“For successful AI application in manufacturing, securing data quality, improving infrastructure, and improving understanding of AI technology within the organization are essential.”

According to Omdia's recent report 'AI in Manufacturing', AI-based manufacturing innovation increases efficiency and competitiveness, but understanding and preparation for AI technology are necessary to successfully introduce AI.

The accelerated adoption of artificial intelligence (AI) in manufacturing is ushering in a new era of production efficiency and technological innovation.

Beyond simple automation, AI is being used in a variety of areas, including data analysis, predictive maintenance, quality verification, and supply chain optimization across industries, and is positioned as a key tool for enhancing manufacturing competitiveness.

The primary role of AI in manufacturing can be summarized as data analysis and decision support.

In particular, large manufacturers aim to maximize operational efficiency and address the shortage of skilled workers through AI.

First, AI-based automation systems detect and correct inefficiencies in the manufacturing process in real time, thereby increasing productivity. Additionally, it enables predictive maintenance by optimizing the machine's operating time through facility sensors and machine learning algorithms.

Additionally, by utilizing high-resolution image data and AI models, defect detection speed can be increased, accuracy can be improved, quality analysis can be performed in real time on the manufacturing line, and defective products can be immediately removed.

Supply chain analysis using AI here precisely adjusts demand forecasts and logistics plans to reduce costs and shorten delivery times, and efficient inventory management is possible through AI-based logistics optimization that takes into account global supply chain volatility.

Manufacturing sites are a mix of various legacy systems and digital environments, making data integration and quality control essential. Companies are expanding AI applications by leveraging edge computing and the cloud.

Although AI technology is being rapidly applied in manufacturing, there are also some challenges.

First, a key element for AI success in the manufacturing industry is securing high-quality data, but data integration and management are difficult because the data is scattered or inconsistent in format.

Therefore, data standardization and real-time linkage are necessary to improve the accuracy of AI systems.

In addition, in the early stages of AI adoption, the hardware and software construction costs are quite high, making it difficult for small and medium-sized manufacturers to access. To address this, manufacturers are turning to lightweight AI models (TinyML) and edge AI solutions.

Changes in organizational culture due to the introduction of technology are also a problem. Because manufacturing has been run in a traditional way for a long time, it requires an organizational culture improvement to embrace AI-based automation and predictive models.

To spread AI, we need to approach it in a way that strengthens collaboration between field engineers and operations teams.

In the future, AI is expected to be applied in a more advanced form in manufacturing.

Generative AI is expected to play a revolutionary role in design optimization, virtual prototype development, and supply chain automation. Combined with digital twin technology, it can virtually simulate real-world manufacturing environments and further improve operational efficiency.

Manufacturers are adopting IoT and edge computing-based AI systems to enhance real-time data analysis. The ability to directly analyze and respond to field data will be expanded by using smart sensors with built-in AI chipsets.

In the early days of AI adoption, individual companies used their own AI models, but in the future, an AI ecosystem based on common standards and collaboration is expected to be built. Manufacturers are expected to reduce cost burdens and accelerate technological advancement by applying common AI frameworks and modularized solutions.

AI is emerging as a key technology that will transform manufacturing into a smarter and more efficient industry.

The introduction of AI will enable production optimization, quality improvement, supply chain automation, and cost reduction, and the combination with generative AI and IoT will enable more sophisticated automation.

On the other hand, for successful AI application, securing data quality, improving infrastructure, and improving understanding of AI technology within the organization are essential.

In the future, manufacturers are expected to combine AI and digital technologies to secure sustainable industrial competitiveness and actively respond to market volatility.