Next Article in Journal
National REDD+ Strategy for Climate Change Mitigation: A Review and Comparison of Developing Countries
Next Article in Special Issue
The Environmental Impact of Ecological Rehabilitation Techniques Applied to Statically and Variably Stressed Welded Structures
Previous Article in Journal
Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review
Previous Article in Special Issue
Implementing Sustainably Managed Fisheries Using Ecological Risk Assessment and Bowtie Analysis
Open AccessArticle

Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

POIST Task-force Team, POSCO (Pohang Iron and Steel Company), 6261 Donghaean-ro, Nam-gu, Pohang 37666, Korea
Graduate Institute of Ferrous Technology & Graduate School of Engineering Mastership, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang 37673, Korea
Construction Engineering and Management, Department of Civil, Environmental and Architectural Engineering, University of Colorado, Boulder, CO 80309, USA
Author to whom correspondence should be addressed.
Sustainability 2018, 10(12), 4780;
Received: 20 November 2018 / Revised: 11 December 2018 / Accepted: 12 December 2018 / Published: 14 December 2018
(This article belongs to the Collection Risk Assessment and Management)


This paper presents an analytic hierarchy process (AHP)-fuzzy inference system (FIS) model to aid decision-makers in the risk assessment and mitigation of overseas steel-plant projects. Through a thorough literature review, the authors identified 57 risks associated with international steel construction, operation, and transference of new technologies. Pairwise comparisons of all 57 risks by 14 subject-matter experts resulted in a relative weighting. Furthermore, to mitigate human subjectivity, vagueness, and uncertainty, a fuzzy analysis based on the findings of two case studies was performed. From these combined analyses, weighted individual risk soring resulted in the following top five most impactful international steel project risks: procurement of raw materials; design errors and omissions; conditions of raw materials; technology spill prevention plan; investment cost and poor plant availability and performance. Risk mitigation measures are also presented, and risk scores are re-assessed through the AHP-FIS analysis model depicting an overall project risk score reduction. The model presented is a useful tool for industry performing steel project risk assessments. It also provides decision-makers with a better understanding of the criticality of risks that are likely to occur on international steel projects.
Keywords: Natural resource development; Risk assessment and mitigation; Analytic Hierarchy Process (AHP); Fuzzy Inference System (FIS); steel plant; Investment Sustainability; Engineer Procure and Construct (EPC) Natural resource development; Risk assessment and mitigation; Analytic Hierarchy Process (AHP); Fuzzy Inference System (FIS); steel plant; Investment Sustainability; Engineer Procure and Construct (EPC)

1. Introduction

Given the moderate recovery in the global economy and steel demand, and the adjustment of supply through the retirement of aging facilities and mergers and reorganizations, global demand for new investments in steel is expected to increase. Thus, the market is ripe for overseas steel-plant investments. However, said investments come with significant risks due to increasing environmental restrictions worldwide, new steel production and processes, and the inherent unknowns of entering an international market (versus domestic) [1]. This study seeks to aid future international steel production investments to identify potential risks and their priority through an exhaustive literature review, survey of subject-matter experts, two case studies, and an analytic hierarchy process (AHP) using the fuzzy inference system (FIS).
The steel industry produces many environmental pollutants along with high energy consumption requirements. Globally, countries are implementing stricter environmental policies which current steel processes will likely not be able to meet in the near future [2]. In addition to these environmental and energy consumption problems, new steel technologies are being developed to improve existing steel processes [3,4,5,6,7]. The inherent challenges of executing projects internationally, compounded by both the execution and exportation new technologies, equates to executing steel-plant construction overseas an activity riddled with uncertainties and risk [8]. To increase the project success rate and to minimize trial and error in using new technologies in overseas steel production investments, this study develops a model to analyze and evaluate the relevant risks.

