists in analyzing market trends and customer feedback to optimize new product development.
- Government Initiatives and Industry Collaboration: The U.S. National Strategy for Advanced Manufacturing (NSAM) promotes adoption of advanced technologies, including AI and machine learning, to ensure competitiveness in domestic manufacturing (Source: manufacturing.gov).
B2B leaders in automotive, electronics, heavy machinery, and semiconductor manufacturing can benefit from machine learning through
- Higher Productivity: Predictive analytics and optimized workflows reduce downtime and accelerate production.
- Better Accuracy: AI-driven quality control improves product reliability and reduces defects.
- Operational Efficiency: Integration of ML into manufacturing processes streamlines operations and reduces costs.
- Cost Savings: Predictive maintenance and optimized resource allocation lower operational expenditure.
Regional Outlook
- North America: Largest market with a 35.15% share (USD 323.8 million in 2023), driven by early adoption of AI and machine learning in industrial operations and government-led initiatives for advanced manufacturing.
- Europe: Fastest-growing region, expected to grow at a CAGR of 34.5%, fueled by Industry 4.0 adoption, smart manufacturing initiatives, and investment in AI-driven industrial automation.
Competitive Landscape
The global market is fragmented, with companies pursuing partnerships, mergers & acquisitions, and product innovations to expand portfolios. Key players include Rockwell Automation, Robert Bosch GmbH, Intel Corporation, Siemens, General Electric, Microsoft, Sight Machine, SAP SE, and IBM.
The full Kings Research report offers detailed segmentation by production stage, job function, application, and region, along with competitive benchmarking and industry forecasts. To request a sample report or access the complete study, visit https://www.kingsresearch.com/machne-learning-in-manufacturing-market-22.
About Kings Research
Kings Research is a global provider of syndicated research reports and consulting services, enabling organizations to navigate emerging markets, assess opportunities, and make informed strategic decisions.
All market data are sourced from Kings Research proprietary analysis, validated against credible sources and industry publications. Key references include the U.S. Department of Commerce (www.commerce.gov), and the U.S. National Institute of Standards and Technology (www.nist.gov).