Evaluation of Influencing Factors on the Supply Chain of Prefabricated Buildings under Engineering Procurement Construction Model: A Case Study in China

Abstract

With strong support from national and local government policies for prefabricated buildings, China’s prefabricated buildings have entered a period of rapid development. This article analyses the literature from various countries and establishes a structural model of the prefabricated building supply chain under the Engineering Procurement Construction (EPC) mode. It analyzes the factors that affect the prefabricated building supply chain under the EPC model from eight aspects: design stage, prefabricated component production and manufacturing stage, procurement stage, and EPC general contracting stage, etc. Then, it establishes an AHP fuzzy comprehensive evaluation model that combines qualitative and quantitative methods to evaluate the entire lifecycle supply chain of prefabricated buildings, providing reference for the robustness and resilience evaluation of prefabricated building supply chains, and further achieving green management of cost reduction and efficiency improvement in prefabricated building supply chains.

1. Introduction

China first proposed “promoting prefabricated buildings” in the 13th Five-Year Plan and once again clarified the important task of “developing intelligent construction and promoting prefabricated buildings” in the 14th Five-Year Plan. With the strong promotion of national policies, China’s prefabricated buildings have entered a stage of rapid development. At present, prefabricated buildings under the EPC model in China face problems, such as separation of design and construction, poor collaboration among enterprises, and low application of information technology in project management, so it is difficult to control the cost of prefabricated buildings. Faced with the current severe market environment and diverse customer demands, the development of prefabricated buildings under the EPC model has faced new challenges.

Many scholars have conducted research on the management of the supply chain of prefabricated buildings from different perspectives, focusing on the EPC general contracting model and facing the difficult-to-control costs of prefabricated buildings. Based on supply chain theory, An Kaige et al. [1] analyzed the composition and formation mechanism of transaction costs in prefabricated building supply chains under the EPC model and established a transaction cost model for the prefabricated building supply chain. They quantified the transaction costs of prefabricated building supply chain under the EPC model and concluded that transaction costs could be controlled by searching for ways to reduce transaction times and design incentive contracts, thereby reducing project changes and claims, improving management efficiency, and establishing long-term stable supply chain strategic alliances and cooperation relationships on the basis of increasing the benefits of various enterprises. Zhang Beibe [2] proposed a modular green supply chain structure model based on the EPC model and proposed development strategies, such as building a green information technology platform, to address the impact of prefabricated building components on environmental personnel during procurement, production, transportation, assembly, and recycling. From the perspective of the general contractor, Liu Guangchen and Zhu Tianji [3] used a literature review and questionnaire survey to quantitatively analyze the interrelationships between the influencing factors of collaborative management in the prefabricated building supply chain, using a combination of the Best Worst Method (BWM) and the Decision Making Trial and Evaluation Laboratory (DEMATEL). By calculating the weights and degrees of each influencing factor, key influencing factors were identified to improve the efficiency of collaborative management in the prefabricated building supply chain and achieve the maximization of resource allocation in the entire prefabricated building supply chain. Yue Chaolong et al. [4] applied evolutionary game theory to propose five basic assumptions for the two parties involved, the general contractor and the supplier, and then established an evolutionary game model to analyze the factors that affect the unified supply chain management of the general contractor and the supplier under different conditions in the EPC model of prefabricated construction. They obtained the support of the general contractor for the supplier, which is beneficial for the evolutionary game to converge on the ideal state faster, thus achieving a dynamic balance of supply chain management between the general contractor and the supplier and maximizing the benefits between the two. Based on the successful investigation of stakeholder-related risks and interactions in complex green building projects, major public works projects, and housing demolition projects in social network analysis, Lizi Luo et al. [5] used this method to construct a supply chain risk network for a prefabricated building in Hong Kong. They concluded that the main challenges faced by the prefabricated building supply chain are poor resources, schedule planning, workflow control, and information sharing among stakeholders. Gaining a deeper understanding of the risks in the prefabricated building supply chain in Hong Kong can help practitioners to more effectively respond to such risks. Tongguang Si et al. [6] proposed a dynamic compensation mechanism to achieve timely component delivery, based on mutual compensation and incentives between the general contractor and the factory, an optimization model based on a genetic algorithm was established to obtain the optimal delivery planning solution, and this model can help both parties reduce waste of prefabricated delivery when either the general contractor or the factory has the motivation to change the delivery date of prefabricated parts. Zhenmin Yuan et al. [7], based on previous research that did not systematically identify and dynamically analyze construction deviations caused by various factors in the production, transportation, and installation stages of prefabricated buildings, and based on system dynamics theory, unraveled the causal relationships between these factors and verified, through examples, that the installation stage is the most critical stage affecting the construction accuracy of prefabricated components. These findings and suggestions can facilitate the general contractor to allocate relevant resources to more critical factors, thereby improving the construction accuracy of prefabricated components.

Although the above research analyzes the transaction costs, green supply chain, and supply chain collaborative management of prefabricated building supply chains from different perspectives, there is little research on the influencing factors of supply chain lifecycle management during the construction period of prefabricated buildings from the perspective of general contractors. At present, supply chain management has a mature knowledge system, while the theoretical system of prefabricated building supply chain management is not fully mature, especially based on the perspective of EPC general contractors. Therefore, this study combines qualitative and quantitative analysis methods to establish a structural model of the prefabricated building supply chain under the EPC mode. It analyzes the factors that affect the full life cycle management of the prefabricated building supply chain under the EPC model from eight aspects: design stage, prefabricated component manufacturer, procurement stage, EPC general contractor, owner, supply chain and logistics, government, and supervision. By analyzing the AHP fuzzy comprehensive evaluation model, the results are obtained to reduce the cost and risk of the full life cycle management of the prefabricated building supply chain under the EPC mode, improve the robustness, resilience, and efficiency of collaborative management of the supply chain, and achieve green management that reduces costs and increases efficiency.

In summary, the rest of this article is organized as follows: in Section 2, we construct a supply chain structure model for prefabricated buildings based on the EPC model theory; in Section 3, we introduce the theoretical concepts of the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation Method, respectively; on the basis of extensive literature analysis and questionnaire survey, the fourth section involved the screening out the influencing factors of the prefabricated building supply chain under the EPC mode; in Section 5, we first construct a hierarchical model of the influencing factors of the prefabricated building supply chain, calculate the weights of each layer, sort the influencing factors based on the calculation results, and then use the fuzzy comprehensive evaluation method to establish a fuzzy evaluation matrix. The selected influencing factors are evaluated in layers to determine the degree of influence of each influencing factor on the prefabricated building supply chain under EPC mode; in Section 6, we further discuss the research findings of this article.

2. Structural Composition of Prefabricated Building Supply Chain under EPC Mode

When it comes to the Engineering Procurement Construction (EPC) mode, the first thing to explain is its difference and connection with the turnkey mode. The general contracting mode of the EPC is an integrated contracting mode of design, procurement, and construction, which refers to the contracting party being entrusted by the owner to carry out the entire process or several stages of the design, procurement, and construction of the engineering construction project according to the contract agreement, and to be responsible for the quality, safety, cost, and progress of the contracted project, excluding the preparation work in the early stage of project construction and the operation work in the later stage. The turnkey mode refers to the engineering management method in which the contracting party hands over the management and use rights of the entire engineering project to the owner after passing the basic construction, equipment installation, commissioning, and operation of the project. In other words, it not only undertakes the construction and implementation tasks of the engineering project but also provides comprehensive services for the pre-construction work and operation preparation work of the construction project. It can be seen that the turnkey model is an extension of the EPC model in terms of business and responsibility, and it also bears greater risks. At the same time, this study is also applicable to the management of the prefabricated building supply chain under the turnkey model.

Prefabricated building refers to the transfer of a large amount of on-site operations from traditional construction methods to factories, transporting prefabricated components processed and manufactured by factories to construction sites for assembly. It adopts standardized design, factory production, assembly construction, information management, and intelligent application, and is a representative of modern industrial production methods [8].

The building supply chain proposed against the background of traditional architecture mainly targets the needs of the owners and goes through all activities and related organizational structures in the construction process from pre-design work, construction, and completion, to expansion and demolition, forming a construction network.

To clarify the influencing factors of the prefab building supply chain in EPC mode, it is first necessary to clarify the participants in the supply chain and sort out the relationship between the participants; so, on this basis, a supply chain model diagram with the EPC general contractor as the core is established, as shown in Figure 1, which intuitively shows the synergy effect between the participants in the prefabricated building supply chain under the EPC model [9]. The initiator and terminator are both the owners, and the EPC general contractor plays the core role of the entire supply chain, starting from the needs of owners, coordinating with all parties involved, and promoting the operation of the supply chain.

3. Research Methods

3.1. Analytic Hierarchy Method

The analytic hierarchy method is a multi-objective decision analysis method that combines qualitative and quantitative analysis methods and is a layered weighted decision analysis method proposed by American operations researcher Saaty in the early 1970s in the 20th century. The basic idea of the analytic hierarchy method is to hierarchize the problem to be analyzed based on the nature of the problem and the target to be realized; it can be divided into several components and then these can be combined at different levels in order to form a multi-level analytical structure model, and, finally, the advantages and disadvantages of the problem are compared and arranged.

When using the analytic hierarchy to make decisions, it can be roughly divided into four steps [10].

(1) Analyse the relationship between various factors in the system and establish a hierarchical model of the system.

The decision-making objectives, factors considered (decision criteria), and decision-making objects are divided into a target layer, criterion layer, and sub-criterion layer according to their mutual relationship, and a hierarchical diagram is drawn. Among them, the target layer refers to the purpose of the decision-making decision and the problem to be solved, the criterion layer is the influencing factors to be considered to achieve the optimal result, and the subcriterion layer is the factor layer.

(2) Construct a judgment matrix, that is, compare the importance of different factors at the same level with each other on the upper criterion.

When determining the weight of factors at various levels, qualitative results are often not easy to accept, so, Saaty et al. [11,12] proposed the consistent matrix method, that is, different factors at the same level are compared with each other based on the importance of the upper criterion, and, on the basis of expert consultation, the Delph method was used to reduce the difficulty of comparing factors of different nature with each other to improve accuracy. Among them, the number of layers in the hierarchical structure is related to the complexity of the problem and the level of detail that needs to be analyzed. Generally, the 1–9 comparative scale method is used, as shown in

Table 1. Judgment matrix comparison scale a_ij.

Quantified Value	Meaning
1	Indicates that two factors are equally important compared to each other
3	Indicates that one factor is slightly more important than the other compared to two factors
5	Indicates that one factor is significantly more important than the other compared to two factors
7	Indicates that one factor is more strongly important than the other compared to two factors
9	Indicates that one factor is extremely important compared to the other
2, 4, 6, 8	The middle value of the above two adjacent judgments
reciprocal	If factors i and j are compared, aij, then factors j and i are compared, aij = 1/aij

.

(3) Hierarchical single sorting and its consistency test.

