Risks Identification and Allocation in the Supply Chain of Modular Integrated Construction (MiC)

Authors

  • Ibrahim Y. Wuni Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong
  • Geoffrey Q.P. Shen Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong

DOI:

https://doi.org/10.29173/mocs93

Abstract

Modular integrated construction (MiC) is an offsite construction technique which can improve construction quality, the certainty of the project cost, provide value for money and reduce construction time, waste generation, and carbon emissions. However, MiC is associated with a unique business model, engineering, supply chain, and stakeholder composition, resulting in bespoke uncertainties and risks. Prominent among them is the uncertainties and risk events in its linked supply chain segments. However, risks identification and allocation in the MiC supply chain segments is not well-established. This research identified and assessed 28 risk events (REs) across the manufacturing, logistics and on-site assembly segments of the MiC supply chain. A principal component analysis generated 10, 6 and 12 REs within the modular manufacturing, logistics, and on-site assembly segments, respectively. A fuzzy synthetic evaluation (FSE) modeling revealed that the on-site assembly REs are the most critical set of risk events with a criticality index of 5.58, followed by the modular manufacturing risk events (5.28) and logistics risk events (5.08). These rankings and criticality assessment have profound implications for the practice and praxis MiC risks management. It is a source of relevant information to stakeholders and practitioners in understanding the MiC supply chain risk events and may prioritize the riskiest events to improve the performance of MiC projects. Again, the assessed REs contributes to the checklists of MiC risk events and may form the basis for future studies on the risk of MiC. Future studies may examine the assessed risk events in different countries using larger samples.

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Published

2019-05-24