Analyzing resilience influencing factors in the prefabricated building supply chain based on SEM-SD methodology

Abstract

The supply chain for prefabricated buildings (PB) currently grapples with pressing challenges. In order to ensure the safe and stable development of the prefabricated building supply chains (PBSC), this study aims to identify the key factors and internal mechanisms affecting the PBSC, and propose a supply chain resilience enhancement mechanism, so as to promote the sustainable development of the PB industry. The study combined a literature review and survey data to identify key resilience factors in PBSC. A Structural Equation Model (SEM) was used to explore the relationships between these factors. System dynamics were applied to create a simulation model, assessing the resilience impact level and conducting sensitivity analysis. The results show that the transportation and procurement processes are the most significant factors influencing supply chain resilience. The external environmental factors wielded a more pronounced impact on the overall evaluation of supply chain resilience than the delivery and use processes, but delivery and use processes are more sensitive. The study uses the Pressure-State-Response (PSR) model to suggest strategies for enhancing supply chain resilience. This study contributes to more sustainable and efficient construction practices by offering an innovative theoretical framework to analyze the factors influencing PBSC resilience and proposing enhancement strategies.

Keywords: Influencing factors; Prefabricated building supply chain; Resilience; Structural equation model; System dynamics.

Figure 1

Research framework and process. This diagram presents a four-step approach to understanding and enhancing supply chain resilience in the prefabricated building industry. Step 1 establishes an indicator system to pinpoint influencing factors of resilience. Step 2 involves constructing and validating a Structural Equation Model (SEM) to analyze the identified factors. Step 3 develops a System Dynamics Model (SD) to track and project the behavior of these factors over time. The final step, Application, applies the findings to propose practical strategies for strengthening resilience, guided by the Pressure-State-Response (PSR) theory. The framework transitions from static identification to dynamic modeling and practical application, reflecting an integrated process for strategic resilience enhancement.

Figure 2

Causality diagram of influencing factors. This schematic illustrates the interplay between key elements within the supply chain for prefabricated buildings. It highlights how factors like production costs, material quality, and technological capability directly influence the manufacturing process, while external factors like economic conditions, market demand, and natural disasters shape the overall environment. The arrows depict the directional influence between components, revealing the interconnected nature of supply chain activities.

Figure 3

Impact factor stock flow diagram. This diagram provides a snapshot of the dynamic flows and levels within the prefabricated building supply chain. It shows the accumulation and depletion of various factors such as changes in laws and policies, information sharing, and supply chain structure, that collectively influence the resilience of the supply chain. The circles represent stocks of impact factors, and the arrows show the flow of changes or adjustments within the system.

Figure 4

Perturbed PSR process for security resilience of prefabricated building supply chain. The graph depicts the resilience levels of a supply chain under the Pressure-State-Response framework. Initially, the system operates at its original level, signifying a smooth run. Upon encountering a disturbance, it experiences a drop to a minimum resilience level, indicating an absorption capacity challenge. In the status phase, resilience is assessed as the system’s ability to withstand pressure. As the system transitions to the response phase, it undergoes recovery and may reach a new level of resilience, ideally higher than the original due to adaptive measures and learned strategies. The x-axis represents the time period, illustrating the pre-disturbance stability, the impact of the disturbance, and the post-disturbance adaptation and learning processes.

Figure 5

Structural equation modeling of impact factors. This diagram visualizes the complex relationships and relative strengths among various impact factors affecting the resilience of the prefabricated building supply chain. Each circle, labeled A1 through A6, represents a latent variable or group of factors, with the corresponding rectangles (A11 through A63) depicting observed variables. The directed arrows from latent to observed variables signify hypothesized influences, with the values beside the arrows indicating the strength and direction of these relationships. The higher the absolute value, the stronger the influence. Error terms (e1 through e23) associated with observed variables account for measurement error or unexplained variance. This model provides a quantitative representation of how multiple factors interconnect to determine the overall resilience of the supply chain.

Figure 6

Trend chart of the dynamics of the level of factors affecting the resilience of the prefabricated building supply chain. This set of trend charts depicts varying levels of resilience across different stages of the supply chain for prefabricated buildings over an eight-week period. The overarching trend, represented in the top chart, indicates an overarching increase in resilience levels, with fluctuations that suggest varying degrees of impact across different phases. Individual charts for the transportation, manufacturing, procurement, planning, external environment, and delivery and use processes show differing trends. While some processes exhibit a steady increase in resilience, indicating a robust response to disturbances, others maintain a flatter line, implying a more moderate enhancement of resilience. These variations highlight the disparate levels of sensitivity and capacity for recovery inherent to each segment of the supply chain. The x-axis uniformly measures time across all charts, and the y-axis represents the relative level of resilience, showcasing the nuanced impact each factor has on the system’s overall robustness.

Figure 7

Trend chart of the level of factors influencing the resilience of prefabricated building supply chain after single factor change. The graph illustrates the trends in resilience levels in response to changes in individual factors over time, represented in weeks. Each line corresponds to a specific factor within the supply chain. The various colors differentiate between the factors, with each line tracing the progression of resilience levels as they are impacted by a single factor change. The temporal axis, scaled in weeks, captures a snapshot of the resilience trajectory, underscoring the varying degrees of sensitivity to changes across different supply chain components.

Figure 8

A Strategic framework for security resilience improvement in the prefabricated building supply chain. This diagram outlines a three-tiered approach to bolstering resilience, categorized into the Pressure, State, and Response layers. The Pressure layer, driven by project changes and the economic environment (A12, A42) as well as raw materials and technology (A43, A44), is where initial stressors are identified. The State layer, depicted in the center, addresses the system’s current condition, focusing on maintaining credit and standards (A11, A15) and quality and programs (A24, A31). The Response layer encompasses strategies like cost optimization and information synergy (A13, A21, A23, A33, A34) and partnerships and strategic planning (A14, A22, A32), aimed at enhancing the system’s functionality and efficiency. The connecting arrows suggest a flow from recognizing pressures, stabilizing the current state, to implementing responsive strategies, all contributing to a comprehensive resilience improvement strategy.