Intelligent Manufacturing Technologies Enable Cognitive Factories
Information technology has been largely impacting the manufacturing industry. With the increase in the number of IIoT solutions and real-time data processing, predictive analytics is enabling processing of large volumes of data captured from the connected machines. The current scenario includes involvement of humans in making logical decisions along with low-level machine processors. The concept of artificial intelligence (AI) or cognitive intelligent systems will thus enable machines to self-detect vicissitudes in the manufacturing processes and be in-built with capabilities to respond in real time with limited need for human intervention. Mass customization, complete automation, adoption of intuitive robots, and responsive intelligent machines will be enabled by the adoption of AI technologies.
This technology and innovation report provides a detailed look into the world of artificial intelligence and the key research focus areas that will impact the manufacturing sector. This study also focuses on AI technologies and how they enable various manufacturing supply chain participants to become smarter. Other modules include market potential, market participants, as well as implementation case studies and the future of individual technologies and its impact on the manufacturing sector.
Key questions answered in the study:
- 1. What are the trending technologies of AI in manufacturing?
- 2. What is the value addition of AI technologies in manufacturing?
- 3. What are the key trends that will shape the cognitive factory?
- 4. Who are the cognitive solution providers?
- 5. What are the benefits of AI application in manufacturing?
- 6. What is the market potential of AI?
- 7. What are some of the strategies that manufacturers should adopt to accelerate growth?
- 8. What are future technology trends? How will it impact the industrial and manufacturing sectors?
Table of Contents
1.0. Executive Summary
- 1.1. Research Scope
- 1.2. Research Methodology
- 1.3. Explanation of Research Methodology
- 1.4. Key Findings
- 1.4. Key Findings (Continued)
2.0. Technology Status Assessment
- 2.1. Cognitive Manufacturing Era
- 2.2. Current Scenario in the Manufacturing Industry and the Need for Advancements
- 2.3. Adaptable Agile Technology over Rule Based
- 2.4. Benchmarking of Current Technology with AI
- 2.5. Supply Chain and How Organizations Are Adopting and Aligning Themselves?
- 2.6. Current Trends and Competitive Strategies of Organizations
- 2.7. Technology Trends
- 2.8. Impact of Artificial Intelligence Changes the Manufacturing Industry
- 2.9. What are the Key Focus Areas within AI ?
- 2.10. What are the Key AI tools? How do they Help AI to be Used in Manufacturing?
3.0. Intelligent Manufacturing Systems Implementation Cases
- 3.1. AI Facilitating Development of Intelligent Manufacturing
- 3.2. Analytics Based on Acoustic Sound Captured Using Smart Sensors
- 3.3. Cognitive Analytics Enables 5% Reduction in Operational Costs
- 3.4. Vision-based Analytics Allows Effective Leveling
- 3.5. Eliminating Waste in Production Lines with Automated Vision Systems
- 3.6. Recognition of Defective Objects Using Machine Learning
- 3.7. Innovations in AI Enable Cost Savings
- 3.8. Improved Operational Savings within Three weeks Using Analytics
- 3.9. 3D Printer with AI ?
4.0. Assessment of Market Potential
- 4.1. AI: A Growing Market
- 4.2. Research Focus Areas
- 4.3. Factors Driving Adoption
- 4.4. Unique Challenges in Industrial Artificial Intelligence
- 4.5. Global initiatives
- 4.6. Companies to Action
5.0. Strategic Viewpoint
- 5.1. Strategic Viewpoint on Adoption Factors
- 5.2. Impact Analysis of AI in the Manufacturing Sector
- 5.3. The Road Ahead
- 5.4. Key Conclusions
6.0. Key Patents and Contacts
- 6.1. Key Patents on Deep Learning in Manufacturing Sector
- 6.2. Key Patents on Machine Learning in Manufacturing Sector
- 6.3. Key Patents on Machine Vision in Manufacturing Sector
- 6.4. Key Patents on NLP in Manufacturing Sector
- 6.5. Key Contacts
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