The eigenvector corresponding to the largest eigenroot ${\lambda }_{max}$ of the judgment matrix is normalized to W. The elements of W are the ranking weights of the relative importance of a factor at the same level as a factor at the previous level, a process known as hierarchical single ranking. The metrics that define consistency are as follows:

$$CI=\left({\lambda }_{max}-n\right)∕\left(n-1\right)$$

(1)

CI = 0 indicates complete consistency; CI close to 0 indicates satisfactory consistency; the larger the CI, the more serious the inconsistency. In order to measure the size of the CI, the random consistency index RI is introduced, which is related to the order of the judgment matrix, and the specific standard values are as follows in

Table 2. Average randomness indicators [13].

Matrix order	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.52	0.89	1.11	1.25	1.35	1.40	1.45	1.49

:

When testing whether the matrix has satisfactory consistency, it is also necessary to compare CI with RI to obtain the test coefficient CR, which is as follows:

$$CR=CI∕RI$$

(2)

It is generally believed that, when CR < 0.1, the judgment matrix passes the consistency test and we can use its normalized feature vector as a weight vector, otherwise it will not have satisfactory consistency.

(4) Overall ranking of levels and its consistency test.

This step requires calculating the weight of the relative importance of all factors of a certain level to the total goal from high level to low level, which is called hierarchical total ranking [14].

3.2. Fuzzy Comprehensive Evaluation Method

The fuzzy comprehensive evaluation method was proposed by Professor L.A. Zadeh, an American expert in automatic control, in 1965. This method converts qualitative evaluation into quantitative evaluation based on the membership theory of fuzzy mathematics and uses fuzzy mathematics to make an overall evaluation of physical objects or objects constrained by multiple factors. It is suitable for solving the problem of subjective evaluation and complex factors that are difficult to handle with traditional evaluation methods.

When using the fuzzy comprehensive evaluation method for decision-making, the first step is to determine the factor set U = $\left\{u_1,u_2,\dots ,u_n\right\}$ and the evaluation set V = $\left\{v_1,v_2,\dots ,v_m\right\}$. Then, based on the fuzzy subset of the evaluation set V, each factor in the factor set U is evaluated using a fuzzy evaluation matrix R. Finally, a fuzzy evaluation matrix R is established as follows:

$$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& \dots & r_{1m}\\ r_{21}& r_{22}& \dots & r_{2m}\\ ⋮& ⋮& ⋮& ⋮\\ r_{n1}& r_{n2}& \dots & r_{nm}\end{array}\right]$$

Among them, $r_{ij}$ represents the membership degree of $u_i$ with respect to $v_j$, and then the weights obtained from the Analytic Hierarchy Process are applied to W Perform matrix operations with the evaluation matrix R to synthesize W, R. The final evaluation result is B = $WR$ = [$b_1,b_2,\dots ,b_m$].

4. Screening of Influencing Factors of Prefabricated Building Supply Chain under EPC Mode

Based on the structural model of prefabricated building supply chains under the EPC mode, the literature analysis method was used to search for “prefabricated building supply chain under EPC mode” in the China Knowledge Network (CNKI), and 14 related journal papers and dissertations were obtained. (The China Knowledge Network refers to the CNKI source database, foreign language, industry, agriculture, medicine and health, economy, and education database. Among them, the comprehensive databases are the China Journal Full-text Database, China Doctoral Dissertation Database, China Excellent Master’s Dissertation Full-text Database, China Important Newspaper Full-text Database, and China Important Conference Full-text Database). The Web of Science search was “EPC model construction supply chain”, and a total of 10 papers were searched in the core set for the topic. From the results of the search, there are not many comprehensive studies on the impact of the prefabricated building supply chain under the EPC model by domestic and foreign scholars, and most of them are unilateral studies, such as supply chain performance, procurement management, and risk quality.

Based on the fact that the prefabricated building under the EPC model is very different from the traditional building in the design stage, construction stage, and production stage, according to the development status of the prefabricated building at this stage, the influencing factors of the prefabricated building supply chain and the current problems faced by it are taken as the starting point, the relevant literature is collected, and the key influencing factors are extracted from it; the influencing factors of the prefabricated building supply chain under the EPC model are shown in

Table 3. Summary of influencing factors related to the supply chain of prefabricated buildings under the EPC model.

