Artificial Intelligence (AI) in Manufacturing Market Estimation, Global Share, Industry Outlook, Price Trend, Growth Opportunity and Top Regional Forecast 2025

How will APAC AI in manufacturing market perform over 2019-2025?

The increasing adoption of trends like industry 4.0 which warrants the enhancement of operational efficiency will drive Artificial Intelligence (AI) in Manufacturing Market size. There is a growing need among the manufacturers to reduce the cost of operation and increase productivity. The implementation of new technologies to study the customer behavior would thus favor the manufacturing of consumer centric products.
AI based solutions are extremely reliable and provide the enterprises with an extensive set of information which helps to improve the operation scalability and so the cost effectivity. Furthermore, the digitalization of data in the manufacturing sector will drive the industry trends. The rising inclinations toward data digitalization is anticipated to promote the incorporation of AI technologies to enable utilization of advanced data analytics solutions.

How will hardware segment affect the Artificial Intelligence (AI) in Manufacturing Market share over the forecast duration?

The hardware segment holds a substantial share of over 57% in the artificial intelligence (AI) in manufacturing market, owing to the increasing implementation of AI technologies across many industry verticals. Within the same, the GPUs are expected to dominate the hardware processor based AI in manufacturing market share pertaining to the high processing capabilities provided at lower cost. The rising prevalence of enhanced visual content will further promote GPU demand in the manufacturing industry.

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How will the proliferation of machine learning propel the market trends?

The growing demand for automated quality checks in the manufacturing facilities is expected to boost the application of machine learning. The technology has been implemented to guarantee the quality assurance at every stage of production. The system works with a high precision level which safeguards the reputation of the manufacturers, owing to which the machine learning will contribute substantially to market revenue.
As per estimates, in 2018, machine learning accounted for over 47% revenue of the artificial intelligence (AI) in manufacturing market.

How will the Asia Pacific Artificial Intelligence (AI) in Manufacturing Market perform over the forecast duration?

Asia Pacific registered a considerable market share of over 43% in 2018, dominating the industry. The growth of the regional market is credited to the presence of manufacturing hubs in the region including China, India, South Korea and Japan. These countries boast of the presence of some of the most developed manufacturing facilities with the rising prevalence of industry 4.0 technologies, which is expected to propel the adoption of AI solutions.
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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
1.1.6. Methodology & forecast parameters
1.2. Data sources
1.2.1. Secondary
1.2.1.1. Paid
1.2.1.2. Public
1.2.2. Primary
Chapter 2. Executive Summary
2.1. AI in manufacturing industry 360º synopsis, 2016 - 2025
2.2. Business trends
2.3. Regional trends
2.4. Component trends
2.4.1. Hardware trends
2.4.1.1. Processors trend
2.4.2. Software trends
2.4.3. Service trends
2.5. Technology trends
2.6. Application trends
2.7. End-use 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
3.6. Regulatory landscape
3.6.1. Health Insurance Portability and Accountability Act (HIPAA)
3.6.2. Payment Card Industry Data Security Standard (PCI DSS)
3.6.3. North American Electric Reliability Corp. (NERC) standards
3.6.4. Federal Information Security Management Act (FISMA)
3.6.5. The Gramm-Leach-Bliley Act (GLB) Act of 1999
3.6.6. The Sarbanes-Oxley Act of 2022
3.7. Technology and innovation landscape
3.8. Use cases
3.8.1. Outside the factory
3.8.1.1. Engineering
3.8.1.2. Supply chain management
3.8.2. Inside the factory
3.8.2.1. Production
3.8.2.2. Maintanence
3.8.2.3. Quality
3.8.2.4. Logistics
3.9. Price comparision of the AI processors
3.10. Industry impact forces
3.10.1. Growth drivers
3.10.1.1. Increasing venture capiatal investemnt in AI
3.10.1.2. Exponential growth in digital data
3.10.1.3. Rapid adoption of industry revolution 4.0
3.10.1.4. Changing customer behavior and demand
3.10.2. Industry pitfalls & challenges
3.10.2.1. Latency sensitive applications
3.10.2.2. Lack of skilled professtionals
3.11. Growth potential analysis
3.12. Porter’s analysis
3.13. PESTEL analysis
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