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Article: A cost evaluation model for IoT-enabled prefabricated construction supply chain management

TitleA cost evaluation model for IoT-enabled prefabricated construction supply chain management
Authors
KeywordsCost evaluation
Prefabricated construction
Supply chain management
Issue Date2021
PublisherEmerald Group Publishing Limited. The Journal's web site is located at http://www.emeraldinsight.com/imds.htm
Citation
Industrial Management & Data Systems, 2021, v. 121 n. 12, p. 2738-2759 How to Cite?
AbstractPurpose: 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.
Persistent Identifierhttp://hdl.handle.net/10722/302459
ISSN
2023 Impact Factor: 4.2
2023 SCImago Journal Rankings: 1.207
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZHANG, W-
dc.contributor.authorKANG, K-
dc.contributor.authorZhong, R-
dc.date.accessioned2021-09-06T03:32:36Z-
dc.date.available2021-09-06T03:32:36Z-
dc.date.issued2021-
dc.identifier.citationIndustrial Management & Data Systems, 2021, v. 121 n. 12, p. 2738-2759-
dc.identifier.issn0263-5577-
dc.identifier.urihttp://hdl.handle.net/10722/302459-
dc.description.abstractPurpose: 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.-
dc.languageeng-
dc.publisherEmerald Group Publishing Limited. The Journal's web site is located at http://www.emeraldinsight.com/imds.htm-
dc.relation.ispartofIndustrial Management & Data Systems-
dc.subjectCost evaluation-
dc.subjectPrefabricated construction-
dc.subjectSupply chain management-
dc.titleA cost evaluation model for IoT-enabled prefabricated construction supply chain management-
dc.typeArticle-
dc.identifier.emailZhong, R: zhongzry@hku.hk-
dc.identifier.authorityZhong, R=rp02116-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1108/IMDS-12-2020-0735-
dc.identifier.scopuseid_2-s2.0-85114176081-
dc.identifier.hkuros324847-
dc.identifier.volume121-
dc.identifier.issue12-
dc.identifier.spage2738-
dc.identifier.epage2759-
dc.identifier.isiWOS:000692144700001-
dc.publisher.placeUnited Kingdom-

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