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A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics

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  • Zonggui Tian
  • Ray Y. Zhong
  • Ali Vatankhah Barenji
  • Y. T. Wang
  • Zhi Li
  • Yiming Rong

Abstract

The rapid development of urbanisation and the ever-changing consumers’ demands are constantly changing the urban logistics industry, imposing challenges on logistics service providers to improve customer satisfaction which is one of the indicators for the sustainability of urban logistics. Existing customer satisfaction evaluations are based on a questionnaire survey, which is time-consuming and labour intensive. Moreover, the logistics data are confidential and can only be accessed by the stakeholders in existing logistics models, causing the problem of information non-transparency among logistics enterprises and the third authorities like banks and governments, which may hinder the sustainable development of urban logistics. In this paper, we propose a blockchain-based evaluation approach for customer satisfaction in the context of urban logistics. Four criteria affecting customer satisfaction in urban logistics are identified. A machine learning algorithm Long Short-Term Memory (LSTM) is adopted to predict customer satisfaction in the future period. The implementation is demonstrated to illustrate the proposed approach. A smart contract is designed for compensation and/or refund to customers when their satisfaction with the delivery services is at a low level.

Suggested Citation

  • Zonggui Tian & Ray Y. Zhong & Ali Vatankhah Barenji & Y. T. Wang & Zhi Li & Yiming Rong, 2021. "A blockchain-based evaluation approach for customer delivery satisfaction in sustainable urban logistics," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 2229-2249, April.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:7:p:2229-2249
    DOI: 10.1080/00207543.2020.1809733
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    Cited by:

    1. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    2. Soumya Choudhury & Parvathi Jayaprakash & S. Srinivas & S. Sowmya & Tarun Shah & R. Abinaya, 2023. "A blockchain platform for the truck freight marketplace in India," Operations Management Research, Springer, vol. 16(2), pages 684-704, June.
    3. Yu Zhang & Nan Liu, 2021. "Optimal Internet of Things Technology Adoption Decisions and Pricing Strategies for High-Traceability Logistics Services," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    4. Nadia Giuffrida & Jenny Fajardo-Calderin & Antonio D. Masegosa & Frank Werner & Margarete Steudter & Francesco Pilla, 2022. "Optimization and Machine Learning Applied to Last-Mile Logistics: A Review," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
    5. Cui, Huixia & Qiu, Jianlong & Cao, Jinde & Guo, Ming & Chen, Xiangyong & Gorbachev, Sergey, 2023. "Route optimization in township logistics distribution considering customer satisfaction based on adaptive genetic algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 28-42.

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