Enhancing Supply Chain Resilience in Prefabricated Buildings: The Role of Blockchain Technology in Volatile, Uncertain, Complex, and Ambiguous Environments

Abstract

This study explores how blockchain technology can enhance the resilience of the prefabricated building supply chain in volatile, uncertain, complex, and ambiguous (VUCA) environments. The measurement model of the subject, stage, and overall resilience of the supply chain is constructed. The four indices of blockchain are introduced, and the model from the resilience of the supply chain subject to the overall resilience is established. The interaction behavior between subjects is analyzed. The weight is determined by the AHP method, and the multi-agent model simulation is carried out using NetLogo(6.5) software. After the introduction of blockchain technology, even in the early stage of application, supply chain resilience has been significantly enhanced; especially in the decision-making stage, information transparency and efficiency have been significantly improved. When the technology is maturely applied, the toughness of each stage shows an accelerated growth trend, and the improvement in toughness in the assembly stage is particularly significant. By optimizing key influencing factors, the growth rate of resilience in the assembly stage is further improved, which verifies the positive impact of blockchain technology and main factor optimization on overall resilience. In summary, the introduction of blockchain technology and its mature application are crucial for improving the resilience of the prefabricated building supply chain, providing an effective way to meet the challenges of VUCA.

1. Introduction

The construction industry is a vital part of the national economy on a global scale. However, the industry still faces many challenges, including labor-intensive working methods, extensive production methods, fragmentation and non-standardization of organization and trading methods, unclear management responsibilities, uneven skill levels of workers, and relatively weak core competitiveness of enterprises. In order to cope with these challenges, prefabricated buildings, as a technical path of the “five in one” new construction methods such as design standardization, production factory prefabrication, construction assembly, decoration integration, and management informationization, are gradually being valued [1]. This construction method not only helps to save resources and energy, reduce environmental pollution, improve labor productivity, and ensure building quality and safety but also promotes the deep integration of the construction industry and information technology, which is in line with the trend in global green and high-quality development. In light of this, China’s “14th Five-Year Plan” clearly proposes actively promoting the development model of combining prefabricated buildings with green buildings. To this end, local governments at all levels have increased the application proportion of prefabricated buildings in urban planning and specific construction projects and taken measures to strengthen the implementation of prefabricated buildings. Vigorously developing the new building industrialization process led by prefabricated buildings is not only an effective way to solve the long-standing problems of low industrial chain collaboration efficiency and backward construction technology in China’s construction industry but also an important measure to achieve sustainable and healthy development of the construction industry.

Currently, the global landscape increasingly exhibits volatility, uncertainty, complexity, and ambiguity (VUCA). Within this context, the supply chain is also adopting VUCA characteristics [2]. Following the emergence of COVID-19 in 2020, ongoing global emergencies ranging from extreme weather events to geopolitical instabilities have repeatedly disrupted supply chains across the world. Consequently, terms like resilience and supply chain resilience have gained prevalence across diverse sectors, with resilience now regarded as an integral component of supply chain management education. The establishment of supply chain resilience represents a data-driven, long-term undertaking. Leveraging digitization facilitates the swift identification of potential risks, developmental bottlenecks, and areas of underperformance within the global supply chain, thus enhancing its resilience. Current industry practices underscore substantial enhancements in supply chain collaboration and resilience through the adoption of cutting-edge technologies such as the industrial Internet, big data, and artificial intelligence. The increased transparency in information flow within the supply chain mitigates information asymmetry, thereby fortifying enterprises’ ability to withstand risks.

Positioned at the heart of the 21st-century technological revolution, blockchain technology’s key advantage lies in decentralization. Leveraging chain data structure encryption, timestamp, proof of work, and consensus mechanisms, it addresses issues related to information security, inefficiencies in common trust, and high interaction costs associated with traditional centralized models. Following its inception as the underlying technology of Bitcoin, blockchain technology has progressively permeated diverse application domains, including financial services, smart healthcare, product anti-counterfeiting traceability, supply chain management, and public services, demonstrating robust performance. In the future, blockchain technology is poised to expand its reach into additional domains. The combination of ‘Blockchain + Engineering’ enables the automatic execution of comprehensive project quality information contracts, fosters information coordination among project participants, enhances supply chain transparency, reduces participant dispersion, and eliminates the bullwhip effect within the supply chain [3].

Historically, most research on supply chain resilience has primarily focused on identifying various factors that affect resilience, with limited in-depth analysis of the specific operational mechanisms within the supply chain. In contrast, this study specifically emphasizes the resilience of the prefabricated building supply chain and aims to explore its internal operational mechanisms to provide a more comprehensive understanding. This study focuses on the resilience of the prefabricated building supply chain. By introducing blockchain technology, it not only deepens the understanding of the factors affecting the resilience of the supply chain but also innovatively introduces four blockchain technical indicators on the basis of previous research. In addition, this study also constructs a complex and appropriate mathematical model of supply chain resilience, deeply analyzes the interaction details between the main bodies in the supply chain, and quantifies the indicators. Via the multi-agent simulation of NetLogo software, this study simulates the whole process of the prefabricated building supply chain, which provides valuable guidance for dynamically improving the resilience of the supply chain.

The research steps in this study are as follows: Section 2 provides a literature review. Section 3 involves the development of a supply chain resilience measurement model. Section 4 includes data collection and preliminary multi-agent simulations using the NetLogo software. Section 5 discusses the simulation results, evaluating the interaction behaviors among the supply chain subjects and the impact of blockchain technology on resilience. Section 6 summarizes the findings of this study.

