What's driving the Artificial Intelligence in Manufacturing market Share? Advanced Micro Devices, Canvas Analytics, General Vision, Graphcore, IBM, Landing AI, SAP, Sight Machine, UBTECH Robotics, Oracle, Progress Software, Qualcomm, Microsoft, Nvidia, In

MarketStudyReport.com present the report on global Artificial Intelligence in Manufacturing Market report evaluates the growth trends of the industry through historical study and estimates future prospects based on comprehensive research. The report extensively provides the market share, growth, trends and forecasts for the period 2019-2025.

Asia Pacific is leading in AI in the manufacturing landscape with over 43% market share in 2018. The market growth is attributed to the presence of highly advanced manufacturing facilities in countries including China, Japan, and South Korea. The widespread adoption of industry 4.0 technologies is also promoting the use of AI solutions. Europe?s AI in the manufacturing market is anticipated to grow at a CAGR of over 44% during the forecast timeframe. High investment by the prominent automakers in the region to incorporate advanced production technologies is adding to the growth of the market.

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The rapid proliferation of industry 4.0 technologies is promoting the growth of AI in the manufacturing market. The increasing need among manufacturers to reduce their operational costs and enhance productivity is the key factor leading to the adoption of industry 4.0. The advanced technologies enhance productivity and reduce the time to market the products. This has enabled the manufacturers to anticipate customer demand and align their operations with customer requirements in real-time. These solutions enable cost-effective and reliable operations without incurring any intensive capital investments.

The hardware market holds over 57% stake in AI in the manufacturing market. The growth is attributed to the rising demand for AI processors across various industry verticals. The GPUs are dominating the AI processors market with over 45% share in the market. The increasing need for high processing capabilities and enhanced visual content is promoting GPUs use in the manufacturing industry. The AI platforms market is predicted to grow exponentially over the projected time period. The rising need for personalized solutions is encouraging manufacturers to incorporate AI technologies into their manufacturing facilities. The AI platforms offer pre-built algorithms with unsophisticated workflows along with features that easily allow the necessary data to connect to end solutions.

The machine learning market accounted for over 47% of the revenue in AI in the manufacturing market in 2018. The technology is used to warrant quality management at every stage of the manufacturing process. It anticipates product quality at early stages of the production with a high level of precision. The computer vision market is expected to grow at a CAGR of 45% over the forecast timespan. The rise in automation and extensive adoption of IoT systems are encouraging its use in the manufacturing industry.

The material movement market is predicted to grow at a CAGR of over 43% during the projected timeframe. As the skilled labor force is becoming increasingly problematic to retain and recruit, the enterprises are shifting toward AI-enabled machine movement solutions to manage labor issues to ensure efficiency and profitability. The predictive maintenance and machine inspection segment in AI in the manufacturing landscape is expected to grow at a CAGR of over 44% during the forecast timeline. There has been an increasing pressure on manufacturing facilities to increase productivity, which has made maintenance a prime factor to enhance cost-savings and efficiency. The cost of machine downtime is anticipated to be over USD 647 billion annually. The technology eradicates the need to periodically shut down machinery to conduct preventive measures.

The energy & power sector is predicted to grow at a CAGR of over 41.2% during the projected time period. The companies are investing in AI technology due to its ability to adapt to changing environments. This allows the power-generation organizations to create accurate predictions regarding energy demand, reducing their reliance on backup mechanisms. The heavy metal & machinery industry is expected to register a growth rate of over 43% during the forecast timeframe. The companies present in the heavy metal machinery sector are using the technology to optimize the raw material consumption and improve melt costs to ensure significant cost savings.

AI in the manufacturing market is anticipated to surpass USD 16 billion by 2025. The rising venture capital investment is propelling the growth of AI in the manufacturing market. In 2016, the global investment in the AI landscape is expected to be over USD 12 billion. The value of the deals has also increased exponentially with over 60% of the investments led by digital natives and technology players. The surge in the availability of digital data is also promoting the use of AI-enabled systems in the manufacturing sector. The is anticipated that by 2020, over 1.7 megabytes of new data will be generated every second. This is encouraging the companies to use AI technologies and advanced data analytics solutions among the manufacturers.

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AI in the manufacturing market is dominated by large players such as Nvidia and Intel. These companies are investing heavily in the development of high-performance enterprise solutions. The start-ups operating in the market are conducting fundraising activities to scale their operations across the foreign markets. For instance, in August 2018, Canvass Analytics raised over USD 5 million from Gradient Ventures to pursue aggressive sales strategies to expand its market in Europe, Asia, and North American regions.

The key vendors in the market are Advanced Micro Devices, Canvas Analytics, General Vision, Graphcore, IBM, Landing AI, SAP, Sight Machine, UBTECH Robotics, Oracle, Progress Software, Qualcomm, Microsoft, Nvidia, Intel, Google, Falkonry, AWS, and Vicarious.

Table of Content:
Chapter 1. Methodology & Scope
1.1. Methodology
1.1.1. Initial data exploration
1.1.2. Statistical model and forecast
1.1.3. Industry insights and validation
1.1.4. Scope
1.1.5. Definitions
Chapter 2. Executive Summary
2.1. AI in manufacturing industry 360 degree synopsis, 2016 – 2025
2.2. Business trends
2.3. Regional trends
2.4. Component trends
Chapter 3. AI in Manufacturing Industry Insights
3.1. Introduction
3.2. Industry segmentation
3.3. Industry landscape, 2016 – 2025
3.3.1. AI processors market
3.3.2. AI in manufacturing market
3.4. Industry ecosystem analysis
3.5. AI in manufacturing evolution
Chapter 4. Competitive Landscape, 2018
4.1. Introduction
4.2. Major market players, 2018
4.2.1. NVIDIA
4.2.2. Intel
4.2.3. IBM
4.2.4. AWS
4.2.5. Google
4.3. Innovation leaders, 2018
4.3.1. Canvass Analytics
4.3.2. Falkonry
4.3.3. Graphcore
4.3.4. Landing AI
Chapter 5. AI in Manufacturing Market, By Component
5.1. Key trends, by component
5.2. Hardware
5.2.1. Market estimates and forecast, 2016 – 2025
5.2.2. Processor
5.2.2.1. Market estimates and forecast, 2016 – 2025
5.2.2.2. Field-Programmable Gate Array (FPGA)
5.2.2.2.1. Market estimates and forecast, 2016 – 2025
5.2.2.3. Application-Specific Integrated Circuit (ASIC)
5.2.2.3.1. Market estimates and forecast, 2016 – 2025
5.2.2.4. Central Processing Unit (CPU)
5.2.2.4.1. Market estimates and forecast, 2016 – 2025
5.2.2.5. Graphics Processing Unit (GPU)
5.2.2.5.1. Market estimates and forecast, 2016 – 2025
5.2.3. Memory
5.2.3.1. Market estimates and forecast, 2016 – 2025
5.3. Solutions
5.3.1. Market estimates and forecast, 2016 – 2025
5.3.2. Software
5.3.2.1. Market estimates and forecast, 2016 – 2025
5.3.3. AI platforms
5.3.3.1. Market estimates and forecast, 2016 – 2025
5.3.4. Application Program Interface (API)
5.3.4.1. Market estimates and forecast, 2016 – 2025
5.4. Services
5.4.1. Market estimates and forecast, 2016 — 2025
5.4.2. Managed services
5.4.2.1. Market estimates and forecast, 2016 – 2025
5.4.3. Professtional services
5.4.3.1. Market estimates and forecast, 2016 – 2025

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