1.1. Existing Literature

Although most literature dedicated to the steel manufacturing process has been on improving the efficiencies of the production process (e.g., [3,4,5,6,7]), as early as 1985, investigations were taking place on the effect uncertainty has on the steel industry and associated investments [9]. Min [1], Price et al. [10], and Bucur et al. [11] have all investigated uncertainties in steel manufacturing from a global perspective. They presented optimized steelmaking processes and technologies to overcome the loss of profitability caused by an oversupply of steel mills [1,10] and identified a correlation between global economic growth, car production, and steel manufacturing [11]. Other studies have identified uncertainties in steelmaking at the plant level. Zhang [12] presented a model of China’s iron and steel industry risk factors based on resource ecological economics and eco-industry theory. De Magalhães Ozorio et al. [13] included uncertainty in assessing steel manufacturing plant processes and layouts and the profitability of the associated required investments. Kaushal [14] discussed the risk experiences on a failed Korean-led steel plant meant for Orissa, India. To mitigate the impacts that these risks have on cash-flow fluctuations, Kim et al. [15] developed a two-color rainbow options valuation to optimize the investment timing on a hypothetical steel plant. Mali and Dube [8] and Lee [16] have performed risk analysis specifically pertaining to the topic of this paper and are two publications this paper most significantly builds from. Mali and Dube [8] presented a risk register for steel-plant construction, ranking the risks based on their probability, impact, and detectability scores. The register and rankings were based on the case study findings of the construction and operation of a steel plant in India [8]. Lee [16] investigated the project definition rating index (PDRI) theory, developed by Gibson and Dumont [17] for industrial projects, identifying the most impactful early planning activities for overseas construction.
From a more general prospective, there has been a significant amount of literature on the risks associated with overseas construction and technology transfer. Many of these have been performed through an assessment of surveys, interviews, and/or case studies. Shen et al. [18] performed risk analysis on international joint venture investments, ranking the risks based on averages obtained through surveys of subject-matter experts. El-Sayegh [19] identified and assessed risks experienced in the United Arab Emirates construction industry through a questionnaire distributed to construction experts. Transitioning to technology transfer, Mansfield [20] discussed costs and potential problems related to technology transfer. Future studies built on this, presenting the risks of international licensing and investment [21]; risks of entry into foreign markets based on product exports, licensing, joint ventures, and subsidiaries [22]; and risks specifically experienced by the company providing the technology to the overseas entity [23].
Modelling tools have also been used to assess overseas construction and technology transfer risks. One of the more frequently used modelling tools has been fuzzy-logic-based methods due to their appropriateness to address uncertainty and subjectivity in decision-making processes [24]. FIS and/or fuzzy-AHP analysis have been used to rank water quality indicators [24], aid in environmental management decision-making [25,26], assess the quality and sustainability of supply chains [27,28,29,30], evaluate manufacturing processes [31], manage investment portfolios [32], provide the appropriate healthcare services for senior citizens [33], optimize the liquefied natural gas importation in Korea [34], optimize robot path selections of mobile robots [35], optimize joint distribution alliance partnerships [36], assess emerging three-dimensional integrated circuit technologies [37], assess potassium saturation of calcareous soils [38], evaluate the land suitability for a multitude of purposes [39], evaluate barriers of corporate social responsibility [40], and aid a multitude of other decision-making processes. Concerning the risks of technology transfer, fuzzy analysis was used to aid technology-based decisions for information technology organizations competing in global markets [41] and in transferring biotechnology [42].
Fuzzy analysis was specifically found to be a viable technology for modelling, assessing, and managing global risk factors affecting construction performance [43]. To that end, the fuzzy and/or AHP method has been used to assess construction projects based on sustainable development criteria [44], improve the efficiency of contractor bidding decisions [45,46], assess e-procurement outsourcing risks [47,48], evaluate the risk of bridge structure failure [49], and aid owners in selecting the best contractor [50,51]. They have been used for general risk assessment of overseas construction projects [52] and for more specific project types such as the build-operate-transfer project delivery model [53]. Fuzzy analysis has been used to in contract management, ranking which risks the owner and contractor could most effectively manage [54]. Tah and Carr [55], Carr and Tah [56], and Abdelgawad and Fayek [57] all used fuzzy analysis to assess the most common risks, their relative impact and probability of occurrence, and correlation to project performance on different types of construction projects. Karimi Azari et al. used fuzzy analysis to develop a tool to aid contractors and owners in selecting the most appropriate risk assessment model for their given project [58]. More closely related to steel manufacturing, fuzzy analysis has been used to identify and rank risks for power plant construction [59] and in choosing the optimal technologies to be used for a manufacturing plant [60,61].

1.2. Point of Departure and Research Motivation

While there exists a significant amount of research dedicated to the assessment of different types of constructing projects internationally, there has been very little research specifically related to international steel projects. The authors only found one publication that discusses risks associated with steel project construction and operation, focused on domestic steel-plant production and operation within India [8]. This publication identified and ranked 11 development risks, 12 pre-construction risks, 71 construction risks, 14 operational risks, 15 transfer of termination risks associated with one Indian steel plant [8]. While an impactful publication, the data collection was isolated to a singular project limiting its applicability. This paper contributes to the existing body of knowledge by building from Mali and Dube’s [8] findings, increasing the applicability through a more rigorous research methodology (AHP and FIS) and robust data collection (14 international subject-matter experts). Because there are few projects within this area, fuzzy analysis is one of the more effective methods of translating human vagueness into quantifiable risk impacts [43].
The overall motivation of this study is to aid international steel production sponsors and managers in their early project planning risk assessments. The findings of this study will provide these early decision-makers a general list of the most impactful risks expected to be experienced on international steel production projects. Furthermore, the research methodology and examples provide a process for risk assessment to be potentially replicated within the steel industry.