2. Literature Review

2.1. Prefabricated Building Supply Chain

In the construction industry, the supply chain can be understood as an organizational process that encompasses everything from building planning, engineering design, and material manufacturing and delivery to subcontracting practices, facility management, and operations [4]. In this context, the prefabricated building supply chain integrates the entire process from raw materials to on-site installation, establishing a highly efficient, superior quality, environmentally friendly, and contemporary integrated production system [5]. This is similar to an efficient production line, where each component collaborates meticulously to seamlessly execute the prefabricated building construction process. Currently, researchers from both domestic and international spheres are exploring the prefabricated building supply chain comprehensively, predominantly emphasizing the synthesis or examination of supply chain risks, performance assessment, and cost management. These scholarly works enhance the theoretical foundation of the prefabricated building supply chain from diverse viewpoints.

In their risk research, Zhang et al. [6] posited that the inclination to share information and the level of trust among participants are crucial elements for expediting and enhancing the development of assembly buildings. They identified assembly supply chain risks through information dissemination models and implement blockchain technology in constructing information dissemination models to mitigate the risks associated with information dissemination in the assembly building supply chain. Du et al. [7] summarized the risks associated with design changes and the contributing factors, outlining relevant management strategies using a blend of SEM and multi-agent simulation models. They summarized the factors inducing risk occurrence and suggested corresponding management strategies by utilizing the combination of SEM and multi-agent simulation models to simulate the operational impact of these strategies, enabling the prediction and control of design change risks. Sun et al. [8] employed complex network theory to create a risk network model and investigated the impact of risk transfer. They analyzed the risk transfer effects and advocated for controlling design changes, enhancing information sharing, and ensuring timely supply chain operations to effectively mitigate the risks within the assembly building supply chain. Luo et al. [9] developed a risk assessment framework for the assembly building supply chain utilizing the SNA method, underscoring the need to prioritize stakeholder risks and ultimately pinpointing the inadequacy of information sharing as the primary risk source. Baghdadi et al. [10] put forth a methodology to optimize supply chain risk management by altering pertinent design decisions from a managerial standpoint.

In terms of performance evaluation, Zhang et al. [11], drawing on the reference model of supply chain operation and taking into account the unique attributes of the prefabricated building supply chain, constructed a set of performance evaluation index systems including six dimensions such as procurement, production, and distribution and then used the fuzzy network analytic hierarchy process to successfully establish a performance evaluation model for quantitative evaluation. From the perspective of sustainable development, Zhao and SM [12] established a performance evaluation system of prefabricated building supply chain based on SEM and virtual frontier SBM-DEA, and revealed the impact of supply chain operation, economic benefits, environmental protection, and social responsibility on supply chain performance. Sholeh et al. [13] used the SCOR model to construct a construction supply chain performance evaluation system, analyzed and simulated the indicators, and concluded that focusing on risk prevention in the contract phase and strengthening on-site management can effectively improve supply chain performance.

In terms of cost research, Wang et al. [14] established a calculation model based on activity-based costing, found the key areas to reduce the cost of prefabricated building construction supply chain through this model, and verified the feasibility of this model to predict the cost of prefabricated building construction supply chain under uncertain conditions. After studying the influencing factors of the performance of New Zealand prefabricated buildings, Massod et al. [15] found that cost and quality have the greatest impact on the performance of New Zealand prefabricated buildings, followed by delivery period and product flexibility.

2.2. Supply Chain Resilience

While Rice and Caniato initially introduced the notion of supply chain resilience [16], Christopher and Peck later precisely defined it as the capacity to restore the supply chain to its original state or an improved state following disruptions [17]. As the evolution of supply chain resilience continues, an increasing number of scholars have engaged in multidimensional research on this topic. Zhu et al. [18] employed questionnaires and expert interviews to identify the 15 critical influencing factors affecting the resilience of the prefabricated building supply chain. They applied the ISM method to unveil the internal logical connections among these influencing factors. Cao et al. [19] utilized econometric models to empirically investigate the resilience and vulnerability of the rail transit industry chain across 27 provinces in China from 2008 to 2017. Fan et al. [20] developed a resilience evaluation index system comprising five dimensions, including supply chain forecasting capability. They applied the ISM and entropy-TOPSIS methods to empirically investigate supply chain resilience in the automobile industry. Aggarwal et al. [21] combined the concept of collaborative resilience with the supply chain, identified eight key influencing factors of supply chain collaborative resilience, and used the gray DEMATEL method to model and analyze these factors, which provided guidance for supply chain managers to build collaborative resilience. Riccardo et al. [22] introduced a risk-averse mathematical model for designing and strategizing resilient supply chain networks, demonstrating through practical examples that recovery actions are the most efficient responses to short-term disruptions. Moreover, enhancing effective redundancy configuration can bolster supply chain resilience. Sahu et al. [23] presented a hierarchical evaluation framework for resilient supply chain networks founded on multiple indicators, utilizing fuzzy set theory as the basis for this approach.

2.3. Application of Blockchain Technology in Construction Supply Chain

Blockchain has the potential to mitigate the bullwhip effect in supply chain management, address logistics forgery issues, improve transparency, reliability, and traceability, and reduce high dispersion within the supply chain. The application of blockchain in the construction supply chain represents an emerging area of research and development within the construction industry. Current scholarly works predominantly focus on analyzing the potential of blockchain for the construction supply chain and devising relevant systems and applications. Cao et al. [24] developed an information-sharing system framework for the construction supply chain based on blockchain technology. This framework successfully addressed issues such as lack of trust between project participants, challenges in information traceability and supervision, as well as the opacity and insecurity of information. Moreover, it presented a novel model for information sharing among construction enterprises. Yao et al. [25] leveraged the features of alliance chains and smart contracts to address trust issues during the standard renewal process in the construction industry. They implemented a process feedback system to gather feedback, enhance standard iterations, and achieve comprehensive life cycle management of standards. Li et al. [26] introduced a blockchain framework incorporating a multi-agent approach and a comprehensive life cycle quality management system for engineering construction. This framework aims to enhance the quality management of engineering projects and overcome traceability challenges. Yang et al. [27] enhanced the efficiency of project information transmission by optimizing the information management platform with blockchain technology and integrating a project information collection system. In view of the main problems facing the construction industry, Zhang et al. [28] delved into the utilization of blockchain in engineering projects, providing an in-depth examination of the application methods and scenarios of this technology. Tezel et al. [29] conducted a comprehensive analysis of the potential use of blockchain technology in the construction, engineering, and construction (AEC) sector, incorporating a SWOT analysis. Furthermore, they developed a conceptual framework to facilitate blockchain-enabled construction supply chains. Yoon et al. [30] pinpointed the five critical issues in the construction supply chain and delineated twelve areas where blockchain technology can be effectively integrated into supply chain operations. They highlighted the key concerns in the construction supply chain pertaining to sustainability, collaboration, and information sharing and suggested an implementation strategy involving rewards and penalties to incentivize collaboration and information sharing.

