Modelling productivity performance risks in modular integrated construction in Hong Kong

Abstract

The architecture, engineering, and construction (AEC) industry plays a vital role in the global economy, employing 7% of the workforce and contributing 13% of the world's GDP. Despite its significance, the industry has faced challenges such as low productivity and a lack of innovation compared to other sectors. With traditional technology reaching its limits, there is an urgent need to develop innovative solutions that can enhance productivity in the construction industry. The scarcity of residential land supply, along with the increase in the population, has posed significant challenges to the construction industry in Hong Kong. Modular integrated construction (MiC) as a solution to housing straits has been progressively gaining momentum and becoming more widely adopted in the public housing projects of Hong Kong due to its underlying benefits of a more efficient and productive production process and a safer and cleaner working environment. However, the implementation of MiC has faced numerous bottlenecks and constraints due to its complex, fragmented, and dynamic nature. Previous studies on MiC management have mainly focused on examining risk factors from a static viewpoint. However, this approach is insufficient, as MiC management issues are dynamic and require a comprehensive and dynamic approach to risk identification and evaluation that considers the interactions among influencing risk factors and analyses risks from a dynamic perspective with stakeholders' involvement. Furthermore, all MiC activities are closely interconnected and interactive, making it imperative to establish a collaborative mechanism that streamlines the MiC process. Therefore, it is crucial to take a more comprehensive and dynamic approach to address the challenges facing the implementation of MiC and improve the overall efficiency and effectiveness of the process. This study aims to provide a model for managing PPRs and develop an intelligent platform to mitigate PPRs in MiC. The research looks into major processes within MiC, unfolds the interaction mechanisms of various PPRs within the stakeholders’ involvement, and offers a dynamic approach for evaluating and simulating the potential influence of risks on MiC's productivity performance. The detailed objectives of this study are as follows: (1) To explore and determine the significant risk factors affecting the productivity performance of MiC projects; (2) To investigate and analyse stakeholder-associated critical PPRs that affect productivity performance and decipher their interactive relationships within the context of a MiC project in Hong Kong; (3) To establish a dynamic model for evaluating and simulating the influences of identified critical PPRs on the productivity performance of MiC; (4) To propose efficient strategies and establish an intelligent platform to address critical PPRs and enhance the productivity performance of MiC projects. First, this research investigated the major MiC process to identify the research gaps and problems, and a series of productivity performance risk factors were demonstrated through a comprehensive literature review. The social network analysis (SNA) method was employed to identify and decipher stakeholder-associated productivity performance risks (PPRs) and their interrelations in an MiC project. Critical PPR and links with associated major challenges were identified and prioritised. Then, a dynamic model was developed to evaluate and stimulate the impact of essential PPRs on the schedule and quality performance of MiC, given underlying interrelationships and interactions. The possible solutions were proposed through mixed methods, incorporating simulation results, site visits, and interviews. Subsequently, an IoT-­enabled smart BIM platform (SBIMP) was developed to address the critical PPRs embedded in the MiC project. A real-world project in Hong Kong validated the effectiveness of mitigation strategies. This study makes a significant contribution to the current knowledge of MiC management by developing an effective model that offers a detailed understanding of how the productivity performance of MiC is dynamically influenced by interrelated and interacting PPR variables.By depicting the interrelationships among various PPR variables, the model enables stakeholders to gain insight into the complex mechanisms inherent in the MiC system. The model's adaptability makes it easy to modify its structure to reflect real-world scenarios, conduct risk analyses, and communicate simulation results. Additionally, the proposed smart work platform functions as an efficient tool to enable proactive measures for mitigating potential risks. Furthermore, it serves as an experimental platform for addressing critical PPRs in MiC, which sets the groundwork for establishing a standardized platform applicable to all AEC construction projects.