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Artificial intelligence could give us the last needed piece for fully automated manufacturing. Credit: Hyundai Motor Group
Powered through artificial intelligence (AI), the use of AI algorithms for manufacturing has transformed company operations and led to vast improvements in productivity, higher quality, and lower costs. The piecemeal deployments we still see today will eventually give way to the agility and operational insight this new level of data management enables.
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AI in manufacturing today
AI is being enthusiastically adopted, and novel applications are changing the playing field within the industry. According to Fortune Business Insights, AI in the 2019 worldwide manufacturing market was valued at $8.14 billion and is projected to reach $695.16 billion by 2032. This is part of a broader trend called Industry 4.0, where connectivity coupled with advanced analytics pave the way for more agile and productive manufacturing executed on the fly. A few examples follow.
Predictive maintenance
Traditional maintenance systems often require unnecessary maintenance or unexpected equipment failure. AI’s predictive maintenance capabilities are a game-changer for developers. AI systems analyze data from embedded sensors to predict potential failures. Using machine learning, these systems can identify patterns and anomalies, enabling maintenance teams to actively address issues. This approach not only reduces downtime but also extends equipment life, ultimately reducing costs and improving efficiency.
Supply chain optimization
AI plays an important role in optimizing supply chain operations. Machine learning algorithms can analyze historical data, market data, and external factors to accurately forecast demand. This allows manufacturers to maintain optimal inventory levels, reduce overstock, and reduce inventories. Additionally, AI can improve logistics efficiency by optimizing delivery routes, thereby reducing delivery time and costs. Greater supply chain visibility also enables better risk management, ensuring that manufacturers can respond quickly to problems.
Quality control and defect detection
AI-powered vision systems, as an example, are revolutionizing quality control in manufacturing. These systems use advanced image recognition and machine learning algorithms to screen products for defects and discrepancies in real time. Unlike human monitoring systems, AI systems can work tirelessly, ensuring that every product meets stringent quality standards. This not only reduces the number of faulty products reaching customers, but also helps to quickly identify and address problems in the manufacturing process.
Increased human-machine integration
AI is not just replacing human jobs, but enhancing human capabilities. Collaborative robots, or cobots, are designed to work with human workers to enhance their skills and productivity. These AI-powered machines can tackle dangerous or complex tasks, allowing human workers to focus on the more complex and creative aspects of construction. This interaction between humans and machines creates a safe, efficient, and innovative work environment.
AI-powered MRP software
Material requirements planning (MRP) is an important part of manufacturing that ensures just-in-time inventory. AI-powered MRP software takes traditional MRP to the next level by incorporating advanced algorithms and predictive analytics. These systems can analyze a wealth of data from multiple sources, such as supplier performance, lead times, and production schedules.
For example, AI-enhanced MRP systems can do the following.
Optimize inventory levels: AI algorithms predict the precise amount of raw materials needed, minimizing excess inventory and reducing holding costs.
Improve production scheduling: By analyzing production data and market demand, AI can create more efficient production schedules and ensure timely delivery of products.
Enhance supplier management: AI can evaluate supplier performance, predict potential disruptions, and suggest alternative suppliers to mitigate risks.
Streamline order processing: AI can automate order processing, reducing errors and accelerating the fulfillment process.
Key players in AI-driven manufacturing
Many companies have successfully incorporated AI into their product development with impressive results. Siemens, for example, has implemented AI-powered predictive maintenance, significantly reducing machine operations. Similarly, General Electric uses AI for quality control, significantly improving production accuracy and customer satisfaction.
Other examples include IBM and Fanuc. IBM’s Watson IoT platform applies AI to industrial data to enhance operational efficiency, improve product quality, and reduce downtime. Fanuc integrates AI with robotics to create smarter manufacturing systems capable of learning and adapting to new tasks.
Challenges and considerations
Despite its many benefits, adopting AI in manufacturing comes with challenges. A major concern is the potential displacement of workers due to automation. However, the focus should be on retraining and upskilling workers to work on AI technologies. Additionally, it’s important to ensure that data are secure and confidential, because AI systems often handle sensitive information. Developers also need to consider the initial investment and the need for ongoing maintenance and updating of AI systems.
Conclusion
AI is driving a major shift in the manufacturing industry and offering solutions that enhance productivity, safety, quality, and cost efficiency. With key players like Siemens, GE Digital, IBM, and Fanuc at the fore, AI’s role in manufacturing continues to expand. AI-powered MRP software further exemplifies the potential of AI to optimize manufacturing processes and resource management. As AI technology advances, its integration into manufacturing promises to unlock new levels of innovation and efficiency, shaping the future of the industry.
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