Via the above review, it can be seen that the research on the prefabricated building supply chain and its resilience is basically mature, but there is little research on the impact of blockchain technology on the resilience of prefabricated buildings. The increasing maturity of blockchain technology will have a significant impact on the resilience of the prefabricated building supply chain. In view of the shortcomings of the research in this field, the nine subjects of the prefabricated building supply chain and the four indicators based on blockchain technology are innovatively combined and quantified. Finally, the multi-agent simulation research of NetLogo is carried out. It has made a significant theoretical contribution and directional guidance to the study of the impact of blockchain technology on the resilience of the prefabricated building supply chain.

3. Materials and Methods

3.1. The Influence Factors System

In this study, operational resilience is chosen as the criterion for determining the factors influencing resilience. Operational resilience denotes the capacity to restore, adapt, and adjust operational processes within and across organizations amidst adversity, encompassing task completion, job performance compliance, and timely product delivery [31]. Utilizing a literature summary approach, the keywords ‘prefabricated building’, ‘supply chain’, ‘resilience’, and ‘blockchain’ are chosen in CNKI, Web of Science, and Engineering Village. After reviewing pertinent literature on the resilience of prefabricated building supply chains, nine resilience subjects are developed in accordance with principles of scientific rigor and rationality (Table 1).

On the basis of synthesizing multiple studies, 9 main bodies (first-level indicators) and 32 influencing factors (second-level indicators) were determined. On one hand, it is helpful for readers to understand the various participants of the prefabricated building supply chain more clearly. On the other hand, it is important to construct a reasonable resilience mathematical model for each stage based on the interaction behavior of each subject of the supply chain.

3.2. Blockchain Technology’s Impact on the Resilience of Prefabricated Building Supply Chains

Blockchain technology can significantly affect the operational resilience of the prefabricated building supply chain by improving task completion, work performance standards, and timely delivery of products. Smart contracts can perform pre-defined rules and standards to ensure that tasks are completed accurately and on time. Its specific application in the assembly building supply chain is shown in Table 2. Therefore, via smart contracts and distributed ledger systems, the operation process can not only be automated but also become relatively simple, thereby reducing manual errors and delays. The real-time visibility and traceability of the blockchain allow participants to track the movement of materials and components throughout the supply chain, thereby achieving active decision-making and timely adjustment, reducing the risk of stockouts or delays, achieving better inventory management, and improving overall operational efficiency. In addition, the decentralization and immutability of the blockchain help to maintain the integrity of the operating data and reduce the risk of data loss or manipulation. This data reliability and integrity improves the resilience of the operation process, enabling the supply chain to recover quickly in the event of disruption.

Blockchain technology is gaining significant attention for its potential to enhance transparency, security, and traceability in supply chain management. Blockchain platforms utilize an access permission system where each participant is a trusted entity identified by a unique cryptographic code. This section outlines the parameters influencing the impact of blockchain technology on the resilience of the assembly building supply chain through the construction of a mathematical model. By quantifying this parameter, we can assess the extent to which blockchain technology contributes to improving supply chain resilience. By constructing the transparency index (tp), traceability index (ta), security index (sc), and efficiency index (ef), we can quantify the extent to which blockchain technology contributes to improving supply chain resilience. (Table 3 shows these four indices).

3.3. Resilience Measurement Modeling

The resilience measurement model is composed of multi-agent modeling, stage resilience modeling, and overall resilience modeling. This multi-level, multi-dimensional modeling approach enables a more thorough analysis of the internal structure and operational mechanisms of the prefabricated building supply chain, facilitating the identification of weaknesses in each link, optimizing resource allocation, and further enhancing the overall resilience of the supply chain in complex and dynamic environments.

3.3.1. Multi-Agent Modeling

This part is composed of nine participants in the supply chain. The mathematical models of nine participants are presented in the form of tables, and nine models are presented in Table 4.

In the above formula, αi, βj, μ_h, δi, γv, εi, ζj, and ηi are the corresponding weights of each influencing factor, indicating the degree of influence of each attribute on the end-user resilience. ui, gj, ch, di, sv, pi, w, lj, and ai are different attribute values of each agent (i = 1... 5, j = 1... 4, h = 1... 3, v = 1, 2); ${\sigma }_1$, ${\sigma }_2$…${\sigma }_9$ denote the influence or error term of other attributes except the above attributes.

3.3.2. Stage Resilience Modeling

The five stages of decision-making, design, procurement, production, transportation, and assembly span the entire lifecycle of the prefabricated building supply chain. Via stage-based resilience modeling, the specific issues within each supply chain link can be thoroughly identified, providing systematic and precise research and decision-making support for enhancing the resilience of the prefabricated building supply chain. The stage resilience model is presented in Table 5.

In the above formula, ω1, ω2... and ω7 are the weights of the resilience of each agent in the prefabricated building supply chain. UR, CR..., AR represent the end user, the general contractor..., and the resilience of the nine main agents of the on-site construction unit.

