Advances in Civil Engineering
Volume 2022 (2022), Article ID 4447399, 9 pages
https://doi.org/10.1155/2022/4447399

Research Article

Evaluation of Key Factors of Logistics Risks for Overseas EPC Projects

Jingshi He and Dai Han

Business College, Dongguan Polytechnic, Dongguan 523808, China

Correspondence should be addressed to Jingshi He; hejings@163.com

Received 30 October 2021; Revised 20 January 2022; Accepted 2 March 2022; Published 21 March 2022

Academic Editor: Jose Matos

Copyright © 2022 Jingshi He and Dai Han. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Correctly identifying and evaluating the logistics risks of EPC (engineering procurement construction) projects is conducive to strengthening the risk prevention and control of engineering projects. An evaluation index system for the logistics risk factors of overseas EPC projects was established, and the entropy-DEA (Data Envelopment Analysis) method used to evaluate and sort risk decision-making unit (DMU) with risk likelihood and risk consequences as decision variables. The risk reduction ratio and improvement space of each risk DMU are obtained, and the risk factor distribution matrix is constructed, pointing out that legal risks, operational risks and management risks are the primary risk DMU, which provide reference decisions for the optimal allocation of resources. Finally, it is proposed to carefully select overseas logistics carriers, conduct in-depth research before formulating logistics plans, pay attention to logistics process tracking and supervision, and strengthen logistics packaging and bundling technologies.

1. Introduction

Economic globalization has promoted the vigorous development of the international construction market and created many development opportunities for international construction enterprises (ICEs) [1]. With the implementation of the “The Belt and Road Initiative”, more and more Chinese companies are actively participating in international construction projects. China’s enterprises have participated in the construction of highway, railway, petroleum, communication, water conservancy and other engineering projects in Asia, Africa and the Middle East, which has improved the external business influence of Chinese enterprises [2]. It plays an important part in China’s international construction enterprises’ moving towards internationalization and obtaining profit sources in the international market. Engineering Procurement Construction (EPC) is the mode in which the owner chooses an overall contractor to be responsible for the entire project design, construction, procurement operation among others [3]. The EPC model uses a single contractor to integrate the design and implementation, which is more cost-effective and shorter in construction period than the traditional way of multiple subcontractors working together to complete the project [4]. Therefore, the EPC model has gradually become the most common construction project delivery method for engineering projects in recent years [5, 6].

In engineering practice, some large-scale construction projects have the characteristics of complex technology, difficult management, and high management risk. As the only contract subject, the EPC project contractor needs to bear greater risks [7]. Large-scale construction projects often require joint completion by multiple companies, and the logistics of engineering construction is an important guarantee for companies to participate in international engineering projects. The data shows that for engineering projects, the total cost of transportation, customs clearance, and insurance designed in the logistics links accounts for about 14.6% of the total investment cost [8], and it does not include the logistics cost related to human resources, personnel travel, and internal logistics performed by suppliers or contractors. The successful completion of the construction project largely depends on the materials being able to arrive at the site on time as needed, and the delay in logistics links will cause the entire construction project not to be completed in accordance with the expected time. Construction engineering logistics is an important link in shortening the construction period of international engineering projects and ensuring the quality of engineering projects. Project risk management is critical to determining the future performance of complex projects. Large-scale projects involve many stakeholders during the construction phase, and risks may escalate into catastrophic economic loss events through complex stakeholders’ relationships [9]. Overseas engineering construction projects involve large amounts of large-scale equipment and construction materials. Besides, overseas construction projects have long logistics routes and many transportation links, spanning multiple countries and regions, and there are many uncertain factors. Identification and evaluation of logistics risks of overseas EPC projects is conducive to taking targeted measures to strengthen risk prevention, improve engineering construction safety, and increase corporate profits.

The hazards faced by construction projects are significant, and Salmon (2020) points out that there are two levels of project risk to manage for construction company risk: corporate risk and project implementation risk, including the risk of contractors undertaking lump-sum engineering, procurement, and construction (EPC) projects [10]. Salmon and other existing literature do not specifically analyze the logistics component of EPC risk. Focusing on the analysis of logistics risk for Overseas EPC Projects is an innovative addition to the field of engineering project risk research. Therefore, this study intends to summarize the risk factors of the logistics link of overseas construction projects, take the likelihood and consequences of risk occurrence as decision variables, and use the entropy weight-DEA method to evaluate and sort the risk decision-making unit. The risk reduction ratio and improvement space of each DMU will be studied to bring the most risk reduction with the smallest risk resource input, and realize the optimal allocation of system resources.

