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

预制 供应链 过程(计算) 政府(语言学) 质量(理念) 风险分析(工程) 系统动力学 晋升(国际象棋) 要素成本 工程类 计算机科学 运输工程 业务 土木工程 营销 操作系统 认识论 政治 哲学 宏观经济学 人工智能 经济 法学 语言学 政治学
作者
Wennan Zhang,Kai Kang,Ray Y. Zhong
出处
期刊:Industrial Management and Data Systems [Emerald (MCB UP)]
卷期号:121 (12): 2738-2759 被引量:14
标识
DOI:10.1108/imds-12-2020-0735
摘要

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.
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