2. Research Methodology and Data Collection

To identify and rank overseas steel investment risks, the authors followed the internationally recognized Project Management Body of Knowledge (PMBOK) project risk management process. This includes the following four steps: identify risks, qualitative risk analysis, quantitative risk analysis, and plan risk response [62]. The research methodology, as it fits within these four steps, is illustrated in Figure 1, and presented in greater detail within the following pages.

2.1. Risk Identification

To identify all potential risks in planning, constructing, and operating an overseas steel-plant investment, the authors reviewed existing literature focusing on technology transfer, construction, and international projects. Risks associated with the transference of technology came from Park’s [23] proposed checklist evaluating overseas technology transference through licensing, from the perspective of the technology provider. General construction risks were pulled from Lee’s [16] identification of 30 external and 36 internal risk factors associated with international construction. General industrial risks came from Gibson and Dumont’s [16] PDRI which lists the 70 most impactful construction, operation, and maintenance planning elements. Finally, Osland et al.’s [22] identified risk factors associated with entering or expanding into an international market were also compiled. From these publications, 164 risks were identified. The authors chose 57 risks applicable to steel-plant technology and overseas construction and operation, defined in greater detail below.

2.2. Qualitative Risk Analysis

The authors developed a risk breakdown structure (RBS) hierarchy, reducing the 164 risks identified through the literature review to 57 risks applicable to overseas steel-plant project execution. This reduction was made based on previous experience of the authors, performed by a Korean Pohang Iron and Steel Company (POSCO) Senior Manager with 17 years of steel-plant experience, with guidance via informal interviews of several POSCO employees. The resultant RBS hierarchy went from Level 1 to Level 3. Level 1 are broad risk definitions, broken into four categories: the project’s external environment (R1), project feasibility and plan (R2), contract (R3), and EPC (R4). Level 2 consists of more defined areas of risk and Level 3 are the actual risks identified for assessment. The RBS can be seen below in Table 1, Table 2, Table 3 and Table 4.