2. Literature Review

In terms of the uncertainties of EPC projects, the first solution is to estimate the completion time of the project, optimize the investment of project resources, and conduct risk assessment of the project [11]. Therefore, it is of great significance to identify logistics risks and correctly evaluate the reduction ratio of different logistics risks in order to optimize the resources where the most obvious benefits can be obtained. The research on project risks is helpful in making effective decisions to reduce the degree of construction project risk exposure [12]. In the current wave of globalization, there are a wide range of risks associated with international construction projects. Time, cost management, management experience, quality, environment and safety are essential for determining the risk level of a construction project [13–15]. Research shows that the risks of internal organization [16] and construction management [17] are very likely to occur. Ganbat et al. [18] pointed out that the risks of international construction projects include structural risks, construction risks, health and safety risks, financial risks and environmental risks. Renuka et al. [19] indicated that construction project risks are affected by rules, capital availability, management skills, resource availability, weather, etc. [20] pointed out that the risks of joint ventures in China’s construction industry include financial risks, legal risks, management risks, market risks, policy risks and technical risks. Wang and Yuan [21] pointed out that decision-making results, engineering experience and the completeness of project information are the key risk factors of Chinese construction project contractors.

According to the existing literature, there is a wealth of research on the risks of engineering construction projects, but there is a lack of systematic and in-depth research on the risks of the logistics links of engineering construction projects. Identifying the key factors of the logistics risks of construction projects and taking effective measures will facilitate the smooth completion of construction projects and ensure the quality and safety of construction projects. This research intends to focus on the logistics links of overseas EPC projects, explore the risks of the logistics, and analyze whether the risk likelihood and risk consequences of the risk decision-making unit can be reduced, and by what ratio can be reduced. There is no literature on this aspect currently. Carrying out this research is conducive to the allocation of risk decision-making resources, to put the risk resources in the place where they can achieve the greatest effect.

For construction projects, some methods can be used to reduce risks, such as dynamic analysis of risk level and characteristics based on statistical process [22], risk assessment based on schedule performance index (SPI) [23], and using the building information model (BIM) to mitigate risks [18]. The models for analyzing risk sources and risk factors include BP (back propagation) neural network method [24], analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) method [25], system dynamics [17], fault tree analysis (FTA), event tree analysis (ETA), and Bayesian network (BN) [26]. However, the traditional risk analysis methodology cannot well measure ratio of risk likelihood and consequences that can be reduced for each factor, and this is of great significance for how to invest risk resources in the place where the greatest effect can be achieved. DEA Model is widely used in economic efficiency evaluation [27]. It can also be used to evaluate the security and risks of complex systems, such as using DEA to analyze the outsourcing risks [28], using AHP-DEA to evaluate the risks of bridge structure [29] and the failure risks of sewage pipes [30], and using Fuzzy DEA to evaluate the supply chain risks [31]. In previous studies, the DEA or the DEA combined with AHP sets was applied to evaluate the risks. However, there is great subjectivity and freedom for the evaluation system with multilevel structure. He and Zhu proposed an entropy weight and AHP comprehensive constraint DEA method to analyze risk factors, which complicates the calculation process [32]. Therefore, in this paper, an entropy-DEA method highlighting entropy weight’s objectivity is proposed to evaluate the logistics risks of EPC projects. Taking the risk likelihood and risk consequences as decision variables, the “projection” of DEA maximum & minimum risk surfaces is used to predict the possibility of risk reduction or increase, and rank the risks by the linear programming model of DEA. The entropy-DEA method conducts a comprehensive analysis of the effectiveness and efficiency of the evaluation unit to propose an optimization plan for resource allocation to reduce risk factors.

The research ideas in this article are as follows: firstly, this research summarizes the logistics risks of overseas EPC projects through literature summaries and expert surveys, and establishes a risk factor evaluation index system; and secondly, an entropy-DEA method is proposed for the evaluation of logistics risks of overseas EPC projects, and the distribution matrix of risk factors is obtained. After evaluating and ranking the risk DMU, the risk reduction ratio of each decision-making unit is obtained, which provides a decision-making basis for the risk management of construction projects.

