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Industrial IoT is the inception of a new digital industrial technology, which is expected to enable the interconnection of sensors, machines, and IT systems across the value chain of manufacturing companies beyond a single enterprise. These interconnected systems interact with each other using standard internet-based protocols and enable companies to analyze data to predict the failure of machines. Industrial IoT is expected to make it possible to gather and analyze data related to machines, thereby enabling fast, flexible, and efficient processes to produce high-quality products at reduced co
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The United States is a leader in applying Artificial Intelligence (AI) in manufacturing. Here's a breakdown of the trends: Artificial Intelligence In Manufacturing Market size is estimated to be valued USD 3.2 billion in 2023 and is anticipated to reach USD 20.8 billion by 2028, at a CAGR of 45.6% during the forecast period. •Market Growth: The US AI in manufacturing market is expected to see significant growth, with estimates suggesting a compound annual growth rate (CAGR) of around 35.6% reaching $31.47 billion by 2032 [1, 4]. •Focus on Efficiency and Cost Reduction: A key driver for AI adoption is automation, aiming to increase efficiency and lower production costs. Predictive maintenance and quality control are prime areas where AI is optimizing processes [2, 3]. •Supply Chain and Customization: AI is being used to enhance supply chain management by forecasting demand trends and enabling faster responses to customer needs. This allows for greater customization capabilities [3]. •Government Support: The US government plays a role in fostering AI development through initiatives like AI.gov and the Modernizing Government Technology (MGT) Act [4].
Downlod PDF Brochure - https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=72679105 Specific Applications: •Predictive Maintenance: AI analyzes sensor data from machines to predict equipment failures before they occur. This prevents costly downtime and ensures smooth production flow [1]. •Robot Learning: AI-powered robots are becoming more adaptable and can learn new tasks through simulation and real-world experience, increasing their flexibility on the factory floor [2]. •Defect Detection: Machine vision with AI algorithms can inspect products with high accuracy, catching defects that might slip by human inspectors, leading to improved quality control [3]. •Production Optimization: AI can analyze vast amounts of data to optimize production scheduling, material usage, and logistics, minimizing waste and maximizing output [2]. •Generative Design: AI can be used to design products with specific properties or functionalities, leading to faster innovation and lighter, stronger materials [4]. Challenges and Considerations: •Workforce Impact: While AI can create new jobs, it may also automate some existing ones. Reskilling and upskilling the workforce is crucial for a smooth transition [1]. •Data Security: Manufacturing facilities often handle sensitive data. Robust cybersecurity measures are needed to protect against AI-powered cyberattacks [2]. •Explainability and Bias: Understanding how AI systems make decisions is critical. Bias in training data can lead to biased AI outputs, requiring careful design and data curation [3]. Examples of US Companies using AI in Manufacturing: •Ford: Uses AI for predictive maintenance, optimizing factory operations and reducing downtime [5]. •General Electric: Offers AI-powered software suites like Predix for industrial applications, including predictive maintenance and performance optimization [6]. •Honeywell: Develops AI-powered solutions for smart factories, focusing on areas like anomaly detection and process optimization [7]. About MarketsandMarkets™ MarketsandMarkets™has been recognized as one of America’s best management consulting firms by Forbes, as per their recent report.
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