Kai Kang

Mr. Kai Kang


Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 June 2020 in Robotics and Computer-Integrated Manufacturing
Verified
Journal Article
Closed access
VVV.G. Venkatesh
V.G. Venkatesh
KKKai Kang
Kai Kang
Bill WangBill Wang
Bill Wang
RZRay Y Zhong
Ray Y Zhong
AZAbraham Zhang
Abraham Zhang

1 January 2020 in Resources, Conservation and Recycling
Verified
Journal Article
Closed access
AZAbraham Zhang
Abraham Zhang
RZRay Y Zhong
Ray Y Zhong
MFMuhammad Farooque
Muhammad Farooque
KKKai Kang
Kai Kang
VVV G Venkatesh
V G Venkatesh

7 June 2020 in Sustainability
Verified
Journal Article
Open access
PDF file
BTBing Qing Tan
Bing Qing Tan
FWFangfang Wang
Fangfang Wang
JLJia Liu
Jia Liu
KKKai Kang
Kai Kang
FCFederica Costa
Federica Costa
The logistics industry around the world has proliferated over recent years as a large number of business organizations have come to recognize the importance of logistics. Cost control used to be emphasized to remain competitive, but recently green logistics has gained attention with the awareness of the integration of economy and society as a whole. Nowadays, green logistics is a useful concept to improve the sustainability of logistics operations, and its related policies and theoretical research have been investigated and explored. However, the practical applications of green logistics are impeded by real-time data sharing, which is common in the logistics industry. Blockchain technology is adopted to address this challenge and enable data sharing among related stakeholders. This paper presents a reference framework for green logistics based on blockchain to reach the sustainable operations of logistics, with the integration of the Internet of Things and big data. Finally, potential benefits and limitations are analyzed when implementing this framework.

1 December 2021 in Computers in Industry
Verified
Journal Article
Closed access
YJYishuo Jiang
Yishuo Jiang
XLXinlai Liu
Xinlai Liu
KKKai Kang
Kai Kang
ZWZicheng Wang
Zicheng Wang
RZRay Y. Zhong
Ray Y. Zhong
George G.Q. HuangGeorge G.Q. Huang
George G.Q. Huang

6 May 2023 in Expert Systems with Applications
Verified
Journal Article
Closed access
KKKai Kang
Kai Kang
BTBing Qing Tan
Bing Qing Tan

3 September 2021 in Industrial Management & Data Systems
Verified
Journal Article
Closed access
WZWennan Zhang
Wennan Zhang
KKKai Kang
Kai Kang
RZRay Y. Zhong
Ray Y. Zhong
Purpose: This paper proposes an evaluation model for prefabricated construction to guide a supply chain with controllable costs. Prefabricated construction is prevalent due to area limitations. Nevertheless, the development is limited by budget control and identifying the factors affecting cost. The degree of close collaboration in the supply chain is closely interconnected with cost performance that includes direct and indirect factors. This paper not only quantizes these factors but also distinguishes the degree of influence of various factors.Design/methodology/approach: System dynamics is applied to simulate and analyze the construction cost factors through Vensim software. It can also clarify the relationship between cost and other influencing factors. The input data are collected from an Internet of Things (IoT)-enabled system under a Building Information Modeling (BIM) system and Hong Kong government reports.Findings: Simulation results indicate that prefabricated construction cost is mainly influenced by government promotion degree (GPD), working pressure from on-site construction (WPOSC), prefab quality (PQ), load-bearing capacity per vehicle (LBPV) and mold quality (MQ). However, it is more sensitive toward GPD, which indicates that the government should take measures to promote this construction technology. On-site worker management is also essential for the assembly process and indirectly influences the construction cost.Research limitations/implications: This paper quantifies indirect influential factors to clarify the specific features for prefabricated construction. The investigated factors are limited.Practical implications: The contractor can identify all factors and classify the levels of influence to make decisions under the supply chain system boundary.Social implications: The input data are collected from an IoT-enabled system under a BIM system and Hong Kong government reports. Thus, the relationship between construction cost influential factors can be investigated.Originality/value: This paper quantifies indirect influencing factors and clarifies the specific features in prefabricated construction. The contractor could identify these factors to make decisions and classify the levels of influence under the supply chain system boundary.