2.3. Quantitative Risk Analysis

The relative importance of the above risk factors was identified through questionnaires answered by subject-matter experts. Questionnaires are conducted through pairwise comparisons between risk factors in the group for each level. Figure 2 is an example of a portion of the questionnaire, representing the R13 risk factor group at Level 3. Fourteen (14) industry experts were chosen with the following qualifications: expert with new steelmaking processes such as Financial Instruments Exchange and/or Compact Endless Cast [1] and a minimum of 10 years of experience in project management for steel, construction, and/or heavy industry. This equated to the questionnaire being answered by nine steel and five general overseas investment subject-matter experts.
From the data collected from the questionnaires, the authors performed an AHP analysis. Figure 1 shows the five steps involved in an AHP analysis: develop a hierarchy, perform a pairwise comparison, derive the matrix, calculate risk importance for each element, and verification of consistency. The hierarchy developed is represented by Table 1, Table 2, Table 3 and Table 4 risks. Subject-matter experts (nine steel and five general overseas investment described in greater detail above) performed a pairwise comparison by comparing and scoring risk factors. An example survey sent for Risk R13 is shown in Figure 2. As can be seen, the authors used the 1–9 scale [63] to have the subject-matter experts compare differing risks. 1 represents that the risk factors being compared are of equal importance and 9 represents one of the risk factors being extremely more important than the other. This is performed “n” times until all alternatives are compared and, from these values, a pair comparison matrix is constructed. The survey results are aggregated via the geometric mean method to creating a single vector which represents the combined responses [64]. Assuming the expert filled out the example Figure 2 questionnaire with all 9s, the matrix would appear as follows [65]:
A = | 1 a 1 n 1 a i j a j i 1 a n 1 1 | = | 1 9 9 9 1 9 1 9 9 1 9 1 9 1 9 1 9 1 9 1 9 1 |
where A is the pairwise comparison matrix and aji is the comparison between i and j and a j i = 1 a i j .
To interpret and give relative weights to each risk (calculate importance of each element), it is necessary to normalize the comparison matrix (matrix derivation). This is performed with three equations (shown below using the example from Figure 2) [65]:
Sum the elements of each column:
A = |   1     9     9     9     1 9     1     9     9     1 9     1 9     1     9     1 9     1 9     1 9     1   | P = 1.33 10.22 19.11 28
Divide each value by its column sum:
A = | 1 1.33 9 10.22 9 19.11 9 28 0.11 1.33 1 10.22 9 19.11 9 28 0.11 1.33 0.11 10.22 1 19.11 9 28 0.11 1.33 0.11 10.22 0.11 19.11 9 28 |
Mean of Each Row:
A = | 1 1.33 9 10.22 9 19.11 9 28 0.11 1.33 1 10.22 9 19.11 9 28 0.11 1.33 0.11 10.22 1 19.11 9 28 0.11 1.33 0.11 10.22 0.11 19.11 9 28 | = λ = | μ 1 = 0.605 μ 2 = 0.243 μ 3 = 0.117 μ 4 = 0.034 |
where A is the pairwise comparison matrix, P is the priorities vector, λ is the eigenvector, and μn is the average for row “n” and weight of the factor (risk importance).
As the number of elements increases, the number of pairwise comparisons increases, which can result in poor concentration and error in judgment or inconsistent matrices [66]. As the weights of the factors (risk importance) only makes sense if derived from consistent matrices, a consistency check must be applied [65]. The consistency ratio (CR) is an indicator of the degree of error or contradiction of decision-makers, calculated through the following equations:
Consistency   Index   ( CI ) = λ m a x n n 1
where, λ m a x = λ P and is the max eigenvalue of matrix, and n is the number of evaluated criteria.
CR = C I R I
where, RI is the random consistency index and is a fixed value (values pulled from [67]).
If the value of CR is less than 10%, then the pairwise comparison matrix has acceptable consistency. There are two types of consistency. One is ordinal consistency and the other is cardinal consistency. Ordinal consistency (transitivity) means that when there are A, B, and C comparisons, if A is more important than B and B is more important than C, then A must be more important than C. Cardinal consistency means that if A is p times more important than B and B is q times more important than C, A should be p*q times more important than C. If a decision maker satisfies the cardinal consistency, the ordinal consistency is also satisfied, but satisfying the ordinal consistency does not guarantee that the cardinal consistency is satisfied [68].
The survey resultant data was assessed by Matrix Laboratory (MATLAB, developed by MathWorks U.S.) for consistency verification and weighting of the responses of the questionnaire. Consistency tests showed that CR in some responses exceeded 10%. Saaty [63] states, in general, human beings cannot accurately maintain cardinal consistency in AHP because they cannot make accurate measurements of intangibles. It is difficult to judge human thoughts, feelings, and preferences when people try to maintain cardinal consistency [63]. Therefore, the responses with CR of 10% or more were classified into two types. If the response does not satisfy the ordinal consistency, a new judgment is required for the part that does not satisfy the transitive feature of the respondent. If the CR of the response exceeds 10% and does not satisfy the cardinal consistency, the original value is used to reflect the vagueness or uncertainty of the respondent’s subjective judgment.
Next, the authors used the data from two case study projects, descriptions shown in Table 5 to perform a FIS analysis.
From the two case studies, each of the 57 were given a linguistic value to their degree of influence and likelihood of occurrence rated as one of five intensities: very low, low, medium, high, or very high. The resultant data falls on a risk probability-impact matrix or heat map seen below in Table 6.
The linguistic variable impact was then used as an input to the MATLAB FIS tool [69] for evaluating all the individual risk scores for the case study projects. The MATLAB tool uses the Mamdani FIS method. The basic standard operations were used for AND and OR operations. The fuzzification interface was set to min, and aggregation on output was set to max. For defuzzification, the centroid method was used so that the risk could be evaluated at the most appropriate level. In this study, because MATLAB was used in the overall process of FIS, Gaussian type membership functions that best described actual phenomena were used as input and output membership functions, shown below:
f ( x ; σ , c ) = e ( x c ) 2 2 σ 2
where, f(x; σ, c) is the membership function, plotted in Figure 3 below; x is the impact value given to the risk based on Table 6 and the MATLAB FIS tool [69], c is the center value as shown in Table 7 (linguistic variable derived from Table 6), and σ is a constant value of 10.5 per the Gaussian membership function (MF).
From the risk impact (Table 6) and probability (Equation (7)), the MATLAB FIS tool [68] assigned each of the 57 Level 3 risk factors a valuation, or individual risk score, on a scale of 0 to 100 points. As seen in Figure 4, the AHP weighted values and FIS individual risk scores are multiplied to achieve a final AHP-FIS weighted individual risk score. These individual risk scores are then summed to equate to a final project risk score which can be used to understand the overall “riskiness” of the project on a scale of 0 to 100.