3. Indicator System

The logistics of overseas EPC projects revolves around the material needs of overseas construction projects, and is usually contracted and coordinated by a third-party logistics company. It is a series of services that transport the equipment and materials required by the engineering project from the manufacturer to the production location of the engineering project after packing, warehousing, customs declaration, inspection declaration, taxation and multiple modes of transportation from the manufacturer across multiple countries and regions. The logistics risks of overseas EPC projects include the following six aspects:

(1)Political and legal risk. China’s EPC overseas engineering projects are mainly in Africa and the Middle East. Political violence, regime changes, terrorist attacks and wars in some countries have brought great risks to project logistics. Some government workers are inefficient and corrupt, and the implementation of policies is quite arbitrary. There are big differences in customs clearance, inspection, insurance, and taxation among countries, and there are uncertainties in the time of logistics clearance. Uncertain factors such as government control, foreign exchange policies, and labor restrictions may also cause logistics risks to affect project construction [33].(2)Risk of natural factors. The transportation of engineering equipment and materials may encounter natural disasters such as lightning, storms, tsunamis and earthquakes, or encounter force major accidents such as collisions, ship stranding, sinking, wars, strikes, etc. In the logistics operation of overseas EPC projects, it is necessary to plan routes in advance and purchase commercial insurance to prevent and reduce losses.(3)Market risk. Whether in the international or domestic market, business operations are faced with market risks, such as the credit risk of suppliers and customers, the risk of price changes, the risk of cultural differences between suppliers and customers, etc. Compared with the stable public security in China, the political and public security in some countries in Africa and the Middle East is turbulent, and goods are stolen from time to time. Unpredictable risks of contract performance are also factors that should be considered during project implementation. Price fluctuations, material delivery delays, market demand fluctuations and design changes are considered as important factors that affect project completion.(4)Technical risk. Some equipment involved in overseas engineering construction projects is nonstandard and oversized. For example, a single generator of a Chinese oil company’s Niger project weighs 310 tons. Oversized items have higher requirements for ports and inland transportation, but some countries in Africa and the Middle East have simple local road transportation conditions, underdeveloped inland waterway transportation, and poor loading and unloading operations. The applicability of transportation equipment and tools, whether the packaging and reinforcement meet the requirements, and whether the information system and equipment are complete are all factors that need to be considered in technical operations. From a technical point of view, the transportation plan needs to be scientifically demonstrated in order to reduce the risk of logistic technology.(5)Operational risk. The logistics operations of engineering projects involve the entire process of procurement, construction, and operation. In logistics operations, operation accidents frequently occur due to irregular operations. The untimely delivery of logistics operation information will cause serious obstacles to ship schedules and vehicle scheduling, and even lead to loss of space and affect the progress of the project. Engineering construction projects such as hydropower stations and railways are located in remote areas, the transportation of large equipment is difficult, and the conditions of transportation yards, loading and warehousing operations are poor, all of which have brought great difficulties to overseas EPC logistics operations.(6)Management risk. Cargo handover involves multiple countries and different transportation sections, and it is prone to problems in equipment handover and operation implementation across countries and regions. Unreasonable organizational setup and management decision-making errors will all lead to risks. Logistics operators in backward countries and regions have relatively low business capabilities and literacy, and failure to comply with operating specifications or not being fully trained will bring unpredictable hidden dangers.

According to the above description, the logistics risks of overseas EPC projects are divided into the above six types of risks and used as the first-level risk index. The survey team constructed 40 indicators based on relevant literature data. Then interviews were conducted with overseas project department staff and project logistics companies such as China State Construction Group and China Railway Construction Group, and finally, 22 indicator factors were retained as shown in Table 1.

Table 1: Logistics risks of overseas EPC projects.

First-level index
Risk factors of second-level index

Political and legal risk
y₁: risk of local protectionism and exclusivity

y₂: corruption and inefficiency of government agencies

y₃: risk of arbitrary changes in government policy

y₄: risk of foreign exchange and interest rate policy changes

Risk of natural factors
y₅: natural disasters such as thunderstorms and tsunamis

y₆: accidents such as collision, grounding and sinking

Market risk
y₇: credit risk of suppliers and customers

y₈: risk of price change

y₉: risk of cultural differences between suppliers and customers

y₁₀: unpredictable risks of contract performance

Technical risk
y₁₁: security risks of insufficient packaging and reinforcement

y₁₂: risk of transportation routes and plans not being scientifically proven

y₁₃: risk of inapplicable transportation equipment and tools

y₁₄: risk of noncompliance of information systems and equipment

Operational risk
y₁₅: risk of irregular operations by operators

y₁₆: risk of loading and warehousing operation conditions not meeting the requirements

y₁₇: risk of untimely and inaccurate job information delivery

y₁₈: risk of transportation roads and depots not meeting the carrying requirements

