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Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques

Author

Listed:
  • Chang Liu
  • Yongfu Feng
  • Dongtao Lin
  • Liang Wu
  • Min Guo

Abstract

The authors propose an innovative Internet of Things (IoT) based E-commerce business model Cloud Laundry for mass scale laundry services. The model utilises big data analytics, intelligent logistics management, and machine learning techniques. Using GPS and real-time update of big data, it calculates the best transportation path and update and re-route the logistic terminals quickly and simultaneously. Cloud laundry intelligently and dynamically provides the best laundry solutions based on the current state spaces of the laundry terminals through the user's specifications and thus offers local hotel customers with convenient, efficient, and transparent laundry services. Taking advantage of the rapid development of the big data industry, user interest modelling, and information security and privacy considerations, cloud laundry uses smartphone terminal control and big data models to maintain customers’ security needs. Different from the traditional laundry industry, cloud laundry companies have higher capital turnover, more liquidity, and stronger profitability. Therefore, this new generation of smart laundry business model could be of interest to not only academic researchers, but E-commerce entrepreneurs as well.

Suggested Citation

  • Chang Liu & Yongfu Feng & Dongtao Lin & Liang Wu & Min Guo, 2020. "Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5113-5131, September.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:17:p:5113-5131
    DOI: 10.1080/00207543.2019.1677961
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    Cited by:

    1. Jinhao Xie & Chao Chen, 2022. "RETRACTED ARTICLE: Supply chain and logistics optimization management for international trading enterprises using IoT-based economic logistics model," Operations Management Research, Springer, vol. 15(3), pages 711-724, December.
    2. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    3. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    4. Núñez-Merino, Miguel & Maqueira-Marín, Juan Manuel & Moyano-Fuentes, José & Castaño-Moraga, Carlos Alberto, 2022. "Industry 4.0 and supply chain. A Systematic Science Mapping analysis," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    5. Dhirendra Prajapati & Felix T. S. Chan & H. Chelladurai & Lakshay Lakshay & Saurabh Pratap, 2022. "An Internet of Things Embedded Sustainable Supply Chain Management of B2B E-Commerce," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    6. Fanshun Zhang & Zhuorui Zhang & Quanquan Zhang & Xiaochun Zhu, 2023. "Dynamic Evaluation of Product Innovation Knowledge Concerning the Interactive Relationship between Innovative Subjects: A Multi-Objective Optimization Approach," Mathematics, MDPI, vol. 11(9), pages 1-33, April.

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