3.3.3. Total Resilience Modeling

The Total Resilience (TR) of the prefabricated building supply chain consists of the resilience of the five aforementioned stages, each multiplied by its respective weight. This quantitative resilience measurement method enables a more intuitive evaluation of both the local and overall resilience of the prefabricated building supply chain. These findings offer valuable insights for improving the overall resilience and performance of the supply chain. Let TR represent the primary framework for total resilience measurement.

(1)$\mathrm{T}\mathrm{R}=\left({\mathsf{\varphi }}_1\ast \mathrm{D}\mathrm{M}\mathrm{S}\mathrm{R}+{\mathsf{\varphi }}_2\mathrm{D}\mathrm{S}\mathrm{R}+{\mathsf{\varphi }}_3\ast \mathrm{P}\mathrm{S}\mathrm{R}+{\mathsf{\varphi }}_4\ast \mathrm{T}\mathrm{S}\mathrm{R}+{\mathsf{\varphi }}_5\ast \mathrm{A}\mathrm{S}\mathrm{R}\right)\ast \left(1+\mathrm{t}\mathrm{p}+\mathrm{t}\mathrm{a}+\mathrm{s}\mathrm{c}+\mathrm{e}\mathrm{f}\right)$

Among them, φ1, φ2..., and φ5 are the weights of the resilience of each stage of the prefabricated building supply chain. DMSR, DSR, PSR, TSR, and ASR represent the resilience of the five main stages of the decision-making stage, design stage, procurement production stage, transportation stage, and assembly stage, respectively.

3.4. Multi-Agent Behavior Rules

Via the analysis of the behavior of each agent and its decision-making process, the interaction relationship of each agent in this study is shown in Figure 1. According to Holland s‘ theory, the basic logical structure of the subject behavior rules required by the multi-agent model is as follows: IF stimulates S to occur, and THEN subjects respond R.

The main rules of behavior related to each agent are set out below:

• (1). General contractor

IF the end-user’s financing ability and capital level increase, THEN the general contractor’s work experience increases;

If the safety accident rate of the on-site construction unit is reduced THEN the final safety accident rate of the general contractor is reduced.

It is converted into a mathematical model as follows:

(2)$c_1=C_1\ast \left(0.5\ast {\displaystyle \frac{u_1}{U_1}}+0.5\ast {\displaystyle \frac{u_2}{U_2}}\right)$

(3)$c_2=C_2\ast {\displaystyle \frac{a_5}{A_5}}$

(4)$c_3={\displaystyle \frac{a_4}{A_4}}$

where C1, C2, C3, A4, A5, U1, and U2 represent the work experience of the general contractor, the incidence of safety accidents and the quality of the project, the assembly quality of the on-site construction unit and the incidence of safety accidents, the initial value of the final user’s financing ability and capital level.

• (2). Design unit

IF the experience of the end user is improved THEN the assembly design experience of the design unit is improved;

IF the end user’s understanding of the assembly process and the manufacturer’s production skills are improved THEN the design change frequency of the design unit is reduced.

It is converted into a mathematical model as follows:

(5)$d_1=D_1\ast {\displaystyle \frac{u_3}{U_3}}$

(6)$d_2=-D_2\ast \left(0.5\ast {\displaystyle \frac{u_4}{U_4}}+0.5\ast {\displaystyle \frac{p_1}{P_1}}\right)$

where D1, D2, U3, U4, and P1 represent the design experience and design change frequency of the design unit, the experience of the end user and the understanding of the assembly process, and the initial value of the production skills of the manufacturer’s personnel, respectively.

• (3). Suppliers

IF the general contractor’s work experience is improved THEN the supply efficiency and supply quality of the selected supplier will be improved.

It is converted into a mathematical model as follows:

(7)$s_1=S_1\ast {\displaystyle \frac{c_1}{C_1}}$

(8)$s_2=S_2\ast {\displaystyle \frac{c_1}{C_1}}$

where S1 and S2 represent the initial values of supplier component supply efficiency and supply quality, respectively.

• (4). Warehouse management unit

If the supplier’s prefabricated component supply efficiency is improved THEN the inventory management level of the warehouse management unit is improved.

It is converted into a mathematical model as follows:

(9)$w=W\ast {\displaystyle \frac{s_1}{S_1}}$

where W and S1, respectively, represent the initial value of the inventory management level and supplier component supply efficiency of the warehouse management unit.

• (5). Producer

IF the general contractor’s work experience is improved THEN the production skills of the selected manufacturer will be improved;

IF the supplier’s component quality is improved THEN the manufacturer’s component quality is improved;

IF the assembly design experience of the design unit and the standardized design system is improved THEN the degree of integration and modularization of the manufacturer’s components is improved;

IF the supplier’s supply efficiency and the warehouse management unit’s inventory management level are improved THEN the manufacturer’s prefabricated component delivery efficiency is improved.

It is converted into a mathematical model as follows:

(10)$p_1=P_1\ast {\displaystyle \frac{c_1}{C_1}}$

(11)$p_2=P_2\ast {\displaystyle \frac{s_2}{S_2}}$

(12)$p_3=P_3\ast \left(0.5\ast {\displaystyle \frac{d_1}{D_1}}+0.5\ast {\displaystyle \frac{d_3}{D_3}}\right)$

(13)$p_4=P_4\ast \left(0.5\ast {\displaystyle \frac{s_1}{S_1}}+0.5\ast {\displaystyle \frac{w}{W}}\right)$

where P2, P3, and P4 represent the manufacturer’s component quality, component integration and modularity, and the delivery efficiency of prefabricated components, respectively.

• (6). Logistics unit

IF the general contractor’s work experience is improved THEN the transportation capacity of the selected logistics unit will be improved;

If the component delivery efficiency of suppliers and manufacturers is improved THEN the transportation efficiency of logistics units is improved.

It is converted into a mathematical model as follows:

(14)$l_1=L_1\ast {\displaystyle \frac{c_1}{C_1}}$

(15)$l_3=L_3\ast \left(0.5\ast {\displaystyle \frac{s_1}{S_1}}+0.5\ast {\displaystyle \frac{p_4}{P_4}}\right)$

where L1 and L3 represent the transport capacity and transport efficiency of logistics units, respectively.

• (7). On-site construction unit

IF the work experience of the general contractor is improved, namely the labor proficiency THEN the rationality of the construction plan and the assembly capacity of the on-site construction unit will be improved.

IF the transportation quality assurance capability of the logistics unit is improved THEN the assembly quality of the on-site construction unit is improved.

It is converted into a mathematical model as follows:

(16)$a_1=A_1\ast {\displaystyle \frac{c_1}{C_1}}$

(17)$a_2=A_2\ast {\displaystyle \frac{c_1}{C_1}}$

(18)$a_3=A_3\ast {\displaystyle \frac{c_1}{C_1}}$

(19)$a_4=A_4\ast {\displaystyle \frac{l_4}{L_4}}$

where A1, A2, A3, and L4 represent the initial value of the skilled labor force of the on-site construction unit, the rationality of the construction plan, and the transportation quality assurance capability of the logistics unit.

3.5. Multi-Agent Simulation Model Based on NetLogo

Multi-agent modeling and simulation is a crucial methodology for studying complex adaptive systems (CAS). It reveals the dynamic characteristics, self-organizing mechanisms, and adaptive evolutionary pathways of CAS by simulating the behavior, interactions, and system evolution of individual agents within the system. Therefore, when analyzing and understanding dynamic systems with numerous interacting entities, such as the prefabricated building supply chain, its application value becomes particularly significant. By constructing a multi-agent model of the prefabricated building supply chain, the operational processes of each link in the chain can be simulated, allowing for a deeper understanding of its dynamic evolution mechanisms. Additionally, it offers decision-makers valuable insights by testing the impact of supply chain management strategies. In addition, the technology-driven supply chain change path can be explored by simulating the application effect of new technologies (such as blockchain) in the supply chain.

NetLogo is a programming platform continuously developed by the Center for Connected Learning and Computer-Based Modeling at Northwestern University. It is well-suited for multi-agent modeling and can simulate the interactions between micro-agents and the emergence of macro-level systems. The main simulation parameters are defined through programmatic modeling. The multi-agent simulation of the prefabricated building supply chain is conducted by analyzing subject attributes, influencing factors, and interactions between agents. According to the discussion of the results of multi-agent simulation experiments, development suggestions are put forward. The specific steps are shown in Figure 2.

4. Data and Research Results

4.1. Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) represents a robust methodology for analysis and evaluation, offering significant advantages in scientific analysis and system evaluation. AHP integrates the inherent decision-making objectives of complex systems, constructs a detailed hierarchical measurement index system, and determines weight values by assessing the relative importance of different levels of indicators. In this paper, we use the following three steps to determine the weight of each factor: (1) construct the judgment matrix by the 1–9 scale method; (2) determine the weight of each index by the root method; (3) use the consistency ratio (CR) to measure whether the judgment matrix meets the consistency requirements.

4.2. Determination of Evaluation Index Weight

In this survey, nine relevant front-line staff and experts were recruited to form a professional evaluation group. The 1~9 scale method was used to systematically score the factors that affect each other and provided a data basis for subsequent experimental simulation, with the resultant data encapsulated in Table 6.

4.3. Initial Model Parameter Design

For the simulation experiments, it is essential to specify the parameters for each variable. In this study, the degrees of influence of the resilience factors associated with the nine subjects on the resilience of the prefabricated building supply chain are determined by consulting relevant literature, conducting expert interviews, and analyzing other statistical results, which allow for the establishment of reasonable values for each parameter. The initial values of each influencing factor are presented in Table 7. Based on practical experience, it is evident that the attribute values of various subjects do not change significantly over short periods. Considering the above factors, the article takes the month as the cycle and sets the system running step n (subroutine stop-ticks) of the multi-agent model to 120. To better demonstrate the intelligence and effectiveness of the model, different reaction times are assigned to each subject based on their distinct stimulus-response modes, allowing for the observation of the measured values during system operation.

By configuring the technical parameters of the blockchain alongside the primary parameters, the initial state of the model is established. Subsequently, while maintaining other control parameters constant, the levels of specific variables are adjusted individually to observe how these changes impact the resilience of each stage as well as the overall assembly building supply chain. The specific parameter values of the initial model are shown in Figure 3.

4.4. Model Simulation Results

The factors influencing the resilience of the prefabricated building supply chain are in a state of constant flux due to external changes, leading to variations in the resilience level of the supply chain. The initial data such as the influencing factor parameters and behavior rule coefficients of each subject are input into the multi-agent simulation model of the prefabricated building supply chain, and the initial simulation results of the effective resilience of each stage and the overall resilience level of the prefabricated building supply chain are obtained, as shown in Figure 4. These simulation results serve as the comparative benchmark for subsequent simulation experiments.

According to Figure 4, the output of the initial model system is as follows:

• (1). During the operation period set by the model, the development level of each stage and the overall resilience of the supply chain show a significant dynamic change trend. At the initial stage, the toughness performance was relatively good, and the peak value reached about 1 × 10^-30. With the passage of time, the toughness showed a rapid and obvious decline and then began to maintain a low but still stable numerical range. The development of the model has experienced a process from high to low and then tends to be stable. It has not been continuously enhanced throughout the operating cycle, indicating that maintaining and improving supply chain resilience is a task that requires continuous attention and dynamic adjustment.

• (2). Resilience performance is best at the design stage, and as the supply chain process moves downstream, the level of resilience drops to the lowest point at the production stage. The reason is that the design phase often has higher flexibility and is forward-looking, can foresee and avoid some potential problems, and improves the system’s ability to resist risks through innovative design and redundancy planning. In the decision-making, production, transportation, and assembly stages of the supply chain, the complexity of the process is increasing. Subject to various practical problems such as equipment aging, operational errors, logistics delays, and information asymmetry, the level of resilience at each stage is gradually reduced.

• (3). Further analysis of the fluctuations in overall supply chain resilience and its stages during the operation of the model, it can be found that the resilience values of each stage fluctuate within their respective stable intervals except for the decline of resilience in the initial stage. This strongly proves that without the introduction of external technical interventions, the internal factors such as the initial state, management level, and resource allocation status of each participant in the supply chain will have a significant impact on the resilience of the supply chain in the short term. However, as the supply chain continues to operate, these short-term effects will gradually be digested and absorbed, and supply chain resilience will begin to return and remain in an inherently stable state based on existing conditions. This also shows that in the VUCA environment, only relying on the existing management mode and resource allocation, the good development of the prefabricated building supply chain will be subject to considerable limitations.

5. Discussion

5.1. Discussion of Multi-Agent Simulation Results

5.1.1. The Impact of Blockchain Technology on Parameter Changes

The simulation experiment for the resilience model of the prefabricated building supply chain aimed to systematically evaluate and compare the impact of blockchain technology on both the overall resilience of the supply chain and its various components. To achieve this objective, all other parameters were held constant. Subsequently, the four blockchain technical parameters that directly impact supply chain resilience were adjusted to varying degrees. This approach facilitated clearer observations of trends and the effects of these parameter changes on supply chain resilience, enabling effective comparative analysis.

In the initial simulation model, the four parameters of transparency index tp, traceability index ta, safety index sc and efficiency index ef are all 0, that is, the resilience of prefabricated building supply chain without blockchain technology in Figure 4; by adjusting the sum of the four parameters between 0 and 0.5 (the sum of the parameters is adjusted to 0.3 in this experiment), the resilience of the prefabricated building supply chain can be observed when the blockchain technology is not mature. The simulation results are shown in Figure 5. Finally, the sum of the four parameters is adjusted between 0.5 and 1 (the sum of the parameters is adjusted to 0.8 in this experiment, which can observe the resilience of the prefabricated building supply chain when the blockchain technology is mature), and the simulation results are shown in Figure 6.

By comparing the output results of the three systems, the following conclusions can be made:

• (1). Compared with the simulation results of the initial model (Figure 4), after using the blockchain technology, even if the technology application is not mature, the resilience of the prefabricated building supply chain increases by multiple orders of magnitude, both in each stage and the overall resilience level. This is because blockchain technology reduces information asymmetry and improves decision-making efficiency by providing a decentralized and credible information-sharing platform. Therefore, after a long period of accumulation (i.e., after the seventh year), the improvement speed of each stage of the supply chain and the overall resilience has been significantly accelerated, even showing a nearly linear upward trend. Especially in the decision-making stage, because blockchain technology can transmit and track information instantly and accurately, the accuracy and timeliness of decision-making are greatly improved, and the resilience of this stage is improved most rapidly. This phenomenon is in stark contrast to the simulation results of the initial model.

• (2). Compared with the simulation results under the immature application of blockchain technology, the resilience of each stage and the overall resilience of the prefabricated building supply chain significantly improved when blockchain technology was fully applied. Not only did the resilience increase dramatically but the rate of improvement was also faster. This is attributed to the full utilization of blockchain technology in practice, which enhances its advantages in information processing, trust mechanisms, and risk prevention. The observed results also indicate that, under this condition, the development speed of resilience across the different stages and the overall supply chain follows a sequential order: decision-making stage, overall resilience, transportation stage, production stage, assembly stage, and design stage. This is in sharp contrast to the simulation results of the initial model.

5.1.2. Changes in Key Influencing Factors of Each Agent

Given the interactions among multiple agents, this study builds upon the model simulation experiments with mature blockchain technology applications. While keeping other parameters constant, it selects the attribute with the highest weight from each agent’s supply chain resilience attributes, adjusts its value, and analyzes the specific impacts of these key attribute changes on supply chain resilience. Specifically, the scientific and reasonable maintenance (u5), support strength (g2), engineering quality (c3), design change frequency (d2), accuracy of design documents (d4), component quality (s2), component quality (p2), prefabricated component transportation quality assurance (l4), and assembly capability (a4) are adjusted, respectively. The values of the nine factors are simulated. The simulation results are shown in Figure 7 and Figure 8.

During the model operation cycle, when compared to the simulation results of models using mature blockchain technology, adjusting the key influencing factors for each agent (i.e., increasing the values of positive influencing factors and decreasing the values of negative influencing factors) shows no significant change in the rapid development points of resilience across stages. Interestingly, the slope of the resilience curve in the assembly stage has significantly increased, indicating an accelerated rate of improvement in resilience during this phase. Thus, it can be inferred that under conditions of mature blockchain technology application, an ongoing increase in the values of positive influencing factors and a decrease in the values of negative influencing factors for each agent will contribute to a comprehensive enhancement of the resilience of the prefabricated building supply chain.

6. Conclusions

This paper deeply studies the resilience of prefabricated building supply chains based on blockchain technology. By identifying the nine key subjects in the supply chain, the resilience problems faced by these subjects in the operation process are analyzed, and the key factors affecting their resilience are determined. Further, according to the characteristics of blockchain technology, four technical impact parameters of transparency, traceability, security, and efficiency are set. Based on these factors, a multi-level resilience measurement model is constructed, including subject resilience, stage resilience, and overall resilience model. Then, the decision-making and interaction behaviors among the subjects in the supply chain are described by a mathematical model, and the model is initialized and simulated by the NetLogo simulation platform.

The research results are described from the following three aspects:

• (1). This study first reveals the practical challenges in the resilience development of prefabricated construction projects and examines the compatibility of blockchain technology in addressing these issues. By analyzing the difficulties faced in the resilient development of prefabricated construction projects, this paper explores the root causes of these challenges and how blockchain technology can potentially mitigate them.

