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Using data mining methods to develop manufacturing production rule in IoT environment

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Abstract

In order to meet the needs of customers in an Internet of Things (IoT) environment, the traditional manufacturing production strategy has gradually shifted from mass production to a small number of diverse forms. In traditional industry, when the production type changes to a small number of diverse forms, the complexity of scheduling increases and the rules of adaptability between products and production lines is not easy to judge. However, in traditional production management scheduling, the adaptability of production lines is mostly planned based on past experience. If the number of orders is too large or the production schedule changes, errors will increase. This situation will cause the actual production situation to be far removed from the planned result, which will affect the schedule achievement and delivery time. The present paper reports research using association rules to explore production lines and apply logic to solve the problem of production rules between production lines and products in the car manufacturing industry. The results show that the application of data mining association rules has an accuracy above 87%. The application of data mining can provide manufacturing production rules to assist managers to make better decisions in the IoT environment and to reduce the time required for manufacturing production.

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Authors and Affiliations

  1. Engineering Research Institute, Guangzhou City University of Technology, Guangzhou, 510800, China

    Lei Wang & Bin Lin

  2. Department of Computer Engineering, Dongguan Polytechnic, Dongguan, 523808, China

    Rongli Chen

  3. Computer Science &Information Management, Soochow University, Taipei, 11490, Taiwan

    Kuan-Han Lu

Authors
  1. Lei Wang
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  2. Bin Lin
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  3. Rongli Chen
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  4. Kuan-Han Lu
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Correspondence to Lei Wang.

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Wang, L., Lin, B., Chen, R. et al. Using data mining methods to develop manufacturing production rule in IoT environment. J Supercomput 78, 4526–4549 (2022). https://doi.org/10.1007/s11227-021-04034-6

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  • DOI: https://doi.org/10.1007/s11227-021-04034-6

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