Management risk
y₁₉: risk of incomplete decision-making mechanism of logistics plan

y₂₀: risk of poor operational ability and literacy of operators

y₂₁: risk of imperfect business process and information communication mechanism

y₂₂: unreasonable risks set by the organization

4. Methodology

4.1. DEA Method

DEA method predicts the growth trend of indicators through the “projection” of the DMU on the hyperplane. It predicts the possibility of risk reduction or increase of DMU by calculating the distance between the maximum & minimum risk surface. In the process of risk assessment, the likelihood and consequences of risk indicators of risk DMU are set as variables, and the risk factors are sorted and evaluated by linear programming from the mini-max risk surface.

The value of risk is assumed as . The and represent the likelihood and consequence of risk, respectively, and the vector is a kind of risk state value of DMU . When , , the combination is a possible risk. When the number of reference points is enough, some hyperplanes are constructed by the maximum risk states in the risk state set . We define a binary relation on to be a partial order relation, then the set is a partial order set and denoted as . When is a closed cam curve, and the risk vector is the maximal element of , and there exists a Pareto optimal solution of the planning model [32].

The maximum risk assessment model and minimum risk assessment model are as follows:

In equations (2) and (3), the constraint is an dimension vector. is the residual variable or relaxation variable, and , .

4.2. Entropy Weight to Constrain DEA

DEA method solves the risk assessment through linear programming, but there is no way to evaluate the multilevel evaluation index. Entropy weight method is an objective weighting method based on the degree of dispersion and variation of evaluation data. The method which is combined with the entropy weight method and DEA will modify the freedom and infinity of weights of traditional DEA method.

The calculation principle of entropy weight is as follows:

Firstly, we use represents the evaluation of the indicator by evaluation object , the decision evaluation matrix is standardized by

The ratio of in the index in the evaluation matrix is calculated as (4), and the contribution degree of all indicators of the evaluation matrix to the attribute is calculated as (5).

In the (5), is related to the number of samples . Generally, there is , so there is e. The consistency degree of contribution of each calculation is calculated as , and the entropy weight of each attribute is obtained as follows:

Taken the entropy weight introduced to equations (2) and (3) for DEA calculation.

5. Model Calculation

5.1. Initial Value and Weight of Risk

L and C represent the likelihood and consequences of the risk, and a quantitative score for the original values of L and C is used. Questionnaire surveys and interviews were conducted among managers and front-line employees of logistics operations engaged in overseas EPC projects. Combining the performance of overseas EPC projects in recent years, the likelihood and risk consequences are scored, and the score results are averaged. The initial risk value is shown in Table 2. The investigation team collected 186 score sheets. Respondents are staff from multiple engineering bureaus under China State Construction Group and China Railway Construction Group, as well as logistics outsourcing practitioners for overseas projects. Among them, management personnel from the construction project department accounted for 16.8%; technical personnel accounted for 12.9%; personnel from logistics companies accounted for 46.6%; researchers accounted for 8.6%, and other personnel accounted for 15.1%. Risk likelihood and risk consequences are expressed in 1–5 to varying degrees. The evaluation criteria of risk likelihood are as follows: the value 1 indicates that the risk is extremely unlikely to occur; the value 2 indicates rare likelihood of the risk occurrence; the value 3 indicates that the risk event may occur; the value 4 indicates that the risk event is likely to occur, and the value 5 indicates that the risk event occurs repeatedly. The risk consequence evaluation criteria are as follows: the value 1 means that the risk causes small loss; the value 2 means that the risk causes general loss; the value 3 means that the risk causes personal disability and serious property loss; the value 4 means that the risk causes casualties and major property loss, and the value 5 means that the risk causes catastrophic personal death and property damage.

Table 2: Initial value and weight of risk factors.

