Artificial Intelligence in Manufacturing Market Is Predicted to See Lucrative Gains By 2030

The market size for artificial intelligence in manufacturing is projected to be USD 3.1 billion in 2023 and USD 21.1 billion by 2030, growing at a CAGR of 45.9% over the forecast period. The development of industrial IoT and automation technology is credited with the market expansion.

Key Market Players: Alphabet, Inc. (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), Amazon Web Services, Inc. (U.S.), Siemens AG (Germany), General Electric Company (U.S.), SAP SE (Germany), Robert Bosch GmbH (Germany), Cisco Systems, Inc. (U.S.), Rockwell Automation, Inc. (U.S.), Advanced Micro Devices, Inc. (U.S.), and Sight Machine Inc. (U.S.) are among others

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Segmentation:
By Offering
Hardware
Software
Services

By Technology
Computer Vision
Machine Learning
Natural Language Processing
Context Awareness

By Application
Process Control
Production Planning
Predictive Maintenance & Machinery Inspection
Logistics and Inventory Management
Quality Management
Others

By Industry
Automotive
Medical Devices
Semiconductor & Electronics
Energy & Power
Heavy Metal & Machine Manufacturing
Others (Aerospace &Defense, Conglomerates, etc.,)

By Region
North America (U.S. and Canada)
Europe (U.K., Germany, France, Spain and Rest of Europe)
Asia Pacific (Japan, China, India, Southeast Asia and Rest of Asia Pacific)
Middle East & Africa (South Africa, GCC and Rest of Middle East & Africa)
Latin America (Brazil, Mexico and Rest of Latin America)

Recent Developments
• In August 2023, NVIDIA Corporation announced NVIDIA OVX Servers featuring the new NVIDIA® L40S GPU, a powerful, universal data center processor designed to accelerate the most compute-intensive, complex applications, including AI training and inference, 3D design and visualization, video processing and industrial digitalization with the NVIDIA Omniverse platform.
• In January 2023, Intel Corporation Launched 4th Gen Xeon Scalable Processors, Max Series CPUs and GPUs. These processors are Intel's most sustainable data center processors, delivering a range of features for optimizing power and performance, making optimal use of CPU resources to help achieve customers' sustainability goals.
• In november 2022, IBM announced new software designed to help enterprises break down data and analytics silos so they can make data-driven decisions quickly and navigate unpredictable disruptions. IBM Business Analytics Enterprise is a suite of business intelligence planning, budgeting, reporting, forecasting, and dashboard capabilities that provides users with a robust view of data sources across their entire business.

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Artificial Intelligence in Manufacturing market insights:
Predictive Maintenance: AI has played a crucial role in predictive maintenance in manufacturing. By analyzing data from sensors and equipment, AI can predict when machines are likely to fail, allowing for proactive maintenance. This reduces downtime and saves costs.

Quality Control and Assurance: AI-powered computer vision systems can inspect and analyze products in real-time, identifying defects or inconsistencies in the manufacturing process. This ensures higher product quality and reduces the need for manual inspection.

Supply Chain Optimization: AI can optimize supply chain operations by predicting demand, managing inventory levels, and identifying the most efficient routes for transportation. This leads to cost savings and improved efficiency.
Process Optimization: Machine learning algorithms can analyze data from various stages of manufacturing to identify bottlenecks, inefficiencies, and opportunities for improvement. This can result in optimized production processes and increased productivity.

Robotic Automation: AI-driven robots and robotic systems are increasingly being used in manufacturing for tasks such as material handling, assembly, and welding. These robots can work alongside human workers or in autonomous environments, enhancing efficiency and precision.
Customization and Personalization: AI can enable mass customization by allowing manufacturers to produce customized products at scale. This is particularly relevant in industries like automotive and consumer electronics.

Energy Efficiency: AI can help reduce energy consumption in manufacturing facilities by optimizing equipment usage and identifying energy-saving opportunities.

Safety: AI-powered systems can enhance safety in manufacturing by monitoring worker behavior, identifying potential safety hazards, and ensuring compliance with safety protocols.

Data Analytics: Manufacturers are increasingly using AI for data analytics to gain insights into production processes, customer preferences, and market trends. This data-driven approach can inform decision-making and strategy

Human-Machine Collaboration: Collaborative robots (cobots) that use AI can work alongside human workers, handling repetitive or dangerous tasks and improving overall productivity.

AI Platforms: Various AI platforms and solutions tailored for manufacturing have emerged, making it easier for manufacturers to implement AI technologies in their operations.

Regulatory Compliance: AI can help manufacturers ensure compliance with industry-specific regulations and quality standards by monitoring and documenting processes.

Cost Reduction: Over time, AI implementations in manufacturing can lead to cost reductions through improved efficiency, reduced waste, and optimized resource allocation.
Global Adoption: AI in Manufacturing is a global trend, with companies in various countries investing in AI technologies to remain competitive on a global scale.

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This release was published on openPR.