IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v12y2021i4d10.1007_s13198-021-01108-9.html
   My bibliography  Save this article

The use of data mining technology in agricultural e-commerce under the background of 6G Internet of things communication

Author

Listed:
  • Qiubo Li

    (Fuyang Normal University)

  • Ru Xiao

    (Fuyang Normal University)

Abstract

In view of the particularity of agricultural products and the shortcomings of existing agricultural products logistics distribution system, to reduce the cost of agricultural e-commerce logistics distribution and improve customer satisfaction, in this exploration, data mining technology in the field of artificial intelligence is used. Based on the analysis of the distribution efficiency of the current mainstream agricultural products logistics distribution mode, the logistics mode is optimized and improved, and the agricultural e-commerce common delivery mode is proposed. The logistics cost and customer satisfaction under the logistics mode are tested and analyzed through genetic algorithm and MATLAB software. The results show that the timely distribution rate of three modes of self-operated distribution, third-party logistics and common delivery decreases with the increase of orders; the timely distribution rate of common delivery mode fluctuates the most, followed by the third-party logistics mode; the timely distribution rate of self-operated distribution mode is the stablest, but it is relatively low. The calculation of genetic algorithm reveals that the optimized common delivery mode of agricultural products logistics has a significant effect on oil consumption, damage cost and other aspects. By implementing the common delivery mode, the oil consumption cost, the penalty cost, the refrigeration cost, and the damage cost are optimized by 26.7%, 31.7%, 30.3% and 19.6%, respectively. Moreover, 95% of customers are totally satisfied. In this exploration, the data mining technology is used to optimize the agricultural products distribution mode, which makes a beneficial exploration for solving the logistics bottleneck problem in the e-commerce environment, enriches the research of e-commerce logistics distribution system, and provides reference for promoting the development of agricultural products e-commerce logistics system and improving the efficiency of agricultural products logistics distribution.

