A Blockchain-based Decision Support System for E-commerce Order Prediction

George To Sum Ho; Yuk Ming Tang; H. Y. Lam; Valerie Tang

doi:10.1109/ICAIIC57133.2023.10067036

A Blockchain-based Decision Support System for E-commerce Order Prediction

George To Sum Ho, Yuk Ming Tang, H. Y. Lam, Valerie Tang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

7 Citations (Scopus)

Abstract

The rapidly growing e-commerce sector has created new opportunities and challenges to the logistics industry. Nonetheless, the majority of Hong Kong logistics industry, especially small-and-medium (SMEs) firms, lack operational decision support to adequately handle the seasonal, fragmented, and fluctuating e-commerce orders. To grasp the opportunities of the e-commerce logistics business, logistics service providers (LSPs) should enhance their capability in information exchange and operational planning. With these improvements, the logistics industry would be better able to sustain and expand their e-commerce logistics business. In this paper, a Blockchain-based E-Commerce Analytics Model is developed to enhance digital supply chain integration. Firstly, timely operational decision support would be achieved as blockchain technology, after that, the ML algorithm would enable the logistics industry to manage data efficiently and to forecast dynamic e-commerce order demand. Subsequently, the proposed model allows LSPs to flexibly re-allocate the right number of resources in real time to deal with the hour-to-hour fluctuating arrival of orders in distribution centers. Additionally, the proposed model enables logistics practitioners to predict the sales performance related to e-commerce.

Original language	English
Title of host publication	5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
Pages	41-45
Number of pages	5
ISBN (Electronic)	9781665456456
DOIs	https://doi.org/10.1109/ICAIIC57133.2023.10067036
Publication status	Published - Feb 2023
Event	5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023 - Ramada by Wyndham Bali Sunset Road Kuta, Bali, Indonesia Duration: 20 Feb 2023 → 23 Feb 2023 http://2023.icaiic.org/?page_id=22

Publication series

Name	2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

Conference

Conference	5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
Country/Territory	Indonesia
City	Bali
Period	20/02/23 → 23/02/23
Internet address	http://2023.icaiic.org/?page_id=22

Keywords

Blockchain
E-commerce
Machine learning
logistic industries

ASJC Scopus subject areas

Information Systems and Management
Decision Sciences (miscellaneous)
Artificial Intelligence
Information Systems
Signal Processing
Computer Networks and Communications

More information

10.1109/ICAIIC57133.2023.10067036

Cite this

Ho, G. T. S., Tang, Y. M., Lam, H. Y., & Tang, V. (2023). A Blockchain-based Decision Support System for E-commerce Order Prediction. In 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023 (pp. 41-45). (2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)). https://doi.org/10.1109/ICAIIC57133.2023.10067036

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title = "A Blockchain-based Decision Support System for E-commerce Order Prediction",

abstract = "The rapidly growing e-commerce sector has created new opportunities and challenges to the logistics industry. Nonetheless, the majority of Hong Kong logistics industry, especially small-and-medium (SMEs) firms, lack operational decision support to adequately handle the seasonal, fragmented, and fluctuating e-commerce orders. To grasp the opportunities of the e-commerce logistics business, logistics service providers (LSPs) should enhance their capability in information exchange and operational planning. With these improvements, the logistics industry would be better able to sustain and expand their e-commerce logistics business. In this paper, a Blockchain-based E-Commerce Analytics Model is developed to enhance digital supply chain integration. Firstly, timely operational decision support would be achieved as blockchain technology, after that, the ML algorithm would enable the logistics industry to manage data efficiently and to forecast dynamic e-commerce order demand. Subsequently, the proposed model allows LSPs to flexibly re-allocate the right number of resources in real time to deal with the hour-to-hour fluctuating arrival of orders in distribution centers. Additionally, the proposed model enables logistics practitioners to predict the sales performance related to e-commerce.",

keywords = "Blockchain, E-commerce, Machine learning, logistic industries",

author = "Ho, {George To Sum} and Tang, {Yuk Ming} and Lam, {H. Y.} and Valerie Tang",

note = "Funding Information: ACKNOWLEDGMENT The authors would like to thank the Research Grants Council of Hong Kong for supporting this research under the Grant UGC/FDS14/E06/19. In addition, this project is also supported with a partially matching grant from the University Grants Committee of Hong Kong (RMGS Project No. 700011). The authors would like to thank the support by the Big Data Intelligence Centre in The Hang Seng University of Hong Kong. Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023 ; Conference date: 20-02-2023 Through 23-02-2023",

