New York, April 25, 2022 (GLOBE NEWSWIRE) — Reportlinker.com announces the release of the report “Artificial Intelligence in Manufacturing Market by Supply, Industry, Application, Technology and Region – Global Forecast to 2027” – https://www.reportlinker.com/p05048444/?utm_source=GNW
GPU/CPU manufacturers, such as NVIDIA, AMD, Intel, Qualcomm, Huawei, and Samsung, have invested heavily in AI hardware for the development of chipsets compatible with AI-based technologies and solutions.
In addition to CPUs and GPUs, application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) are being developed for AI applications. For example, Google has built a new ASIC called “Tensor Processing Unit” (TPU).
The compute-intensive chipset is among the critical parameters for processing AI algorithms; the faster the chipset, the faster it can process the data needed to create an AI system. Currently, AI chipsets are mostly deployed in high-end data centers/servers, as end-computers are currently unable to handle such large workloads and do not have enough power and delay.
NVIDIA offers a line of GPUs that provide application-based GPU memory bandwidth. For example, the GeForce GTX Titan X offers 336.5 GB/s memory bandwidth and is mostly deployed on desktop computers, while the 16 GB Tesla V100 offers 900 GB/s memory bandwidth and is used in AI applications.
Application of AI for smart business processes
Rigid, rule-based software currently governs the majority of an organization’s business processes, providing limited capabilities to manage critical issues. These processes take time and force employees to work on repetitive tasks, which hinders employee productivity and overall performance. organisation.
Machine learning and natural language processing tools generated on AI platforms can help companies overcome these challenges through self-learning algorithms, which can reveal new patterns and solutions. Most organizations use enterprise software, which uses rules-based processing to automate business processes. .
This task-based automation has helped organizations improve productivity in a few specific processes, but such rule-based software cannot self-learn and improve with experience. software systems, allow software to gain control while solving individual processes.
This software would be able to improve business performance and productivity over time, instead of providing a one-time boost. All these factors are said to have fueled the demand for intelligent business processes and provided opportunities for the growth of AI in the manufacturing market.
Rising global demand for energy and power is driving energy and power companies to adopt AI-based solutions
Growing global demand for energy and power is driving energy and power companies to adopt AI-based solutions that can help increase production with minimal maintenance and reduced downtime. Maintenance and inspection are the main issues, along with the movement of materials, in a thermal power plant. because the material has to travel a long distance inside the factory.
In addition, the equipment used in this industry, such as turbines, conveyor belts, networks and voltage transformers, is expensive. In addition, there are issues related to the fuel mix, ambient temperature, air quality, humidity, load, weather forecast models and the market. pricing in the electricity sector.
By using AI-based technologies, these problems can be solved and predicted from the earliest stages. AI-based technologies used in power plants include physics knowledge, engineering design knowledge, and new inspection technologies, which are ideal for predictive maintenance and machine inspections.
AI technologies work in 2 layers. First, by recognizing the pattern, and second, by learning the patterns. Early pattern recognition signals impending failures.
The breakdown of primaries conducted during the study is shown below:
• By type of business: Level 1: 55%, Level 2: 25% and Level 3: 20%
• By designation: C-level executives – 60%, directors – 20% and others – 20%
• By region: North America – 40%, Europe – 30%, APAC – 20%, South America – 7% and Middle East and Africa – 3%
Major players operating in the Artificial Intelligence in Manufacturing market are NVIDIA (US), IBM (US), Intel (US), Siemens (Germany), General Electric (US), Google (US), Microsoft Corporation (US) and Micron Technology (US).
The report segments the Artificial Intelligence in Manufacturing market and forecasts its size, by value, based on region (North America, Europe, Asia-Pacific, and RoW), application (predictive maintenance and inspection of machines, inventory optimization, production planning, field services, quality control, cybersecurity, industrial robots and recovery), Technology (machine learning, natural language processing, contextual computing, computer vision), Supply (hardware, software & Services) and Industrial (Automotive, Energy & Power, Semiconductor & Electronics, Pharmaceutical, Heavy Metal & Machinery Manufacturing, Food & Beverage and Others (Textile, Aerospace & Mining)). The report also provides a comprehensive review of market drivers, restraints, opportunities, and challenges in the Head-Up Display market.
The report also covers the qualitative aspects in addition to the quantitative aspects of these markets.
Key Benefits of Purchasing This Report
• This report includes market statistics related to supply, technology, industry, application and region.
• An in-depth value chain analysis has been carried out to provide an in-depth insight into the Artificial Intelligence in Manufacturing market.
• Major market drivers, restraints, challenges and opportunities have been detailed in this report.
• Illustrative segmentation, analysis, and forecast for the market based on supply, technology, industry, application, and region has been performed to provide a comprehensive view of the Artificial Intelligence market in the making.
• The report includes an in-depth analysis and ranking of key players.
Read the full report: https://www.reportlinker.com/p05048444/?utm_source=GNW
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