Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 54 , 01 December 2023
Open
Access | Article
Big Data Management: Empowering Sustainable Logistics with Data-Driven Operation Optimization
* Author to whom correspondence should be addressed.
Abstract
Numerous disciplines, including logistics, have experienced a paradigm shift since the advent of big data. As the backbone of many industries, logistics has been significantly impacted by the expansion of big data. The purpose of this paper is to investigate the far-reaching effects of big data technology on the development of sustainable logistics, with a particular emphasis on the role of big data in various parts of the logistics process. Through a literature review and case analysis, this paper focuses on summarizing the application of big data in all aspects of logistics, and selecting and analyzing the experiences and lessons of businesses that have successfully implemented big data logistics practices. It finds that by optimizing the operation model and other means, big data effectively improved logistics efficiency. Simultaneously, intelligent solutions supported by big data can also aid in reducing the impact on the environment, for instance by encouraging the use of alternative energy vehicles. In addition, big data can encourage various entities to engage in collaborative innovation and promote the continuous generation of logistics-related solutions.
Keywords
operational efficiency, environmental impact, logistics innovation
References
1. Big Data Industry Ecological Alliance. (August 3, 2022). Distribution of big data industry applications in China in 2021, by type [Graph]. In Statista. https://www.statista.com/statistics/1284459/china-share-of-big-data-industry-applications/
2. The State Post Bureau announced the operation of the postal industry from January to July 2023. (n.d.). https://www.spb.gov.cn/gjyzj/c100015/c100016/202308/6d1f51b71c2840a5a7eff912a60b2dd0.shtml
3. Pournader, M., Shi, Y., Seuring, S., & Koh, S. C. L. (2019). Blockchain applications in supply chains, transport and logistics: A systematic review of the literature. International Journal of Production Research, 58(7), 2063–2081. https://doi.org/10.1080/00207543.2019.1650976
4. Vassakis, K., Petrakis, E., Kopanakis, I. (2018). Big Data Analytics: Applications, Prospects and Challenges. In: Skourletopoulos, G., Mastorakis, G., Mavromoustakis, C., Dobre, C., Pallis, E. (eds) Mobile Big Data. Lecture Notes on Data Engineering and Communications Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-67925-9_1
5. United Nations. (2015). In Durban platform for Enhanced Action (Decision 1/CP.17): Adoption of a protocol, another legal instrument, or an agreed outcome with legal force under the convention applicable to all parties: Adoption of the Paris agreement. New York.
6. Kang, K., Zhong, R. Y., & Xu, S. (2019). Cloud-enabled sharing in Logistics Product Service System. Procedia CIRP, 83, 451–455. https://doi.org/10.1016/j.procir.2019.03.103
7. Arishi, A., Krishnan, K., & Arishi, M. (2022). Machine Learning Approach for truck-drones based last-mile delivery in the era of industry 4.0. Engineering Applications of Artificial Intelligence, 116, 105439. https://doi.org/10.1016/j.engappai.2022.105439
8. Tu, M. (2018a). An exploratory study of internet of things (IOT) adoption intention in logistics and Supply Chain Management. The International Journal of Logistics Management, 29(1), 131–151. https://doi.org/10.1108/ijlm-11-2016-0274
9. Lokanan, M., & Maddhesia, V. (2022a). Supply Chain Fraud Prediction with Machine Learning and Artificial. Intelligence. https://doi.org/10.32388/1vzc8w
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-155-1
- ISBN (Online)
- 978-1-83558-156-8
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/54/20230878
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited