Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques
Chang Liu,
Yongfu Feng,
Dongtao Lin,
Liang Wu and
Min Guo
International Journal of Production Research, 2020, vol. 58, issue 17, 5113-5131
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.
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2019.1677961 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:58:y:2020:i:17:p:5113-5131
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20
DOI: 10.1080/00207543.2019.1677961
Access Statistics for this article
International Journal of Production Research is currently edited by Professor A. Dolgui
More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().