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language = "English",

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Ho, GTS, Tang, YM, Lam, HY & Tang, V 2023, A Blockchain-based Decision Support System for E-commerce Order Prediction. in 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023. 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 41-45, 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023, Bali, Indonesia, 20/02/23. https://doi.org/10.1109/ICAIIC57133.2023.10067036

A Blockchain-based Decision Support System for E-commerce Order Prediction. / Ho, George To Sum; Tang, Yuk Ming; Lam, H. Y. et al.
5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023. 2023. p. 41-45 (2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N1 - Funding Information: ACKNOWLEDGMENT The authors would like to thank the Research Grants Council of Hong Kong for supporting this research under the Grant UGC/FDS14/E06/19. In addition, this project is also supported with a partially matching grant from the University Grants Committee of Hong Kong (RMGS Project No. 700011). The authors would like to thank the support by the Big Data Intelligence Centre in The Hang Seng University of Hong Kong. Publisher Copyright: © 2023 IEEE.

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N2 - The rapidly growing e-commerce sector has created new opportunities and challenges to the logistics industry. Nonetheless, the majority of Hong Kong logistics industry, especially small-and-medium (SMEs) firms, lack operational decision support to adequately handle the seasonal, fragmented, and fluctuating e-commerce orders. To grasp the opportunities of the e-commerce logistics business, logistics service providers (LSPs) should enhance their capability in information exchange and operational planning. With these improvements, the logistics industry would be better able to sustain and expand their e-commerce logistics business. In this paper, a Blockchain-based E-Commerce Analytics Model is developed to enhance digital supply chain integration. Firstly, timely operational decision support would be achieved as blockchain technology, after that, the ML algorithm would enable the logistics industry to manage data efficiently and to forecast dynamic e-commerce order demand. Subsequently, the proposed model allows LSPs to flexibly re-allocate the right number of resources in real time to deal with the hour-to-hour fluctuating arrival of orders in distribution centers. Additionally, the proposed model enables logistics practitioners to predict the sales performance related to e-commerce.

AB - The rapidly growing e-commerce sector has created new opportunities and challenges to the logistics industry. Nonetheless, the majority of Hong Kong logistics industry, especially small-and-medium (SMEs) firms, lack operational decision support to adequately handle the seasonal, fragmented, and fluctuating e-commerce orders. To grasp the opportunities of the e-commerce logistics business, logistics service providers (LSPs) should enhance their capability in information exchange and operational planning. With these improvements, the logistics industry would be better able to sustain and expand their e-commerce logistics business. In this paper, a Blockchain-based E-Commerce Analytics Model is developed to enhance digital supply chain integration. Firstly, timely operational decision support would be achieved as blockchain technology, after that, the ML algorithm would enable the logistics industry to manage data efficiently and to forecast dynamic e-commerce order demand. Subsequently, the proposed model allows LSPs to flexibly re-allocate the right number of resources in real time to deal with the hour-to-hour fluctuating arrival of orders in distribution centers. Additionally, the proposed model enables logistics practitioners to predict the sales performance related to e-commerce.

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Ho GTS, Tang YM, Lam HY, Tang V. A Blockchain-based Decision Support System for E-commerce Order Prediction. In 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023. 2023. p. 41-45. (2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)). doi: 10.1109/ICAIIC57133.2023.10067036

A Blockchain-based Decision Support System for E-commerce Order Prediction

Abstract

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Keywords

ASJC Scopus subject areas

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