2.4. Plan Risk Responses

The output of the qualitative risk analysis step, above, is a ranking of all the risks per their weighted individual risk score. From the case study, risk mitigation measures are applied to the top five risks, reducing their impact (influence and/or likelihood of occurrence) thus reducing their individual risk score. As such, the authors then calculated a revised project risk score to understand the impact the mitigation measures had on the overall “riskiness” of the project.

3. Findings and Discussion

3.1. Risk Analysis Results

Table 8, below, shows the weights and rankings of the Level 1 and 2 risk factors from the AHP analysis. As can be seen, project feasibility and planning and the economics or profitability risks are the highest ranked.
Table 9 shows the resultant 12 most important items among the 57 risk factors of Level 3 from the AHP analysis. As can be seen the most impactful risks are procurement issues, design errors and omissions, poor plant performance, technology issues, contract issues, and revenue. In comparison, Mali and Dube [8] found Table 9 risks to rank as follows: non-availability of material ranked 14 of 120, change in design as 50 of 120, operating efficiency as 8 of 120, no discussion of technology transfer, contract disputes as 15 of 120, and no specific discussion of revenue but market price was 3 of 120. Mali and Dube’s risk register is based on opinions of three site-team members versus previous literature. Unfortunately, this has led to the inability to affectively compare this paper’s risk findings and theirs.
Table 10 depicts the top 12 ranked risks after the AHP-FIS analysis. As can be seen, the top 12 rankings are very similar to those found via the AHP analysis alone. However, some differences do exist. In project A, the risk of the possibility of using iron ore and coal from China for new steel technology emerged. Furthermore, the concerns for risk of technology leakage owing to imitation in China were high, and the competitiveness for investment cost by the Korean steel makers was low due to comparison with the relatively low investment cost of blast furnaces in China. The reliability and procurement plans of Chinese-made facilities to reduce investment costs were higher than those in the initial importance ranking. In the case of project B, the risk of performance of the plant using new technology with natural gas emerged. In addition, the risk of the financing plan was high because of the political instability in Iran. Similar to project A, items such as coal, ore and raw materials procurement plans and conditions, investment costs, and technical security were the top priorities.

3.2. Proposed Risk Mitigation Measures

Table 11 shows the risk mitigation measures proposed for the top five risk factors as developed through the two case studies. As can be seen, most of the mitigation measures are better education and/or more a thorough early project planning.
Upon applying these risk mitigation responses, a follow-up AHP-FIS analysis was performed. With risk mitigations applied, it would be expected that the risk scores for the top five risks (and therefore for the projects as a whole) should lower. The expected decrease did occur and, as a result of applying the responses, the risk score decreased from 72.9702 to 66.9258 in the case of project A and from 70.0003 to 64.4484 in the case of project B. The order and items of the top five risk factors also changed, as shown in Table 12 and Table 13. Along with planning a risk response, this represents the PMBOK risk assessment steps of implementing risk responses and monitoring results [62].
After the risk response, in the second risk assessment, the risk score decreased by ~8.3% for project A and ~7.9% for project B. The order and items of the top five risk factors also changed. However, risk factors with high importance remained high even after reassessment. Therefore, risks with high priority should be managed consistently.

4. Discussion: Industry Implications

When a sponsor chooses to execute and finance the construction and operation of an international steel plant, it can play a variety of roles such as a licensor, material provider, operation and maintenance agency, and/or a contractor [70]. The diversity of necessary expertise and general lack of experience in international work exposes managerial teams to unknown risks with unknown magnitudes. By identifying, quantifying, and prioritizing international steel production risks through surveys and case studies of international steel production projects, this paper provides decision-makers a baseline for which to develop project-specific risk management plans. The identified risks will aid project investors in funding the project and managing the contingencies and economic fluctuations of the project. The identified risks will also aid project managers in developing and executing a risk mitigation plan, potentially increasing both the cost and schedule efficiencies of the project [62].