Factor
L
C
Entropy weight

y1
2.7
3.2
0.051

y2
3.2
2.8
0.053

y3
2.6
3.5
0.056

y4
2.2
3.3
0.047

y5
1.2
3.6
0.050

y6
1.8
3.0
0.037

y7
2.8
2.5
0.041

y8
3.3
2.2
0.047

y9
3.6
3.5
0.072

y10
2.3
2.4
0.032

y11
2.7
2.8
0.044

y12
2.5
3.2
0.048

y13
1.5
2.9
0.033

y14
2.4
3.5
0.045

y15
2.2
3.3
0.047

y16
2.6
2.4
0.037

y17
2.8
3.4
0.048

y18
1.7
3.7
0.053

y19
2.5
3.3
0.050

y20
1.8
2.2
0.023

y21
2.2
3.1
0.043

y22
2.5
3.2
0.048

5.2. Calculation and Ranking of Risk Decision

According to the initial value of risk factors, the weight of risk factors is calculated through the principle of the entropy weight method, and the results are shown in Table 2. The risk value of first-level index DMU were calculated based on the data in Table 2, and the results are shown in Table 3.

Table 3: Risk value of the first-level index DMU.

DMU
Risk of first-level index
L
C

1
Political and legal risk
0.553
0.661

2
Risk of natural factors
0.127
0.292

3
Market risk
0.524
0.520

4
Technical risk
0.377
0.503

5
Operational risk
0.445
0.567

6
Management risk
0.410
0.559

According to the DEA method model and the data presentation in Table 3, only the calculation formula of the first-level index DMU 1 was listed here as equation (7), and the calculation formulas of other DMU can be entered into the equation in the same way.

Using the linear programming method to solve formula (7), the Pareto optimal solution of maximum risk decision value can be obtained by calculating the risk indicators of the first-level index DMU1～6. Similarly, according to the model formula (2) of the DEA method, the minimum risk decision value was obtained. The maximum and minimum risk decision values and the objective function results are shown in Table 4.

Table 4: Maximum and minimum risk decision values and objective function.

DMU
(D) Maximum risk decision
(D1) Minimum risk decision

S₁
S₂
Target value
S₁
S₂
Target value

1
0.000
0.000
0.000
0.426
0.369
−0.795

2
0.426
0.369
−0.795
0.000
0.000
0.000

3
0.029
0.140
−0.169
0.397
0.228
−0.626

4
0.175
0.158
−0.333
0.251
0.210
−0.461

5
0.108
0.094
−0.202
0.318
0.275
−0.593

6
0.143
0.101
−0.244
0.283
0.267
−0.551

5.3. Risk Surface Distribution

According to the Pareto target value of the logistics risk decision model the risk value of each risk DMU of first-level index is obtained, and the risk values are ranked from largest to smallest as follows: DMU1, DMU3, DMU5, DMU6, DMU4, DMU2. The maximum risk surface distribution of risk DMU is shown in Figure 1. It can be seen that the risk of DMU 1 is on the Pareto maximum surface, that is, the political and legal risk is on the maximum risk surface, and the likelihood and consequences of risk are Pareto maximum. According to the risk decision target value and surface distribution, the risk DMU is followed by market risk, operational risk, management risk, technical risk, and natural factor risk.

Figure 1: Risk surface distribution diagram of risk DMU.

6. Results Analysis and Discussion

6.1. Distribution Matrix of Risk Factors

As for the second-level index factors of overseas EPC projects, taking the risk likelihood as the X axis and the risk consequences as the Y axis, the distribution matrix of risk factors is shown in Figure 2.

Figure 2: Distribution matrix of risk factors.

The factors located in the first quadrant are key risk factors. These risk factors have a high likelihood of accidents and severe consequences. For such risk factors, we should pay special attention to them, and adopt dynamic loop inspection to prevent accidents.

The factors located in the second quadrant are leverage factors. These risk factors have a low likelihood of accidents but severe risk consequences. To leverage risks, we must cyclically check and prevent them before they happen. At the same time, we must formulate emergency risk plans to improve emergency response capabilities.

The factors located in the third quadrant can be called conventional risk factors. These factors have a low likelihood of occurrence and no severe risk consequences. These risk factors should be normally managed through standardized management in various projects, equipment operations and regular inspection to eliminate hidden dangers.

The factors located in the fourth quadrant can be called influential risk factors. These factors have high risk possibilities but not severe risk consequences. These risks should be strengthened through patrol inspections to reduce the likelihood of risk occurrence, and the emphasis should be placed on improving the speed of risk resolution.

6.2. Risk Reduction Ratio of DMU

The calculation results of each risk decision target value calculated according to the model (D₁) are shown in Table 4. It can be seen that the DMU 2, that is, the natural factor risk, is the Pareto minimum. The risk reduction degree of each DMU is shown in Table 5.

