Dublin, Oct. 28, 2022 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Manufacturing Market by Offering (Hardware, Software, and Services), Industry, Application, Technology (Machine Learning, Natural Language Processing, Context-aware Computing, Computer Vision), & Region - Global Forecast to 2027" report has been added to ResearchAndMarkets.com's offering.
The global artificial intelligence in Manufacturing market size is valued at USD 2.3 billion in 2022 and is anticipated to be USD 16.3 billion by 2027; growing at a CAGR of 47.9% from 2022 to 2027. The growing demand of factors such as improving computing power of AI chipsets is expected to grow the market at an estimated rate.
The increasing adoption of AI has been observed as a new driver for semiconductor chipset manufacturers in the past few years. GPU/CPU manufacturers, such as NVIDIA, AMD, Intel, Qualcomm, Huawei, and Samsung, have significantly invested in AI hardware for the development of chipsets that are compatible with AI-based technologies and solutions. Apart from CPUs and GPUs, application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) are being developed for AI applications. For instance, Google has built a new ASIC called "tensor processing unit" (TPU).
Compute-intensive chipset is among the critical parameters for processing AI algorithms; the faster the chipset, the quicker it can process the data required to create an AI system. Currently, AI chipsets are mostly deployed in data centers/high-end servers as end computers are currently incapable of handling such huge workloads and do not have enough power and time frame. NVIDIA has a range of GPUs that offer GPU memory bandwidth based on application. For example, GeForce GTX Titan X offers a memory bandwidth of 336.5 GB/s and is mostly deployed on desktops, while Tesla V100 16 GB offers a memory bandwidth of 900 GB/s and is used in AI applications.
Application of AI for intelligent business processes
Rigid and rule-based software currently governs a majority of business processes in an organization, offering limited abilities to handle critical problems. These processes are time-consuming and require employees to work on repetitive tasks, hampering the productivity of the employees and the overall performance of the organization. Machine Learning and Natural Language Processing tools generated on AI platforms can help enterprises overcome such challenges with self-learning algorithms, which can reveal new patterns and solutions.
Most organizations use enterprise software, which makes the use of rule-based processing to automate business processes. This task-based automation has helped organizations in improving their productivity in a few specific processes but such rule-based software cannot self-learn and improve with experience.
The integration of AI tools, such as NLP and ML, generated on AI platform for enterprise software systems, enables the software to gain mastery while solving individual processes. This software would be able to provide improved performance and productivity to enterprises over time, instead of providing a one-time boost. All these factors are said to have fueled the demand for intelligent business processes and act as opportunities for the growth of the AI in manufacturing market.
Increasing global demand for energy and power is influencing energy and power companies to adopt AI-based solutions
The increasing global demand for energy and power is influencing energy and power companies to adopt AI-based solutions that can help boost production output with minimum maintenance and reduced downtime. Maintenance and inspection are the major issues, along with material movement, in a thermal plant as the material needs to travel a long distance inside the plant. Besides, equipment used in this industry, such as turbines, conveyer belts, grids, and voltage transformers, are costly.
Moreover, there are issues related to fuel mix, ambient temperature, air quality, moisture, load, weather forecast models, and market pricing in the power industry. By using AI-based technologies, these issues can be resolved and predicted in the early stages.
AI-based technologies used in energy plants comprise physics insights, engineering design knowledge, and new inspection technologies, which are ideal for predictive maintenance and machinery inspections. The AI technologies work in 2 layers. First, by recognizing the pattern, and second, by learning the models. Early-stage pattern recognition notifies about impending failures.