Suggested Citation

  • Qiubo Li & Ru Xiao, 2021. "The use of data mining technology in agricultural e-commerce under the background of 6G Internet of things communication," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 813-823, August.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01108-9
    DOI: 10.1007/s13198-021-01108-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01108-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01108-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Beaman, Lori & Dillon, Andrew, 2018. "Diffusion of agricultural information within social networks: Evidence on gender inequalities from Mali," Journal of Development Economics, Elsevier, vol. 133(C), pages 147-161.
    2. Rong, Aiying & Akkerman, Renzo & Grunow, Martin, 2011. "An optimization approach for managing fresh food quality throughout the supply chain," International Journal of Production Economics, Elsevier, vol. 131(1), pages 421-429, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qi, Bitian & Shen, Yanbo & Xu, Tieyu, 2023. "An artificial-intelligence-enabled sustainable supply chain model for B2C E-commerce business in the international trade," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi-Kuei Lin & Cheng-Fu Huang & Yi-Chieh Liao, 2019. "Reliability of a stochastic intermodal logistics network under spoilage and time considerations," Annals of Operations Research, Springer, vol. 277(1), pages 95-118, June.
    2. Mona Haji & Laoucine Kerbache & Mahaboob Muhammad & Tareq Al-Ansari, 2020. "Roles of Technology in Improving Perishable Food Supply Chains," Logistics, MDPI, vol. 4(4), pages 1-24, December.
    3. Stüve, David & van der Meer, Robert & Lütke Entrup, Matthias & Agha, Mouhamad Shaker Ali, 2020. "Supply chain planning in the food industry," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 317-353, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    4. Amorim, P. & Günther, H.-O. & Almada-Lobo, B., 2012. "Multi-objective integrated production and distribution planning of perishable products," International Journal of Production Economics, Elsevier, vol. 138(1), pages 89-101.
    5. Lapierre, Margaux & Le Velly, Gwenolé & Bougherara, Douadia & Préget, Raphaële & Sauquet, Alexandre, 2023. "Designing agri-environmental schemes to cope with uncertainty," Ecological Economics, Elsevier, vol. 203(C).
    6. Hongpeng Guo & Xiangnan Sun & Chulin Pan & Shuang Xu & Nan Yan, 2022. "The Sustainability of Fresh Agricultural Produce Live Broadcast Development: Influence on Consumer Purchase Intentions Based on Live Broadcast Characteristics," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    7. Sara Martins & Pedro Amorim & Bernardo Almada-Lobo, 2018. "Delivery mode planning for distribution to brick-and-mortar retail stores: discussion and literature review," Flexible Services and Manufacturing Journal, Springer, vol. 30(4), pages 785-812, December.
    8. Magdalena Klopott, 2022. "The Importance of Insurance in Maritime Trade of Chilled or Frozen Cargoes," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 470-482.
    9. Oriana Bandiera & Robin Burgess & Erika Deserranno & Ricardo Morel & Munshi Sulaiman & Imran Rasul, 2023. "Social Incentives, Delivery Agents, and the Effectiveness of Development Interventions," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 1(1), pages 162-224.
    10. Lejarza, Fernando & Pistikopoulos, Ioannis & Baldea, Michael, 2021. "A scalable real-time solution strategy for supply chain management of fresh produce: A Mexico-to-United States cross border study," International Journal of Production Economics, Elsevier, vol. 240(C).
    11. Macours, Karen & Behaghel, Luc & Gignoux, Jérémie, 2020. "Social learning in agriculture: does smallholder heterogeneity impede technology diffusion in Sub-Saharan Africa?," CEPR Discussion Papers 15220, C.E.P.R. Discussion Papers.
    12. Moshood Olatunde Oladapo & Moheeb Abualqumboz & Lawrence M. Ngoe & Abiodun Kolawole Oyetunji & Chiemela Victor Amaechi & Rasheed Bello & Ebube Charles Amaechi, 2023. "Sustainable Technology Adoption as a Source of Competitive Advantage for Pineapple Production in Ejigbo, Nigeria," Economies, MDPI, vol. 11(9), pages 1-29, August.
    13. Pérez-Mesa, Juan Carlos & García Barranco, Mª Carmen & Ciagnocavo, Cynthia & Hernández Rubio, Jesús, 2023. "Seeking new strategic options for promotion of intermodal transport in perishables: the use of Short Sea Shipping," MPRA Paper 119464, University Library of Munich, Germany, revised 2023.
    14. Guowei Liu & Jianxiong Zhang & Wansheng Tang, 2015. "Joint dynamic pricing and investment strategy for perishable foods with price-quality dependent demand," Annals of Operations Research, Springer, vol. 226(1), pages 397-416, March.
    15. Raut, Rakesh D. & Gardas, Bhaskar B. & Narwane, Vaibhav S. & Narkhede, Balkrishna E., 2019. "Improvement in the food losses in fruits and vegetable supply chain - a perspective of cold third-party logistics approach," Operations Research Perspectives, Elsevier, vol. 6(C).
    16. Pan, Fei & Zhou, Wei & Fan, Tijun & Li, Shuxia & Zhang, Chong, 2021. "Deterioration rate variation risk for sustainable cross-docking service operations," International Journal of Production Economics, Elsevier, vol. 232(C).
    17. Mohammed, Ahmed & Wang, Qian, 2017. "The fuzzy multi-objective distribution planner for a green meat supply chain," International Journal of Production Economics, Elsevier, vol. 184(C), pages 47-58.
    18. Soto-Silva, Wladimir E. & Nadal-Roig, Esteve & González-Araya, Marcela C. & Pla-Aragones, Lluis M., 2016. "Operational research models applied to the fresh fruit supply chain," European Journal of Operational Research, Elsevier, vol. 251(2), pages 345-355.
    19. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    20. Gholami-Zanjani, Seyed Mohammad & Klibi, Walid & Jabalameli, Mohammad Saeed & Pishvaee, Mir Saman, 2021. "The design of resilient food supply chain networks prone to epidemic disruptions," International Journal of Production Economics, Elsevier, vol. 233(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01108-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.