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A Framework for Shop Floor Material Delivery Optimization Based on RFID-Enabled Production Big Data

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Advances in Intelligent, Interactive Systems and Applications (IISA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 885))

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Abstract

With the wide use of smart sensor devices in production shop floors, a large amount of real-time and multi-source production process big data is being produced. In order to make a better material delivery decision by utilizing these production big data, in this article, a framework for shop floor material delivery optimization (SFMSO) is proposed. Under the proposed framework, the availability of these data and knowledge about production process can be enhanced. Focusing on material delivery process of shop floor, the key enabling technologies are also designed and discussed to facilitate the implementation of the proposed framework.

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Authors and Affiliations

  1. College of Life Science and Technology, Honghe University, Mengzi, Yunnan, China

    Xin Zhao, Wei Zhang, Hengling Meng & Fangfang He

  2. Engineering College, Honghe University, Mengzi, Yunnan, China

    Shan Ren

Authors
  1. Xin Zhao
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  2. Wei Zhang
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  3. Hengling Meng
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  4. Fangfang He
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  5. Shan Ren
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Corresponding author

Correspondence to Shan Ren .

Editor information

Editors and Affiliations

  1. Department de Ciències de la Computació, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

  2. Department of Computer Science and Engineering, Faculty of Engineering and Technology, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  3. Department of Business Systems and Analytics, La Salle University, Philadelphia, PA, USA

    Madjid Tavana

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Zhao, X., Zhang, W., Meng, H., He, F., Ren, S. (2019). A Framework for Shop Floor Material Delivery Optimization Based on RFID-Enabled Production Big Data. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent, Interactive Systems and Applications. IISA 2018. Advances in Intelligent Systems and Computing, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-030-02804-6_91

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