Category	Influencing Factors	Factor Description	References
Design phase	Technological innovation capabilities	Refers to new patented technology, standardization and modular design	[15,16,17,18,19,20]
	Design risk aversion	It refers to ensuring the correctness and accuracy of the design and controlling the design changes	[19,20,21,22,23,24,25]
	Information collaboration capabilities	It refers to the cooperation between it and prefabricated parts manufacturers to make the designed drawings and models meet the production needs of the factory	[19,26]
Manufacturer of prefabricated parts	Vendor management	It refers to the maturity and quantity of supplier management, the standardization and standardization process of the factory, etc	[18,21,22,27,28,29,30,31,32]
	Technical competence in the production of prefabricated parts	It refers to the proportion of advanced equipment and facilities of the factory, the level of Integration of Production, and the quality of components	[15,21,23,29,32]
	The degree of redundancy of components	It refers to the redundant number of factory components, production overcapacity, etc.	[16,17,20,21,27,28,30,33,34,35,36]
Procurement phase	Procurement management capabilities	Refers to the impact of procurement management on the performance of general contractors	[22,23,28,29,35,37]
	The degree of redundancy of inventory materials	Refers to the redundant quantity of machinery and transportation lifting equipment	[18,20,23,27,30,33,34,36,38]
	Inventory cost management capabilities	It means that raw material suppliers, PC component factories and general contractors have their own different management methods, which will lead to high inventory costs	[1,25,37,38,39,40]
	Ability to collaborate	Refers to the closeness, stability, etc. with the owner and other partners	[5,16,18,20,21,30,34,41,42]
EPC General Contractor	Collaborative management capabilities	It means that as the core enterprise, the general contractor’s ability to coordinate the cooperation between all sides	[18,28,31,32,38,43]
	Comprehensive quality of personnel	It refers to the proportion of professional and technical personnel in the project and the construction experience of on-site construction personnel in prefabricated buildings	[15,21,22,25,28,29,32,44]
	Assembly construction technology and capabilities	It refers to the degree of construction specialization, construction quality and process arrangement	[15,21,22,23,27,29,41]
	Financial autonomy	It refers to the profitability of enterprise operation and the number of financing channels	[18,23,31,38]
	Ability to interact with information	It refers to the degree of construction of the information platform of the general contractor, the accuracy and timeliness of information transmission, etc.	[5,18,28,29,34,38,45]
	Risk response capabilities	Refers to the emergency plan, emergency measures, etc.	[13,18,27,30,38,46]
Owner	Strategic decision-making ability of management	Refers to the risk appetite and experience skills of decision makers	[32,47,48]
	Collaborative interaction capabilities	Refers to stability, long-term and level of communication with other partners	[18,34,38,41,42,43]
	Strong government support	Refers to the government’s promotion mechanism, etc.	[20,45,49,50]
Government	Government policies	Refers to relevant government policies and incentive policy documents	[26,51,52,53]
	Relevant standard specifications	It refers to the improvement of government standards and normative systems and related systems	[3,31,49]
Supply Chain and Logistics	Supply chain structure quality	It refers to the complexity, rationality of structural settings, stability, etc. of the supply chain	[28,32,34,38,41,44,47,54]
	Supply chain risk contingency capabilities	It refers to the risk early warning mechanism, risk response speed and rationality of risk sharing	[15,18,27,30,36,38,41,44,47,55]
	Information integration synchronization capabilities	Information integration and communication are key factors in supply chain operations, and real-time visual information is applied to grasp supply chain logistics	[18,27,30,34,38,41,56,57,58]
	Logistics and transportation support capabilities	Refers to the reliability of logistics companies, transportation costs and delivery rates	[5,15,18,21,25,27,29,38,41]
	Degree of transport redundancy	Refers to the ability to transport overcapacity	[16,21,27,30,38,59,60]
Supervision unit	Regulatory capacity	Refers to the experience and number of personnel of prefabricated building supervision	[20,21,29]

.

In order to screen the key factors of the prefabricated building supply chain under the EPC mode, in this paper we design a questionnaire with a 5-point matrix scale to screen the key influencing factors, and a total of 107 valid questionnaires are collected, the personal distribution of the survey subjects is shown in

Table 4. Distribution of individual respondents.

Subjects	Classify	Frequency (pcs)	Percentage (%)
Educational level	Master’s degree or above	19	17.76
	Undergraduate	77	71.96
	College	10	9.35
	Technical secondary school and below	1	0.93
Unit	Construction unit	13	12.15
	Constructor	19	17.76
	Research institutions	54	50.47
	Consulting firms	6	5.61
	Other	15	14.02

. Through the SPSS reliability and validity test, the results showed that Cronbach’s alpha coefficient was 0.980, which was greater than 0.9, indicating that the reliability quality of the study data was very high, and the KMO value was 0.954, which was greater than 0.8, the significance level of the Bartlett sphericity test is 0.000, which is less than 0.01, indicating that the validity of this questionnaire is good and can be further studied. The key influencing factors of the prefabricated building supply chain under the EPC model are finally determined, as shown in

Table 5. Key influencing factors of prefabricated building supply chain under EPC mode.

Category	Key Influencing Factors	Category	Key Factors
	Technological innovation capabilities		Procurement management capabilities
Design phase	Design risk aversion	Procurement phase	The degree of redundancy of inventory materials
	Information collaboration capabilities		Inventory cost management capabilities
	Vendor management		Strong government support
Manufacturer of prefabricated parts	Technical competence in the production of prefabricated parts	government	Government policies
	The degree of redundancy of components		Relevant standard specifications
	Comprehensive quality of personnel		Supply chain structure quality
	Risk response capabilities		Supply chain risk contingency capabilities
EPC General Contractor	Ability to interact with information	Supply Chain and Logistics	Information integration synchronization capabilities
	Collaborative management capabilities		Logistics and transportation support capabilities
	Ability to collaborate	Supervision	Regulatory capacity
	Assembly construction technology and capabilities

.

5. Hierarchical Model Evaluation of Factors Influencing the Supply Chain of Assembled Buildings under EPC Mode

Based on the key factors extracted from the previous text, a hierarchical structure model of key influencing factors is established using the Analytic Hierarchy Process (AHP), as shown in Figure 2.

5.1. AHP for Calculating the Weights of Various Indicators

This section analyzes the mutual importance of different factors at the same level based on the hierarchical model and constructs a judgment matrix for each layer. Using the 1–9 comparative scale method, 10 experts were invited to rate the 23 identified influencing factors. Among them, the construction unit, prefabricated component factory, and design unit each have two experts, research institutions have three experts, and government agencies have one expert. After completing the scoring, add the average values to obtain the judgment matrix, and then calculate the influence weights of each key factor according to Formulas (3)–(5). The construction of the judgment matrix is shown in Table 6, Table 7, Table 8, Table 9, Table 10, Table 11, Table 12 and Table 13.

(1) Set the judgment matrix A–B, see Table 6.

Table 6. A pairwise comparison matrix of influencing factors.