Key Topics Covered:
1.Introduction
2. Research Methodology
3. Executive Summary
4. Premium Insights
4.1 Attractive Opportunities in AI in Manufacturing Market
4.2 AI in Manufacturing Market, by Offering
4.3 AI in Manufacturing Market, by Technology
4.4 Asia-Pacific AI in Manufacturing Market, by Industry and Country
4.5 AI in Manufacturing Market, by Country
5. Market Overview
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers
5.2.1.1 Intensifying Need to Handle Increasingly Large and Complex Dataset
5.2.1.2 Evolving Industrial IoT and Automation Technologies
5.2.1.3 Improving Computing Power of AI Chipsets
5.2.1.4 Increasing Venture Capital Investments in Manufacturing AI Space
5.2.2 Restraints
5.2.2.1 Reluctance Among Manufacturers to Adopt AI-Based Technologies
5.2.3 Opportunities
5.2.3.1 Growing Focus on Boosting Operational Efficiency of Manufacturing Plants
5.2.3.2 Application of AI for Intelligent Business Processes
5.2.3.3 Adoption of Automation Technologies to Mitigate Effects of COVID-19
5.2.4 Challenges
5.2.4.1 Limited Availability of Skilled Workforce, Especially in Developing Countries
5.2.4.2 Concerns Regarding Data Privacy
5.3 Porter's Five Forces Analysis
5.4 Pricing Analysis
5.5 Trade Analysis
5.5.1 Export Scenario of Automatic Data Processing Machines
5.5.2 Import Scenario of Automatic Data Processing Machines
5.6 Tariffs and Regulatory Landscape
5.6.1 Regulatory Bodies, Government Agencies, and Other Organizations
5.7 AI Ecosystem
5.8 AI in Manufacturing Market: Case Studies
5.8.1 Siemens Gamesa Uses Fujitsu's AI Solution to Accelerate Inspection of Turbine Blades
5.8.2 Volvo Uses Machine Learning-Driven Data Analytics for Predicting Breakdown and Failures
5.8.3 Rolls-Royce is Using Microsoft Cortana Intelligence for Predictive Maintenance
5.8.4 Paper Packaging Firm Used Sight Machine's Enterprise Manufacturing Analytics to Improve Production
5.9 Patent Analysis
5.10 Revenue Shift in AI in Manufacturing Market
5.11 Regulatory Standards
5.11.1 Standards in Its/C-Its
5.12 Supply Chain Analysis
5.13 Technology Analysis
5.14 Key Conferences and Events in 2022-2023
5.15 Key Stakeholder and Buying Process And/Or Buying Criteria
6. Artificial Intelligence (AI) in Manufacturing Market, by Offering
6.1 Introduction
6.2 Hardware
6.2.1 Processor
6.2.1.1 High Parallel Processing Capabilities and Computing Power to Fuel Adoption of Processors
6.2.1.1.1 Microprocessor Unit (MPU)
6.2.1.1.2 Graphics Processing Unit (Gpu)
6.2.1.1.3 Field Programmable Gate Array (Fpga)
6.2.1.1.4 Application-Specific Integrated Circuit (Asic)
6.2.2 Memory
6.2.2.1 Rising High-Bandwidth Memory Requirements to Drive Market Growth
6.2.3 Network
6.2.3.1 Growing Use of Ethernet Adaptors and Interconnects to Drive Market Growth
6.3 Software
6.3.1 AI Solutions
6.3.1.1 Rising Use of List Processing and Programming in Logic Languages to Fuel Adoption of AI Solutions
6.3.1.2 On-Premise
6.3.1.3 Cloud
6.3.2 AI Platform
6.3.2.1 Natural Language Processing, Image Recognition, Voice Recognition, and Predictive Analytics Features to Fuel Adoption of AI Platforms
6.3.2.2 Machine Learning Framework
6.3.2.3 Application Program Interface (Api)
6.4 Services
6.4.1 Deployment and Integration
6.4.1.1 Increasing Demand for Deployment and Integration as a Key Service for Configuring AI Systems in Manufacturing to Drive Market Growth
6.4.2 Support and Maintenance
6.4.2.1 Growing Demand for Support and Maintenance Service to Eliminate Issues Related to Operations After Installation and Training to Drive Market Growth
7. Artificial Intelligence (AI) in Manufacturing Market, by Technology
7.1 Introduction
7.2 Machine Learning
7.2.1 Advancements in Deep Learning and Supervised Learning Technologies to Drive Market Growth
7.2.2 Deep Learning
7.2.2.1 Rapid Adoption of Robotics in Manufacturing Industry to Drive Growth of AI in Manufacturing Market for Deep Learning
7.2.3 Supervised Learning
7.2.3.1 Image Recognition and Predictive and Predictive Analytics Applications to Play Major Role in Market Growth
7.2.4 Reinforcement Learning
7.2.4.1 Ability of Reinforcement Learning to Maximize Performance of Systems and Software to Drive Market Growth
7.2.5 Unsupervised Learning
