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    Applications of artificial intelligence(AI) in Manufacturing

    AI is one of the popular technologies in the manufacturing industry. Many manufacturing companies have already adopted related solutions, which have improved their competitiveness in the industry. However, there are still some challenges that exist and need to be overcome by each industry in order to bring more benefits.

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    What is Ai in manufacturing?

    The proliferation of technology has led the manufacturing industry to increasingly adopt Industrial Internet of Things (IoT) and artificial intelligence (AI). Through IoT, large volumes of data are collected, and AI, including machine learning and deep learning, is used to analyze this data to make precise decisions.

    【Learn more】What is artificial intelligence?

    Applications of artificial intelligence in Manufacturing

    According to Capgemini's survey, analyzing different cases to understand the feasibility of implementing artificial intelligence in manufacturing, the availability of data, solutions, and infrastructure, and how to solve problems and simplify team operations. The analysis results show that the following three items are the first steps for most manufacturers to introduce artificial intelligence: intelligent maintenance, process quality control, and demand analysis.

    Intelligent maintenance

    Smart maintenance of factory equipment is the most common application. When encountering damage or problems, this application provides the highest return on investment. In addition to reducing downtime, it can lower maintenance costs and improve production efficiency. Compared to other applications in the factory environment, it is easier to implement and many different solutions are already available, making it a mature application. Smart maintenance adds the following value to the manufacturing industry:


    1. Fault prediction: Predict when equipment is likely to fail and estimate the best time for maintenance to minimize downtime.

    2. Root cause analysis: Analyze the fundamental factors from the collected data to reduce the probability of their occurrence and prevent future failures.

    3. Ensure timely alerts: As more solutions are introduced, many false alarms are more likely. Through artificial intelligence, the equipment can "receive the right alert at the right time", taking into account "action time" to ensure when to issue alerts to take necessary measures to prevent accidents.


    Process quality control

    Quality inspection in production can predict potential defects through subtle trends in parameters. Sometimes, a review cannot be performed visually during the manufacturing process. With cameras and image recognition technology, combined with artificial intelligence, the inspection cost can be significantly reduced.


    Demand analysis

    In the past, purchasing decisions were made based on experience and data. Now, with the use of machine learning in artificial intelligence, consumer demand changes can be accurately predicted. This allows manufacturers to make purchasing decisions earlier or reduce purchases, thus reducing the amount of inventory and avoiding unnecessary costs.



    AI implementation priorities for manufacturers: Maintenance and quality

    As the impact of AI in the manufacturing industry gradually expands, and from the above, it is known that most manufacturers place their focus on easy-to-implement maintenance and quality. Expanding AI into other areas is still a major challenge, which includes many factors, such as the need to spend a lot of time on trial and error during the deployment phase. There is a certain probability of false alarms during the initial stage, and it takes time to prove its value.

    In addition, a plan is required for managing artificial intelligence and collected data to achieve good utilization. Without proper management of big data, artificial intelligence cannot be implemented. Finally, manufacturers need talented people who have a comprehensive understanding of artificial intelligence and their own field in order to introduce appropriate solutions to improve core issues, and such talent is hard to come by. Therefore, it is necessary to pay more attention to skills training to improve the success rate of implementation.


    Main photo by AdobeStock

    Reference: Capgemini

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