A	B1	B2	B3	B4	B5	B6	B7
B1	1	1/3	1/2	1/3	1/2	1/3	1/2
B2	3	1	2	2	2	1/2	3
B3	2	1/2	1	2	3	1/2	3
B4	3	1/2	1/2	1	3	2	4
B5	2	1/2	1/3	1/3	1	1/2	2
B6	3	2	2	1/2	2	1	3
B7	2	1/3	1/3	1/4	1/2	1/3	1

Table 6. A pairwise comparison matrix of influencing factors.

A	B1	B2	B3	B4	B5	B6	B7
B1	1	1/3	1/2	1/3	1/2	1/3	1/2
B2	3	1	2	2	2	1/2	3
B3	2	1/2	1	2	3	1/2	3
B4	3	1/2	1/2	1	3	2	4
B5	2	1/2	1/3	1/3	1	1/2	2
B6	3	2	2	1/2	2	1	3
B7	2	1/3	1/3	1/4	1/2	1/3	1

$${\overline{\omega }}_i=\sqrt[m]{{{\displaystyle \prod }}_{j=1}^m{a}_{ij}}$$

(3)

$${\omega }_i=\frac{{\overline{\omega }}_i}{{\Sigma }_{j=1}^m{\overline{\omega }}_j}$$

(4)

$${\lambda }_{max}=\frac{1}{n}{{\displaystyle \sum }}_{i=1}^n\frac{{\left(AW\right)}_i}{W_i}$$

(5)

$W$ = [0.06 0.21 0.17 0.19 0.09 0.21 0.06]^T.

${\lambda }_{max}$ = 7.47, $CI=\left({\lambda }_{max}-n\right)∕\left(n-1\right)$ = 0.08, $RI$ = 1.35. $CR=CI∕RI$ = 0.06 < 0.1.

(2) Set the judgment matrix B1–C, see Table 7.

Table 7. Pairwise comparison matrix of influencing factors in B1.

B1	C1	C2	C3
C1	1	1/4	1/4
C2	4	1	3
C3	4	1/3	1

Table 7. Pairwise comparison matrix of influencing factors in B1.

B1	C1	C2	C3
C1	1	1/4	1/4
C2	4	1	3
C3	4	1/3	1

$W$ = [0.10 0.60 0.29]^T.

${\lambda }_{max}$ = 3.08, $CI$ = 0.04, $RI$ = 0.52, $CR=CI∕RI$ = 0.07 < 0.1.

(3) Set the judgment matrix B2–C, see Table 8.

Table 8. Pairwise comparison matrix of influencing factors in B2.

B2	C4	C5	C6
C4	1	3	3
C5	1/3	1	1/2
C6	1/3	2	1

Table 8. Pairwise comparison matrix of influencing factors in B2.

B2	C4	C5	C6
C4	1	3	3
C5	1/3	1	1/2
C6	1/3	2	1

$W$ = [0.59 0.16 0.25]^T.

${\lambda }_{max}$ = 3.07, $CI$ = 0.03, $RI$ = 0.52, $CR=CI∕RI$ = 0.07 < 0.1.

(4) Set the judgment matrix B3–C, see Table 9.

Table 9. Pairwise comparison matrix of B3 influencing factors.

B3	C7	C8	C9
C7	1	1/3	2
C8	3	1	3
C9	1/2	1/3	1

Table 9. Pairwise comparison matrix of B3 influencing factors.

B3	C7	C8	C9
C7	1	1/3	2
C8	3	1	3
C9	1/2	1/3	1

$W$ = [0.25 0.59 0.16]^T.

${\lambda }_{max}$ = 3.07, $CI$ = 0.03, $RI$ = 0.52, $CR=CI∕RI$ = 0.07 < 0.1.

(5) Set the judgment matrix B4–C, see Table 10.

Table 10. Pairwise comparison matrix of B4 influencing factors.

B4	C10	C11	C12
C10	1	1/2	1/2
C11	2	1	1/2
C12	2	2	1

Table 10. Pairwise comparison matrix of B4 influencing factors.

B4	C10	C11	C12
C10	1	1/2	1/2
C11	2	1	1/2
C12	2	2	1

$W$ = [0.2 0.31 0.49]^T.

${\lambda }_{max}$ = 3.07, $CI$ = 0.03, $RI$ = 0.52, $CR=CI∕RI$ = 0.06 < 0.1.

(6) Set the judgment matrix B5–C, see Table 11.

Table 11. Pairwise comparison matrix of influencing factors in B5.

B5	C13	C14	C15	C16	C17	C18
C13	1	2	2	2	2	2
C14	1/2	1	1/2	1/3	1/2	1/2
C15	1/2	2	1	1/2	1/3	1/3
C16	1/2	3	2	1	1/2	1/3
C17	1/2	2	3	2	1	2
C18	1/2	2	3	3	1/2	1

Table 11. Pairwise comparison matrix of influencing factors in B5.

B5	C13	C14	C15	C16	C17	C18
C13	1	2	2	2	2	2
C14	1/2	1	1/2	1/3	1/2	1/2
C15	1/2	2	1	1/2	1/3	1/3
C16	1/2	3	2	1	1/2	1/3
C17	1/2	2	3	2	1	2
C18	1/2	2	3	3	1/2	1

$W$ = [0.27 0.08 0.09 0.13 0.23 0.19]^T.

${\lambda }_{max}$ = 6.38, $CI$ = 0.08, $RI$ = 1.25, $CR=CI∕RI$ = 0.06 < 0.1.

(7) Set the judgment matrix B6–C, see Table 12.

Table 12. Pairwise comparison matrix of B6 influencing factors.