5. Conclusions

Presented in this paper is an AHP-FIS risk assessment model which identifies, quantitatively evaluates, and prioritized risks likely to be experienced on international steel projects. From these combined analyses, weighted individual risk soring resulted in the following top five most impactful international steel project risks: procurement of raw materials, design errors and omissions, conditions of raw materials, technology spill prevention plan, investment cost and poor plant availability and performance. While this knowledge alone is beneficial in the early planning stages of an international steel project, the process presented allows decision-makers to accurately identify risks for any given project type even when data is subjective, vague, and/or uncertain. It also includes a risk mitigation, implementation, and impact assessment cycle which will allow decision-makers to test out the effectiveness of risk mitigation strategies.

5.1. Limitations

Only negative risks are considered in this study. Opportunities, or positive risks, are not considered. This is a limitation as positive risk factors may lower the overall project final risk score and removing it from consideration reduces the efficacy of comparing project Final Risk Scores. Also, the correlations among risks are not taken into consideration. For some projects, when one risk occurs the likelihood of another risk occurring may increase or decrease. Thus, ignoring correlation reduces the accuracy of the presented model. However, this would only impact the plan risk response revised Individual and Total Risk Score portions of the process. Finally, though the process is flexible, the proposed model is not applicable to all cases of overseas new steel technology transfer. The resources and the expected profit for each case are different.

5.2. Future Research

A model should be studied in which optimal cases can be selected by considering both the risks and opportunities of a single project when performing multiple projects with limited company resources. Finally, further data, specifically on the risks associated with new steel technology transfer, are required to increase the model accuracy.

Author Contributions

M.S.K. developed the concept based on the analysis and drafted the manuscript. I.H.J. supported the analysis. D.S.A. provided academic feedback and revised the manuscript. E.B.L. supervised the overall work and revised the manuscript. All the authors read and approved the final manuscript.


The authors acknowledge that this research was sponsored by the Ministry of Trade Industry and Energy Korea through the Technology Innovation Program funding (Developing Intelligent Project Management Information Systems for Engineering Projects; Grant number = 10077606).


The authors appreciate POSCO in Korea for their support on this study. The authors would like to thank Dr. Y.G. Kim in HYUNDAI Steel-works for his support with the manuscript revisions.

Conflicts of Interest

The authors declare no conflicts of interest.


AHPAnalytic Hierarchy Process
FISFuzzy inference system
EPCEngineering Procure and Construct
PDRIProject Definition Rating Index
PMBOKProject Management Body of Knowledge
O&MOperation and Maintenance
POSCOPohang Iron and Steel Company
RBSRisk Breakdown Structure
MATLABMatrix Laboratory