Table 5: Risk reduction degree of DMU.

DMU
Reduction ratio of the risk likelihood (%)
Reduction ratio of the risk consequences (%)

1
77.10
55.79

2
0.00
0.00

3
75.84
43.86

4
66.46
41.88

5
71.54
48.46

6
69.13
47.77

From the perspective of the risk reduction degree of each DMU, DMU 1, that is, the political and legal risk, can be reduced the most. The risk likelihood reduction ratio of DMU 1 is 0.426/0.553 = 77.1%, and the risk consequence reduction ratio is 0.369/0.661 = 55.79%. In DMU 3, that is, the market risk, the likelihood and consequences of risk can be reduced by 75.84% and 43.86% respectively. In DMU 5, that is, the operational risk, the likelihood and risk consequences can be reduced by 71.54% and 48.46% respectively. In DMU 6, that is, the management of risk, the risk likelihood and risk consequences can be reduced by 69.13% and 47.77% respectively. It can be seen from the analysis results that the main resource allocation of risk prevention and control should focus on DMU 1, 3 and 5, to bring the most risk reduction with the smallest risk resource input and realize the optimal allocation of system resources.

6.3. Countermeasures and Suggestions

Based on the above analysis, the following suggestions are put forward for the logistics risks of overseas EPC projects.

(1)Choose overseas logistics carriers carefully. The objects of logistics carriers of overseas EPC projects are often over-gauge, special and bulky cargo, which are transported across multiple countries and regions through the combination of highway, railway, and waterway transportation. Among the key factors and leverage factors analyzed in the previous article, there are many factors that are closely related to logistics carriers, including risk of transportation roads and depots not meeting the carrying requirements, risk of inapplicable transportation equipment and tools, risk of poor operational ability and literacy of operators, risk of incomplete decision-making mechanism of logistics plan, risk of loading and warehousing operation conditions not meeting the requirements, risks of transportation routes and plans not being scientifically proven, unreasonable risks set by the organization. The choice of a logistics carrier involves whether the project can be completed smoothly. The selection of logistics carriers should focus on performance and safety rather than price factors. Choosing a safe and reliable logistics carrier is an important measure to strengthen risk prevention and control of the root cause.(2)In-depth research should be conducted before the formulation of the logistics plan. The social and cultural differences, port handling rules, government regulations, and customs clearance policies in the destination, as well as the countries and regions along the route should be fully understood. For the key risk factors and leverage risk factors, such as cultural differences between suppliers and customers, continuity of government policies, local protectionism and exclusivity, foreign exchange and interest rate policies should be investigated and understood deeply in advance to prevent and solve them. For special cargo transportation, the traffic capacity of roads and bridges along the route should be inspected in advance, and the roads and bridges should be widened and strengthened in advance if necessary. Risk factors such as cultural differences in overseas destinations, government policies, exchange rates, and local protectionism should be understood in advance, and plans and corresponding measures should be made in advance according to the risks that may arise.(3)Pay attention to the tracking and supervision of logistics processes. It is necessary to strengthen the supervision of the collection and distribution of equipment and materials in the port, as well as the combined transportation, and urge the suppliers to collect the goods in the port in time according to the shipping schedule. The logistics operations of the oversized and heavy cargo of the construction project are quite different from the ordinary cargo transportation. When a large number of materials arrive at the port for collection and distribution, full coordination and planning should be done to avoid crushing trucks and cargo. Attention should be paid to the inventory and inspection of goods to prevent damage to goods. Different transportation sections and operation links shall implement handover work strictly in accordance with the specifications, and promptly obtain evidence and settle claims when problems are found.(4)Strengthen logistics packaging and strapping technology. The transportation distance of overseas construction projects is long, thus, it is necessary to carefully check whether the binding of the goods is intact, so as to prevent the goods from loosening and falling off during the long-term transportation. Moreover, it is suggested to confirm whether the packaging and support comply with the packaging specifications, prevent the center of gravity of the goods from being unstable, and prevent the placement and angle of the goods from being incorrect.