B6	C19	C20	C21	C22
C19	1	2	2	3
C20	1/2	1	1/2	2
C21	1/2	2	1	3
C22	1/3	1/2	1/3	1

Table 12. Pairwise comparison matrix of B6 influencing factors.

B6	C19	C20	C21	C22
C19	1	2	2	3
C20	1/2	1	1/2	2
C21	1/2	2	1	3
C22	1/3	1/2	1/3	1

$W$ = [0.41 0.19 0.29 0.11]^T.

${\lambda }_{max}$ = 4.09, $CI$ = 0.03, $RI$ = 0.89, $CR=CI∕RI$ = 0.03 < 0.1.

The comprehensive evaluation weight table of each key factor is finally synthesized, as shown in Table 13.

Table 13. Hierarchical total sort table.

Level	Weight						Comprehensive Evaluation Results	Hierarchical Total Sorting
	B1 (0.06)	B2 (0.21)	B3 (0.17)	B4 (0.19)	B5 (0.09)	B6 (0.21)
C1	0.10						0.006	22
C2	0.60						0.036	11
C3	0.29						0.0174	17
C4		0.59					0.1239	1
C5		0.16					0.0336	12
C6		0.25					0.0525	7
C7			0.25				0.0425	8
C8			0.59				0.1003	2
C9			0.16				0.0272	13
C10				0.20			0.038	10
C11				0.31			0.0589	6
C12				0.49			0.0931	3
C13					0.27		0.0243	14
C14					0.08		0.0072	21
C15					0.09		0.0081	20
C16					0.13		0.0117	19
C17					0.23		0.0207	16
C18					0.19		0.0171	18
C19						0.41	0.0861	4
C20						0.19	0.0399	9
C21						0.29	0.0609	5
C22						0.11	0.0231	15

Table 13. Hierarchical total sort table.

Level	Weight						Comprehensive Evaluation Results	Hierarchical Total Sorting
	B1 (0.06)	B2 (0.21)	B3 (0.17)	B4 (0.19)	B5 (0.09)	B6 (0.21)
C1	0.10						0.006	22
C2	0.60						0.036	11
C3	0.29						0.0174	17
C4		0.59					0.1239	1
C5		0.16					0.0336	12
C6		0.25					0.0525	7
C7			0.25				0.0425	8
C8			0.59				0.1003	2
C9			0.16				0.0272	13
C10				0.20			0.038	10
C11				0.31			0.0589	6
C12				0.49			0.0931	3
C13					0.27		0.0243	14
C14					0.08		0.0072	21
C15					0.09		0.0081	20
C16					0.13		0.0117	19
C17					0.23		0.0207	16
C18					0.19		0.0171	18
C19						0.41	0.0861	4
C20						0.19	0.0399	9
C21						0.29	0.0609	5
C22						0.11	0.0231	15

$CR={{\displaystyle \sum }}_{i=1}^6{B}_{i}CI$ = 0.06 × 0.04 + 0.21 × 0.03 + 0.17 × 0.03 + 0.19 × 0.03 + 0.09 × 0.08 +0.21 × 0.03 = 0.033 < 0.1

From the above formula, the total level of the influencing factors is obtained, and the total ranking has a good consistency.

5.2. Fuzzy Comprehensive Evaluation

Then, the fuzzy comprehensive evaluation method is applied to objectively and systematically evaluate the supply chain management of prefabricated buildings, overcoming the subjectivity in the Analytic Hierarchy Process and improving the reliability, accuracy, and objectivity of the evaluation results. The specific steps are as follows:

(1) Evaluation factor set. According to the hierarchical structure model of key influencing factors in the supply chain of prefabricated buildings under EPC mode, two sets of evaluation factors are obtained:

First layer: U = [B1, B2, B3, B4, B5, B6, B7]

Second layer: B1 = [C1, C2, C3]

B2 = [C4, C5, C6]

B3 = [C7, C8, C9]

B4 = [C10, C11, C12]

B5 = [C13, C14, C15, C16, C17, C18]

B6 = [C19, C20, C21, C22]

B7 = [C23]

(2) Weight set. According to the weights obtained by the Analytic Hierarchy Process in the previous text, the target allocation weight set can be obtained:

W = [W_B1, W_B2, W_B3, W_B4, W_B5, W_B6, W_B7] = [0.06, 0.21, 0.17, 0.19, 0.09, 0.21]

W_B1 = [0.1, 0.6, 0.29]

W_B2 = [0.59, 0.16, 0.25]

W_B3 = [0.25, 0.59, 0.16]

W_B4 = [0.2, 0.31, 0.49]

W_B5 = [0.27, 0.08, 0.09, 0.13, 0.23, 0.19]

W_B6 = [0.41, 0.19, 0.29, 0.11]

(3) Comment collection: V = [V₁, V₂, V₃, V₄, V₅] = [High, relatively high, medium, relatively low, low].

(4) Determine the degree of membership. According to the hierarchical structure model design index evaluation table mentioned above, 10 experts were invited to evaluate the impact of the above-influencing factors on the entire lifecycle supply chain of prefabricated buildings under EPC mode. Among them, more than 80% of the experts obtained a bachelor’s degree or above, 70% of the experts worked in the field of prefabricated buildings for more than 3 years, and 80% of the experts come from construction units and research institutions. The final statistical results are shown in

Table 14. Statistical table for evaluation of various indicators.