• (2). This paper constructs a resilience measurement model for the prefabricated building supply chain based on blockchain technology. The model starts by assessing the resilience of key subjects (such as end-users, government agencies, general contractors, design units, etc.) and gradually extends to evaluate the resilience of various stages (e.g., project decision-making, engineering design, procurement, production) and the overall supply chain. In constructing the model, particular attention is given to the interaction behaviors between participants, their impact on supply chain resilience, and the quantified technical contributions of blockchain to supply chain resilience. Through a local-to-global analysis, this model reveals the transmission mechanisms of resilience under the ripple effect within the supply chain and comprehensively demonstrates the enhancement effect of blockchain technology on the resilience of the prefabricated building supply chain.

• (3). The improvement effect of blockchain technology on the resilience of the prefabricated building supply chain was verified through multi-agent simulation. To assess the application value of blockchain technology for enhancing the resilience of the prefabricated building supply chain, a multi-agent simulation was conducted using the NetLogo platform with nine key subjects. The simulation modeled the supply chain’s operations under varying levels of blockchain technology maturity, and the resulting changes in supply chain resilience were observed. The results clearly demonstrate the significant role of blockchain technology in enhancing the resilience of the prefabricated building supply chain. As the maturity of blockchain technology increases, both the extent and rate of improvement in local and overall supply chain resilience become more pronounced. These results strongly confirm the substantial potential and practical value of blockchain technology in enhancing the resilience of the prefabricated building supply chain.

The limitations of this study include a certain degree of simplification of the reality when constructing the model, which may not reflect all the interaction details in reality, and the potential impact of changes in policies and regulations in different regions on the application of blockchain technology and supply chain resilience is not fully considered. Future research can explore the deep application of blockchain technology in the prefabricated building supply chain, including the practice of smart contracts and decentralized storage, so as to enhance the overall resilience of the supply chain.

Author Contributions

Conceptualization, P.Y. and L.L.; methodology, J.L.; software, L.L.; validation, P.Y. and J.C.; investigation, J.L.; data curation, L.L.; writing—original draft preparation, P.Y.; writing—review and editing, P.Y. and J.C.; visualization, P.Y.; supervision, J.L.; All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. The main agent interaction diagram.

Figure 2. Blockchain technology-based assembly building supply chain resilience modeling and simulation idea.

Figure 3. The simulation model in the initial state.

Figure 4. The initial model system output results.

Figure 5. Supply chain resilience when the application of blockchain technology is immature.

Figure 6. Supply chain resilience when blockchain technology is mature.

Figure 7. Supply chain resilience when the key influencing factors do not change.

Figure 8. Supply chain resilience when the key influencing factors of each subject change.

Table 1

Indicator system of factors affecting PBSC resilience.

Related Agent	Factors	References
End user (UR)	Financing ability (u1)	[6,31,32]
	Capital level (u2)	[32]
	Developer experience (u3)	[31,33]
	Knowledge of prefabrication techniques (u4)	[33,34]
	Scientific and reasonable maintenance (u5)	[31,33,34]
Government agencies (GR)	Policy change and regulation (g1)	[31,35]
	Support strength (g2)	[31,36]
	Completeness of Regulations and Standards (g3)	[31,34,36]
	efficiency of the approval process (g4)	[31,34]
General contractor (CR)	Work experience (c1)	[31,34]
	Incidence of safety accidents (c2)	[31,34]
	Engineering quality (c3)	[31,34]
Design unit (DR)	Assembly design experience (d1)	[6,33,36,37]
	Design change frequency (d2)	[17,33,34,36]
	Standardized design system (d3)	[31,38]
	Accuracy of design documents (d4)	[17,31,33,38]
	Information technology level (d5)	[39,40]
Suppliers (SR)	Prefabricated component supply efficiency (s1)	[17,31,38]
	Quality of components (s2)	[7,34,36,37]
Producer (PR)	Personnel production skills (p1)	[17,33,35,41]
	Quality of components (p2)	[7,17,37]
	Integration of components and degree of modularity (p3)	[17,34,41]
	Efficiency of prefabricated component delivery (p4)	[7,35]
	Efficiency of component production (p5)	[6,37]
Warehouse management unit (WR)	Inventory management (w)	[31,41]
Logistics unit (LR)	transport capacity (l1)	[17,37,41]
	Transport redundancy (l2)	[17,42]
	Transport efficiency (l3)	[6,17,33]
	Transport quality assurance (l4)	[7,37]
On-site construction unit (AR)	Skilled labor (a1)	[6,37]
	Reasonableness of the construction program (a2)	[31]
	Assembly capacity (a3)	[35,41]
	Quality of assembly (a4)	[43]
	Incidence of security incidents (a5)	[31,34]

Table 2

Example of smart contracts for assembly building supply chain.

Contract Agent	Contract Type	Contract Content
Government agencies	Document approval contracts	The general approval items are set through the smart contract. Once the electronic contract to be approved is uploaded, the system automatically approves.
Supplier	Cash on delivery contract	The manufacturer scans the code to confirm the receipt of the smart contract to achieve automatic checkout.
Warehouse management unit	Warehouse control contract	Real-time tracking of product storage dynamics, and automatic execution of default penalties to deal with timeout or out-of-stock situations.
Logistics unit	Transport Control Contract	Real-time monitoring of the transportation process, such as the occurrence of delivery timeouts, automatically triggers a penalty contract.
On-site construction unit	Delivery delay contract	Automatic triggering of penalty contracts in the event of a delay in the arrival of products for delivery.

Table 3

Blockchain technology indicators.

Indicator	Function
Transparency index (tp)	Quantifying the level of information transparency enabled by blockchain technology.
Traceability index (ta)	Quantify the ability of blockchain technology to track and trace materials throughout the supply chain.
Security index (sc)	Quantifying the level of data security enabled by blockchain technology.
Efficiency index (ef)	Quantifying the efficiency of data sharing and transaction processing enabled by blockchain technology.