7. Conclusion

According to the existing literature, there is a wealth of research on the risks of engineering construction projects, but there is a lack of systematic and in-depth research on the risks of the logistics links of engineering construction projects. Identifying the key factors of the logistics risks of construction projects and taking effective measures will facilitate the smooth completion of construction projects and ensure the quality and safety of construction projects. The traditional risk analysis methodology cannot well measure ratio of risk likelihood and consequences that can be reduced for each factor, and this is of great significance for how to invest risk resources in the place where the greatest effect can be achieved. In previous studies, the DEA or the DEA combined with AHP was applied to evaluate the risks. However, there is great subjectivity and freedom for the evaluation system with a multilevel structure. Therefore, in this paper, an entropy-DEA method highlighting entropy weight’s objectivity is proposed to evaluate the logistics risks of EPC projects. Taking the likelihood and consequences of risk as decision variables, the “projection” of DEA maximum & minimum risk surfaces is used to predict the possibility of risk reduction or increase, and rank the risks by the linear programming model of DEA. The entropy-DEA method conducts a comprehensive analysis of the effectiveness and efficiency of the evaluation unit, to propose an optimization plan for resource allocation to reduce risk factors.

The logistics of overseas EPC projects is a practical problem. This study has established an evaluation index system for the logistics risk factors of overseas EPC projects. The risk factors were ranked and evaluated by the entropy weight-DEA method, and the risk factor distribution matrix was constructed. The risk likelihood and risk consequences of first-level index DMU can be reduced and the space for improvement was concluded, providing reference decision-making for risk decision-making and resource allocation.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

This research was funded by the Guangdong Basic and Applied Basic Research Foundation (2019A1515110909) and Guangdong Education Science Planning Project (2021GXJK120).

References

T. Chang, B. G. Hwang, X. Deng, and X. Zhao, “Identifying political risk management strategies in international construction projects,” Advances in Civil Engineering, vol. 2018, Article ID 1016384, 11 pages, 2018.

Z. Hu, “Data-driven analytics for China’s overseas construction projects in the contexts of the maritime silk road and global maritime network,” Complexity, vol. 2020, Article ID 8129172, 21 pages, 2020.

H. Li, L. Lv, X. An, and G. Dong, “Profit sharing mechanism of large EPC project considering the behavior of fairness Concern,” Advances in Civil Engineering, vol. 2020, Article ID 3725254, 9 pages, 2020.

R. A. Perkins, “Sources of changes in design-build contracts for a governmental owner,” Journal of Construction Engineering and Management, vol. 135, no. 7, pp. 588–593, 2009.

F. Y. Yao, Y. Y. Ji, H. X. Li et al., “Evaluation of informatization performance of construction industrialization EPC enterprises in China,” Advances in Civil Engineering, vol. 2020, Article ID 1314586, 18 pages, 2020.

J. Y. Ding, N. Wang, and L. C. Hu, “Framework for designing project delivery and contract strategy in Chinese construction industry based on value-added analysis,” Advances in Civil Engineering, vol. 2018, Article ID 5810357, 14 pages, 2018.

C. Sun, Mi. Wang, and F. Zhai, “Research on the collaborative application of BIM in EPC projects: the perspective of cooperation between owners and general contractors,” Advances in Civil Engineering, vol. 2021, Article ID 4720900, 14 pages, 2021.

E. Subiyanto and Y. T. Suyoto, “Determining value of logistics costs in projects: empirical findings based-on executing several cement projects in Indonesia,” Heliyon, vol. 6, no. 7, Article ID 04352, 2020.

Y. Chen, Z. Hu, Q. Liu, and M. Zhao, “Risk Propagation of delayed payment in stakeholder network of large hydropower project construction considering risk resistance and mitigation,” Mathematical Problems in Engineering, vol. 2018, Article ID 8013207, 14 pages, 2018.

W. A. Salmon, Practical Risk Management for EPC/Design‐Build Projects: Manage Risks Effectively – Stop the Losses, John Wiley & Sons, New Jersey, USA, 2020.

C. Ou-Yang and W. Li. Chen, “A hybrid approach for project crashing optimization strategy with risk consideration: a case study for an EPC project,” Mathematical Problems in Engineering, vol. 2019, Article ID 9649632, 17 pages, 2019.

F. Marle, “An Assistance to project risk management based on complex systems theory and agile project management,” Complexity, vol. 2020, Article ID 3739129, 20 pages, 2020.

C. Chapman, “Project risk analysis and management – PRAM the generic process,” International Journal of Project Management, vol. 15, no. 5, pp. 273–281, 1997.

K. Patel, “A study on risk assessment and its management in India,” American Journal of Civil Engineering, vol. 1, no. 2, pp. 64–67, 2013.