  1. Min, D.J. Global Competitiveness Through Hybridization of FINEX and CEM Processes. Asian Steel Watch 2016, 2, 50–63. [Google Scholar]
  2. Gray, W.B.; Deily, M.E. Compliance and enforcement: Air pollution regulation in the US steel industry. J. Environ. Econ. Manag. 1996, 31, 96–111. [Google Scholar] [CrossRef]
  3. Gajdzik, B.; Gawlik, R. Choosing the Production Function Model for an Optimal Measurement of the Restructuring Efficiency of the Polish Metallurgical Sector in Years 2000–2015. Metals 2017, 8, 23. [Google Scholar] [CrossRef]
  4. Sun, J.; Peng, W.; Ding, J.; Li, X.; Zhang, D. Key Intelligent Technology of Steel Strip Production through Process. Metals 2018, 8, 597. [Google Scholar] [CrossRef]
  5. Manocha, S.; Ponchon, F. Management of Lime in Steel. Metals 2018, 8, 686. [Google Scholar] [CrossRef]
  6. Andersson, A.; Gullberg, A.; Kullerstedt, A.; Sandberg, E.; Andersson, M.; Ahmed, H.; Sundqvist-Ökvist, L.; Björkman, B. A Holistic and Experimentally-Based View on Recycling of Off-Gas Dust within the Integrated Steel Plant. Metals 2018, 8, 760. [Google Scholar] [CrossRef]
  7. Baricová, D.; Pribulová, A.; Futáš, P.; Buľko, B.; Demeter, P. Change of the Chemical and Mineralogical Composition of the Slag during Oxygen Blowing in the Oxygen Converter Process. Metals 2018, 8, 844. [Google Scholar] [CrossRef]
  8. Mali, N.H.; Dube, S.K. Risk Management in Steel Plants. Int. J. Eng. Tech. Res. 2018, 8, 100–105. [Google Scholar]
  9. Farkas, D. The Effect of Demand Uncertainity on Planning: The Steel Industry in Argentina. Ph.D. Dissertation, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, 1985. [Google Scholar]
  10. Price, A.H.; Weld, C.B.; El-Sabaawi, L.; Teslik, A.M. Unsustainable: Government Intervention and Overcapacity in the Global Steel Industry. Available online: (accessed on 11 December 2018).
  11. Bucur, A.; Dobrotă, G.; Oprean-Stan, C.; Tănăsescu, C. Economic and qualitative determinants of the world steel production. Metals 2017, 7, 163. [Google Scholar] [CrossRef]
  12. Zhang, J.L. Risks and Risk Management in Chinese Iron and Steel Enterprises. Adv. Mater. Res. 2011, 201, 1061–1065. [Google Scholar] [CrossRef]
  13. Ozorio, L.; Bastian-Pinto, C.; Baidya, T.K.; Brandão, L.E. Investment decision in integrated steel plants under uncertainty. Int. Rev. Financ. Anal. 2013, 27, 55–64. [Google Scholar] [CrossRef]
  14. Kaushal, L.A. Pohang Steel Company (POSCO) Project: Growth Versus Development; UNNAYAN, International Bulletin of Management and Economics: Gurgaon, India, 2017; Volume 7, Available online: (accessed on 11 December 2018).
  15. Kim, Y.; Shin, K.; Ahn, J.; Lee, E.B. Probabilistic Cash Flow-Based Optimal Investment Timing Using Two-Color Rainbow Options Valuation for Economic Sustainability Appraisement. Sustainability 2017, 9, 1781. [Google Scholar] [CrossRef]
  16. Lee, Y.J. Development of Index Model and Application: Investment Evaluation for Overseas Steel Business. Master’s Thesis, Pohang University of Science and Technology, Pohang, Korea, 2015. Available online: (accessed on 11 December 2018).
  17. Gibson, G.; Dumont, P.R. Project Definition Rating Index (PDRI) for Industrial Projects; Technical Research Report 113-11; Construction Industry Institute: Austin, TX, USA, 1996. [Google Scholar]
  18. Shen, L.Y.; Wu, G.W.; Ng, C.S. Risk assessment for construction joint ventures in China. J. Constr. Eng. Manag. 2001, 127, 76–81. [Google Scholar] [CrossRef]
  19. El-Sayegh, S.M. Risk assessment and allocation in the UAE construction industry. Int. J. Proj. Manag. 2008, 26, 431–438. [Google Scholar] [CrossRef]
  20. Mansfield, E. International Technology Transfer: Forms, Resource Requirements, and Policies. Am. Econ. Rev. 1975, 65, 372–376. [Google Scholar]
  21. Davidson, W.H.; McFetridge, D.G. Key Characteristics in the Choice of International Technology Transfer Mode. J. Int. Bus. Stud. 1985, 16, 5–21. [Google Scholar] [CrossRef][Green Version]
  22. Osland, G.E.; Taylor, C.R.; Zou, S. Selecting international modes of entry and expansion. Marketing Intell. Plan. 2001, 19, 153–161. [Google Scholar] [CrossRef][Green Version]
  23. Park, J.H. Development and Application of Checklist for the Evaluation of International Technology Transfer: From the Viewpoint of Licensor. Korean J. Econ. Manag. 2008, 26, 57–82. [Google Scholar]
  24. Ocampo-Duque, W.; Ferre-Huguet, N.; Domingo, J.; Schuhmacher, M. Assessing water quality in rivers with fuzzy inference systems: A case study. Environ. Int. 2006, 32, 733–742. [Google Scholar] [CrossRef]