Table 4

Agent resilience model.

Agent	Resilience Model
End user (UR)	$UR={\alpha }_1\ast u_{1\ast }(1+sc+ef)+{\alpha }_2\ast u_2+{\alpha }_3\ast u_3+{\alpha }_4\ast u_4+{\alpha }_5\ast u_5\ast (1+tp$ $+ta+ef)+{\sigma }_1$
Government agencies (GR)	$\mathrm{G}\mathrm{R}={\mathsf{\beta }}_1\ast {\mathrm{g}}_1+{\mathsf{\beta }}_2\ast {\mathrm{g}}_2+{\mathsf{\beta }}_3\ast {\mathrm{g}}_3+{\mathsf{\beta }}_4\ast {\mathrm{g}}_4\ast (1+\mathrm{t}\mathrm{p}+\mathrm{t}\mathrm{a}+\mathrm{s}\mathrm{c}+\mathrm{e}\mathrm{f})+{\mathsf{\sigma }}_2$
General contractor (CR)	$CR={\mu }_1\ast c_1\ast (1+tp+ta+sc)+{\mu }_2\ast c_2\ast \left(\right(tp+ta+ef)-1)+{\mu }_3\ast c_3\ast (1$ $+tp+ta+sc)+{\sigma }_3$
Design unit (DR)	$DR={\delta }_1\ast d_1\ast (1+tp+ta+sc)+{\delta }_2\ast d_2\ast \left(\right(tp+ta+sc+ef)-1)+{\delta }_3\ast d_3$ $+{\delta }_4\ast d_4(1+tp+ta+sc)+{\delta }_5\ast d_5\ast (1+ef)+{\sigma }_4$
Suppliers (SR)	$SR={\gamma }_1\ast s_1\ast (1+tp+ta+ef)+{\gamma }_2\ast s_2\ast (1+tp+ta+sc)+{\sigma }_5$
Producer (PR)	$PR={\epsilon }_1\ast P_1\ast \left(1+tp+ta+sc\right)+{\epsilon }_2\ast P_2\ast \left(1+tp+ta+sc\right)+{\epsilon }_3\ast P_3$ ${ +\epsilon }_4\ast P_4\ast \left(1+tp+ts+ef\right)+{\epsilon }_5\ast P_5+{\sigma }_7$
Warehouse management unit (WR)	$WR=w\ast (1+tp+ef)+{\sigma }_6$
Logistics unit (LR)	$LR={\zeta }_1\ast l_1\ast (1+tp+ta+sc)+{\zeta }_2\ast l_2+{\zeta }_3\ast l_3\ast (1+tp+ef)+{\zeta }_4\ast l_4\ast (1$ $+tp+ta+sc)+{\sigma }_8$
On-site construction unit (AR)	$AR={\eta }_1\ast a_1\ast (1+tp+ta+sc)+{\eta }_2\ast a_2\ast (1+tp+sc+ef)+{\eta }_3\ast a_3\ast (1$ $+tp+ta+sc)+{\eta }_4\ast a_4\ast (1+tp+ta+sc)+{\eta }_5\ast a_5\ast ((tp+ta+ef)$ $-1)+{\sigma }_9$

Table 5

Stage resilience model.

Stage	Agent Involved	Resilience Model
Decision-making stage	UR	$DMSR={\omega }_1\ast UR+{\omega }_2\ast CR+{\omega }_3\ast GR$
	CR
	GR
Design stage	DR	$DSR=DR$
Procurement production stage	SR	$PSR={\omega }_4\ast SR+{\omega }_5\ast PR$
	PR
Transportation stage	LR	$TSR={\omega }_{6}WR+{\omega }_7\ast LR$
	WR
Assembly stage	AR	$ASR=AR$

Table 6

Evaluation index weight value.

Stage	Weight	Agent	Weight	Factors	Weight
Decision-making stage	0.037	End user (UR)	0.081	u1	0.209
				u2	0.068
				u3	0.209
				u4	0.111
				u5	0.403
		Government agencies (GR)	0.577	g1	0.154
				g2	0.496
				g3	0.267
				g4	0.083
		General contractor (CR)	0.342	c1	0.062
				c2	0.285
				c3	0.653
Design stage	0.160	Design unit (DR)	1	d1	0.054
				d2	0.358
				d3	0.195
				d4	0.358
				d5	0.345
Procurement production stage	0.097	Suppliers (SR)	0.830	s1	0.34
				s2	0.66
		Producer (PR	0.170	p1	0.031
				p2	0.350
				p3	0.126
				p4	0.229
				p5	0.229
Transportation stage	0.237	Warehouse management unit (WR)	0.25	w	1
		Logistics unit (LR)	0.75	l1	0.207
				l2	0.050
				l3	0.196
				l4	0.547
Assembly stage	0.469	On-site construction unit (AR)	1	a1	0.045
				a2	0.168
				a3	0.093
				a4	0.423
				a5	0.271

Table 7

Initial value of multi-agent resilience influencing factors of prefabricated building supply chain.

Agent	Factors	Initial Value
End user (UR)	u1	0.5
	u2	0.8
	u3	0.3
	u4	0.5
	u5	0.3
Government agencies (GR)	g1	0.5
	g2	0.3
	g3	0.5
	g4	0.6
General contractor (CR)	c1	0.8
	c2	0.2
	c3	0.3
Design unit (DR)	d1	0.7
	d2	0.8
	d3	0.5
	d4	0.3
	d5	0.6
Suppliers (SR)	s1	0.8
	s2	0.3
Producer (PR	p1	0.6
	p2	0.3
	p3	0.5
	p4	0.8
	p5	0.7
Warehouse management unit (WR)	w	0.7
Logistics unit (LR)	l1	0.6
	l2	0.6
	l3	0.7
	l4	0.3
On-site construction unit (AR)	a1	0.7
	a2	0.7
	a3	0.8
	a4	0.3
	a5	0.3

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