P. X. W. Zou, G. Zhang, and J. Wang, “Understanding the key risks in construction projects in China,” International Journal of Project Management, vol. 25, no. 6, pp. 601–614, 2007.

A. Q. Adeleke, A. Y. Bahaudin, and A. M. Kamaruddeen, “Kamaruddeen organizational internal factors and construction risk management among nigerian construction companies,” Global Business Review, vol. 19, no. 4, pp. 921–938, 2018.

T. N. Mhatre, J. J. Thakkar, and J. Maiti, “Modelling critical risk factors for Indian construction project using interpretive ranking process (IRP) and system dynamics (SD),” International Journal of Quality & Reliability Management, vol. 34, no. 9, pp. 1451–1473, 2017.

T. Ganbat, H. Y. Chong, and P. C. Liao, “Mapping BIM uses for risk mitigation in international construction projects,” Advances in Civil Engineering, vol. 2020, Article ID 5143879, 13 pages, 2020.

S. M. Renuka, C. Umarani, and S. Kamal, “A review on critical risk factors in the life cycle of construction projects,” Journal of Civil Engineering Research, vol. 4, no. 2, pp. 31–36, 2014.

L. Y. Shen, G. W. C Wu, and C. S. K. Ng, “Risk assessment for construction joint ventures in China,” Journal of Construction Engineering and Management, vol. 127, no. 1, pp. 76–81, 2011.

J. Wang and H. Yuan, “Factors affecting contractors’ risk attitudes in construction projects: case study from China,” International Journal of Production Management, vol. 29, no. 2, pp. 209–219, 2011.

Y. Wang, H. Zheng, and X. Lu, “Dynamic risk analysis in metro construction using statistical process control,” Mathematical Problems in Engineering, vol. 2020, Article ID 4053042, 11 pages, 2020.

R. B. Santos and A. E. Jungles, “Risk level assessment in construction projects using the schedule performance index,” Journal of Construction Engineering, vol. 2016, Article ID 5238416, 8 pages, 2016.

C. Yang, Z. Li, X. Guo, W. Yu, J. Jin, and L. Zhu, “Application of BP neural network model in risk evaluation of railway construction,” Complexity, vol. 2019, Article ID 2946158, 12 pages, 2019.

H. Zhao and S. Guo, “Risk evaluation on UHV power transmission construction project based on AHP and FCE method,” Mathematical Problems in Engineering, vol. 2014, Article ID 687568, 14 pages, 2014.

L. Bai, K. Zhang, H. Shi, M. An, and X. Han, “Project portfolio resource risk assessment considering project interdependency by the Fuzzy Bayesian Network,” Complexity, vol. 2020, Article ID 5410978, 21 pages, 2020.

B. Radovanov, B. Dudic, M. Gregus, A. M. Horvat, and V. Karovic, “Using a Two-Stage DEA model to measure tourism potentials of EU countries and Western Balkan countries: an approach to sustainable development,” Sustainability, vol. 12, no. 12, Article ID 4903, 2020.

D. L. Olson and D. Wu, “Risk management models for supply chain: a scenario analysis of outsourcing to China,” Supply Chain Management, vol. 16, no. 6, pp. 401–408, 2011.

Y. Wang, J. Liu, and T. Elhag, “An integrated AHP–DEA methodology for bridge risk assessment,” Computers & Industrial Engineering, vol. 54, no. 3, pp. 513–525, 2008.

S. M. Ghavami, Z. Borzoui, and J. Maleki, “An effective approach for assessing risk of failure in urban sewer pipelines using a combination of GIS and AHP-DEA,” Process Safety and Environmental Protection, vol. 133, no. 1, pp. 275–285, 2020.

M. Pournader, K. Rotaru, A. P. Kach, and H. S. H. Razavi, “An analytical model for system-wide and tier-specific assessment of resilience to supply chain risks,” Supply Chain Management, vol. 21, no. 5, pp. 589–609, 2016.

J. He and J. Zhu, “Evaluation on risk factors of elderly services from the perspective of integrated SCOR model,” International Journal of Information Systems in the Service Sector, vol. 14, no. 1, pp. 11–18, 2022.

X. Deng and S. P. Low, “Exploring critical variables that affect political risk level in international construction projects: case study from Chinese contractors,” Journal of Professional Issues in Engineering Education and Practice, vol. 140, no. 1, Article ID 04013002, 2014.