Secondary Indicators	High	Relatively High	Medium	Relatively Low	Low
Technological innovation capabilities (C1)	2	6	2	0	0
Design risk aversion (C2)	3	3	4	0	0
Information collaboration capabilities (C3)	2	5	3	0	0
Procurement management capabilities (C4)	1	6	2	1	0
Degree of redundancy of inventory materials (C5)	2	2	4	2	0
Inventory cost management capabilities (C6)	2	6	2	0	0
Vendor management (C7)	1	6	2	1	0
Technical competence in the production of prefabricated parts (C8)	4	5	1	0	0
Degree of redundancy of components (C9)	1	3	5	1	0
Strong government support (C10)	1	4	5	0	0
Government policies (C11)	2	4	4	0	0
Relevant standard specifications (C12)	4	3	3	0	0
Comprehensive quality of personnel (C13)	1	6	3	0	0
Risk response capabilities (C14)	3	4	3	0	0
Ability to interact with information (C15)	2	6	2	0	0
Collaborative management capabilities (C16)	2	7	1	0	0
Ability to collaborate (C17)	2	6	2	0	0
Assembly construction technology and capabilities (C18)	6	3	1	0	0
Supply chain structure quality (C19)	4	3	1	2	0
Supply chain risk contingency capabilities (C20)	3	4	3	0	0
Information integration synchronization capabilities (C21)	5	4	0	1	0
Logistics and transportation support capabilities (C22)	4	4	2	0	0
Regulatory capacity (C23)	5	4	1	0	0

. From the table, it can be seen that all indicators have varying degrees of impact on the entire lifecycle management of prefabricated building supply chains. At the same time, the membership degrees of each indicator are obtained based on the evaluation statistical table, as shown in

Table 15. General table of membership degrees of various indicators.

Secondary Indicators	High	Relatively High	Medium	Relatively Low	Low
Technological innovation capabilities (C1)	0.2	0.6	0.2	0	0
Design risk aversion (C2)	0.3	0.3	0.4	0	0
Information collaboration capabilities (C3)	0.2	0.5	0.3	0	0
Procurement management capabilities (C4)	0.1	0.6	0.2	0.1	0
Degree of redundancy of inventory materials (C5)	0.2	0.2	0.4	0.2	0
Inventory cost management capabilities (C6)	0.2	0.6	0.2	0	0
Vendor management (C7)	0.1	0.6	0.2	0.1	0
Technical competence in the production of prefabricated parts (C8)	0.4	0.5	0.1	0	0
Degree of redundancy of components (C9)	0.1	0.3	0.5	0.1	0
Strong government support (C10)	0.1	0.4	0.5	0	0
Government policies (C11)	0.2	0.4	0.4	0	0
Relevant standard specifications (C12)	0.4	0.3	0.3	0	0
Comprehensive quality of personnel (C13)	0.1	0.6	0.3	0	0
Risk response capabilities (C14)	0.3	0.4	0.3	0	0
Ability to interact with information (C15)	0.2	0.6	0.2	0	0
Collaborative management capabilities (C16)	0.2	0.7	0.1	0	0
Ability to collaborate (C17)	0.2	0.6	0.2	0	0
Assembly construction technology and capabilities (C18)	0.6	0.3	0.1	0	0
Supply chain structure quality (C19)	0.4	0.3	0.1	0.2	0
Supply chain risk contingency capabilities (C20)	0.3	0.4	0.3	0	0
Information integration synchronization capabilities (C21)	0.5	0.4	0	0.1	0
Logistics and transportation support capabilities (C22)	0.4	0.4	0.2	0	0
Regulatory capacity (C23)	0.5	0.4	0.1	0	0

.

(5) Based on the membership degree of each indicator, the fuzzy evaluation matrix corresponding to each criterion layer indicator can be calculated. The fuzzy evaluation matrices R1, R2, R3, R4, R5, R6, R7 corresponding to indicators B1, B2, B3, B4, B5, B6, R7 are as follows:

$$\begin{gathered} R1=\left[\begin{array}{c}0.2 0.6 0.2 0 0\\ 0.3 0.3 0.4 0 0\\ 0.2 0.5 0.3 0 0\end{array}\right] \\ R2=\left[\begin{array}{c}0.1 0.6 0.2 0.1 0\\ 0.2 0.2 0.4 0.2 0\\ 0.2 0.6 0.2 0 0\end{array}\right] \\ R3=\left[\begin{array}{c}0.1 0.6 0.2 0.1 0\\ 0.4 0.5 0.1 0 0\\ 0.1 0.3 0.5 0.1 0\end{array}\right] \\ R4=\left[\begin{array}{c}0.1 0.4 0.5 0 0\\ 0.2 0.4 0.4 0 0\\ 0.4 0.3 0.3 0 0\end{array}\right] \\ R5=\left[\begin{array}{c}\begin{array}{c}0.1 0.6 0.3 0 0\\ 0.3 0.4 0.3 0 0\\ 0.2 0.6 0.2 0 0\end{array}\\ 0.2 0.7 0.1 0 0\\ 0.2 0.6 0.2 0 0\\ 0.6 0.3 0.1 0 0\end{array}\right] \\ R6=\left[\begin{array}{c}0.4 0.3 0.1 0.2 0\\ 0.3 0.4 0.3 0 0\\ 0.5 0.4 0 0.1 0\\ 0.4 0.4 0.2 0 0\end{array}\right] \\ R7=\left[0.5 0.4 0.1 0 0\right] \end{gathered}$$