7.2.5.1 Capability of Unsupervised Learning to Find Patterns in Large Datasets to Drive Market Growth
7.2.6 Others
7.3 Natural Language Processing
7.3.1 Developments in Natural Language Processing for Real-Time Translation to Drive Market Growth
7.4 Context-Aware Computing
7.4.1 Development of Sophisticated Hard and Soft Sensors to Boost Growth of Context-Aware Computing Segment
7.5 Computer Vision
7.5.1 Capability of Computer Vision to Analyze Information of Different Geometric Shapes, Volumes, and Patterns and Provide Visual Feedback to Users to Fuel Market Growth
8. Artificial Intelligence (AI) in Manufacturing Market, by Application
8.1 Introduction
8.2 Predictive Maintenance and Machinery Inspection
8.2.1 Extensive Use of Computer Vision and Machine Learning Technologies in Predictive Maintenance and Machinery Inspection Application to Drive Market Growth
8.3 Inventory Optimization
8.3.1 Capability of AI to Perform In-Plant Logistics in Manufacturing Industry to Fuel Market Growth for Inventory Optimization
8.4 Production Planning
8.4.1 Extensive Use of Big Data in Production Planning Application to Fuel Market Growth
8.5 Field Services
8.5.1 Growing Use of Field Services in Heavy Metals & Machine Manufacturing, Oil & Gas, and Energy & Power Industries to Drive Market Growth
8.6 Quality Control
8.6.1 Rising Adoption of AI-Based Quality Control Systems in Pharmaceutical, Food & Beverage, and Semiconductor Industries to Drive Market Growth
8.7 Cybersecurity
8.7.1 Growing Adoption of Automation in Work Processes to Drive Market Growth
8.8 Industrial Robots
8.8.1 Rising Adoption of Industrial Robots in Manufacturing Industry to Accelerate Production Processes, Enhance Efficiency, and Minimize Production Costs to Drive Market Growth
8.9 Reclamation
8.9.1 Growing Adoption of Computer Vision and Machine Learning Technologies in Reclamation Application to Eliminate Human-Machine Interaction to Drive Market Growth
9 Artificial Intelligence (AI) in Manufacturing Market, by Industry
9.1 Introduction
9.2 Automotive
9.2.1 Increased Deployment of Machine Learning and Computer Vision Technologies in Automotive Industry to Drive Market Growth
9.3 Energy & Power
9.3.1 Growing Use of AI-Based Solutions in Energy & Power Industry to Increase Production Output and Reduce Downtime to Drive Market Growth
9.4 Pharmaceutical
9.4.1 Extensive Usage of Computer Vision Technology for Quality Control Application in Pharmaceutical Indutry to Drive Market Growth
9.5 Heavy Metals & Machine Manufacturing
9.5.1 Increased Use of Robotics in Heavy Metals & Machine Manufacturing Industry to Drive Market Growth
9.6 Semiconductor & Electronics
9.6.1 Increased Use of Computer Vision Technology in Semiconductor & Electronics Industry to Drive Market Growth
9.7 Food & Beverage
9.7.1 Growing Adoption of Industrial Robots, IoT, and Big Data in Food & Beverage Industry to Fuel Market Growth
9.8 Others
10. Geographic Analysis
11. Competitive Landscape
11.1 Introduction
11.2 Revenue Analysis: Top Companies
11.3 Strategies Adopted by Key Players
11.4 Market Share Analysis, 2021
11.5 Company Evaluation Quadrant
11.5.1 Star
11.5.2 Pervasive
11.5.3 Emerging Leader
11.5.4 Participant
11.6 AI in Manufacturing Market: Company Footprint
11.6.1 Application and Regional Footprint of Top Players
11.7 Small and Medium Enterprises (SME) Evaluation Quadrant, 2021
11.7.1 Progressive Company
11.7.2 Responsive Company
11.7.3 Dynamic Company
11.7.4 Starting Block
11.8 Competitive Situations & Trends
11.9 Competitive Benchmarking
12. Company Profile
12.1 Key Players
12.1.1 Nvidia
12.1.2 IBM
12.1.3 Intel
12.1.4 Siemens
12.1.5 General Electric (Ge) Company
12.1.6 Google
12.1.7 Microsoft Corporation
12.1.8 Micron Technology
12.1.9 Amazon Web Services (Aws)
12.1.10 Sight Machine
12.2 Other Companies
12.2.1 Progress Software Corporation (Datarpm)
12.2.2 AIbrain
12.2.3 General Vision
12.2.4 Rockwell Automation
12.2.5 Cisco Systems
12.2.6 Mitsubishi Electric
12.2.7 Oracle
12.2.8 Sap
12.2.9 Vicarious
12.2.10 Ubtech Robotics
12.2.11 Aquant
12.2.12 Bright Machines
12.2.13 Rethink Robotics GmbH
12.2.14 Sparkcognition
12.2.15 Flutura
13. Appendix
For more information about this report visit https://www.researchandmarkets.com/r/wn87vo
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