Then, using the formula B1 = W_B1R1, calculate the evaluation vectors corresponding to the fuzzy evaluation matrix of each criterion layer indicator. The same applies to B2~B6:

$$\begin{array}{l}\mathrm{B}1=\mathrm{WB}1\mathrm{R}1=\left[0.1, 0.6, 0.29\right]·\left[\begin{array}{c}0.2 0.6 0.2 0 0\\ 0.3 0.3 0.4 0 0\\ 0.2 0.5 0.3 0 0\end{array}\right]\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}=\left[0.2580, 0.3850, 0.3470, 0, 0\right]\\ \mathrm{B}2=\left[0.1410, 0.5360, 0.2320, 0.0910, 0\right]\\ \mathrm{B}3=\left[0.2770, 0.4930, 0.1890, 0.0410, 0\right]\\ \mathrm{B}4=\left[0.2780, 0.3510, 0.3710, 0, 0\right]\\ \mathrm{B}5=\left[0.2550, 0.5340, 0.2010, 0, 0\right]\\ \mathrm{B}6=\left[0.4100, 0.3590, 0.1200, 0.1110, 0\right]\\ \mathrm{B}7=[0, 0, 0, 0, 0]R=\left[\begin{array}{c}\begin{array}{c}0.2580 0.3850 0.3470 0 0\\ 0.1410 0.5360 0.2320 0.0910 0\\ 0.2770 0.4930 0.1890 0.0410 0\end{array}\\ 0.2780 0.3510 0.3710 0 0\\ 0.2550 0.5340 0.2010 0 0\\ 0.4100 0.3590 0.1200 0.1110 0\\ 0 0 0 0 0\end{array}\right]\end{array}$$

The overall evaluation vector is B = [0.2540, 0.4096, 0.2155, 0.0494, 0].

In summary, based on the principle of maximum membership degree, the fuzzy comprehensive evaluation results of each criterion layer are as follows: B1 has a relatively high degree of influence, B2 has a relatively high degree of influence, B3 has a relatively high degree of influence, B4 has a medium degree of influence, B5 has a relatively high degree of influence, B6 has a high degree of influence, and B7 has a low degree of influence. Finally, the overall impact of the fuzzy comprehensive evaluation of the full lifecycle management of prefabricated building supply chain under the EPC model is relatively high.

6. Conclusions

This article establishes an evaluation model for the full lifecycle management of prefabricated building supply chain in the EPC model based on the AHP fuzzy comprehensive evaluation method through literature analysis. By combining qualitative and quantitative methods, multi-level and multi-factor problems are transformed into single-level and single-factor problems for objective and reasonable overall evaluation. From the overall evaluation results, it can be concluded that, in the construction of prefabricated building projects under the EPC mode, it is necessary to strengthen the full lifecycle management of the supply chain to achieve the green management of cost reduction and increased efficiency.

Firstly, the government should introduce and improve relevant green standards and regulations and jointly establish a green code of conduct based on full lifecycle supply chain management with industry associations, enterprises, and other organizations; secondly, in terms of informatization, the establishment of information platforms for supply chain logistics in the construction industry has not yet been popularized, especially for the construction of assembly type building supply chain information platforms under the EPC mode. This leads to the inability of all participating parties in the project to share information in a timely manner, which can easily cause decision-making errors caused by information flow deficiencies and reduce project management efficiency. Therefore, it is necessary to build an information platform based on the full lifecycle management of assembly-type building supply chains under the EPC model so that all participating parties in the project can achieve timely sharing of engineering information; finally, in terms of education, universities should reform the existing education structure according to the latest developments in the field of architecture, establish specialized talent training mechanisms, and connect with the latest developments in the field of architecture.

In addition, regarding the research on green management, it is worth mentioning that Saeed Kamranfar et al. considered the importance of construction projects in developing countries, such as Iran, and addressed the obstacles that sustainability standards pose to building development. They adopted a green development paradigm and developed a new decision-making method using DEMATEL, Delphi technology, and ANP to identify and rank obstacles to building development based on sustainability standards. Based on the research results, taking a case study of Tehran, Iran as an example, they proposed that the identified obstacles and sub-obstacles can be used to formulate and create green building development plans at the strategic level. This study innovated in decision-making methods, and this method can be applied to solve problems in this field in subsequent research [61]. M. Pourvaziri et al. focused on the lack of modern technology and the latest progress in the field of green procurement in Iran’s construction industry. Based on a literature review and questionnaire surveys, they established a structural explanatory model to identify the five most fundamental obstacles to green procurement in Iran’s construction industry. They found that improving the capabilities of suppliers, minimizing product green emissions, and maximizing product compatibility can improve the efficiency of the construction supply chain. This study fills the missing content of the procurement stage in this study, and green procurement should also be given attention in the process of sustainable development [62]. At the same time, green materials should be used as much as possible during the construction process to achieve green recycling and sustainable cleaning [63,64]. With the gradual implementation of sustainable development concepts and government standards, the construction industry should also achieve green sustainability and reduce the impact on the environment and related personnel in procurement, production, transportation, and other aspects.

The main contribution of this article is to construct an AHP model for the influencing factors of the prefabricated building supply chain in the EPC model from the aspects of design, procurement, assembly stage, and supply chain logistics. The fuzzy comprehensive evaluation method is used to evaluate it, and it is found that the supply chain and logistics-related influencing factors have the greatest impact on the prefabricated building supply chain in the EPC mode. Measures to strengthen the management of the prefabricated building supply chain in the EPC model based on the entire lifecycle are proposed.

It should be added that this study only explores the AHP fuzzy comprehensive evaluation model for the management of the entire life cycle of the prefabricated building supply chain in the EPC mode. There is still insufficient research on the operation and performance, trust, and cooperation of the supply chain. Therefore, in future research, it is necessary to increase the amount of research on these two aspects. At the same time, in future research, an early warning mechanism for the supply chain of prefabricated buildings under the EPC model should be established to achieve comprehensive management and control of the entire project lifecycle, providing a more scientific and reasonable evaluation model for the management of the entire lifecycle of prefabricated buildings under the EPC mode.