  25. Yang, M.; Khan, F.I.; Sadiq, R. Prioritization of environmental issues in offshore oil and gas operations: A hybrid approach using fuzzy inference system and fuzzy analytic hierarchy process. Process Saf. Environ. Prot. 2011, 89, 22–34. [Google Scholar] [CrossRef]
  26. Liu, K.F.; Lai, J.H. Decision-support for environmental impact assessment: A hybrid approach using fuzzy logic and fuzzy analytic network process. Expert Syst. Appl. 2009, 36, 5119–5136. [Google Scholar] [CrossRef]
  27. Ganguly, K.K.; Kalyan, K.G. A fuzzy AHP approach for inbound supply risk assessment. Benchmarking 2013, 20, 129–146. [Google Scholar] [CrossRef]
  28. Amindoust, A.; Ahmed, S.; Saghafinia, A.; Bahreininejad, A. Sustainable supplier selection: A ranking model based on fuzzy inference system. Appl. Soft Comput. 2012, 12, 1668–1677. [Google Scholar] [CrossRef]
  29. Carrera, D.A.; Mayorga, R.V. Supply chain management: A modular fuzzy inference system approach in supplier selection for new product development. J. Intell. Manuf. 2008, 19, 1–2. [Google Scholar] [CrossRef]
  30. Diouf, M.; Kwak, C. Fuzzy AHP, DEA, and Managerial Analysis for Supplier Selection and Development; From the Perspective of Open Innovation. Sustainability 2018, 10, 3779. [Google Scholar] [CrossRef]
  31. Lee, A.H.; Chen, W.C.; Chang, C.J. A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Syst. Appl. 2008, 34, 96–107. [Google Scholar] [CrossRef]
  32. Chen, C.T.; Cheng, H.L. A comprehensive model for selecting information system project under fuzzy environment. Int. J. Proj. Manag. 2009, 27, 389–399. [Google Scholar] [CrossRef]
  33. Huang, Y.P.; Basanta, H.; Kuo, H.C.; Huang, A. Health symptom checking system for elderly people using fuzzy analytic hierarchy process. Appl. Syst. Innov. 2018, 1, 10. [Google Scholar] [CrossRef]
  34. Kim, J.; Kim, J. Optimal Portfolio for LNG Importation in Korea Using a Two-Step Portfolio Model and a Fuzzy Analytic Hierarchy Process. Energies 2018, 11, 3049. [Google Scholar] [CrossRef]
  35. Ravankar, A.; Ravankar, A.A.; Kobayashi, Y.; Emaru, T. Optimal Robot Path Selection Using Fuzzy Analytical Hierarchical Process. Multidiscip. Digit. Publ. Inst. Proc. 2017, 2, 111. [Google Scholar] [CrossRef]
  36. He, Y.; Wang, X.; Lin, Y.; Zhou, F. Optimal partner combination for joint distribution alliance using integrated fuzzy EW-AHP and TOPSIS for online shopping. Sustainability 2016, 8, 341. [Google Scholar] [CrossRef]
  37. Lee, Y.C.; Chou, C.J. Technology evaluation and selection of 3DIC integration using a three-stage fuzzy MCDM. Sustainability 2016, 8, 114. [Google Scholar] [CrossRef]
  38. Mokarram, M.; Najafi-Ghiri, M. Combination of Fuzzy Logic and Analytical Hierarchy Process Techniques to Assess Potassium Saturation Percentage of Some Calcareous Soils (Case Study: Fars Province, Southern Iran). Agriculture 2016, 6, 59. [Google Scholar] [CrossRef]
  39. Romeijn, H.; Faggian, R.; Diogo, V.; Sposito, V. Evaluation of deterministic and complex analytical hierarchy process methods for agricultural land suitability analysis in a changing climate. ISPRS Int. J. Geo-Inf. 2016, 5, 99. [Google Scholar] [CrossRef]
  40. Shen, L.; Govindan, K.; Shankar, M. Evaluation of barriers of corporate social responsibility using an analytical hierarchy process under a fuzzy environment—A textile case. Sustainability 2015, 7, 3493–3514. [Google Scholar] [CrossRef]
  41. Samanlioglu, F.; Taskaya, Y.E.; Gulen, U.C.; Cokcan, O. A Fuzzy AHP–TOPSIS-Based Group Decision-Making Approach to IT Personnel Selection. Int. J. Fuzzy Syst. 2018, 20, 1576–1591. [Google Scholar] [CrossRef]
  42. Chang, P.L.; Chen, Y.C. A fuzzy multi-criteria decision making method for technology transfer strategy selection in biotechnology. Fuzzy Sets Syst. 1994, 63, 131–139. [Google Scholar] [CrossRef]
  43. Baloi, D.; Price, A.D. Modelling global risk factors affecting construction cost performance. Int. J. Proj. Manag. 2003, 21, 261–269. [Google Scholar] [CrossRef]
  44. Hatefi, S.M.; Tamošaitienė, J. Construction projects assessment based on the sustainable development criteria by an integrated fuzzy AHP and improved GRA model. Sustainability 2018, 10, 991. [Google Scholar] [CrossRef]
  45. Mustafa, M.A.; Al-Bahar, J.F. Project risk assessment using the analytic hierarchy process. IEEE Trans. Eng. Manag. 1991, 38, 46–52. [Google Scholar] [CrossRef]
  46. Leśniak, A.; Kubek, D.; Plebankiewicz, E.; Zima, K.; Belniak, S. Fuzzy AHP Application for Supporting Contractors’ Bidding Decision. Symmetry 2018, 10, 642. [Google Scholar] [CrossRef]
  47. Ramkumar, M.; Schoenherr, T.; Jenamani, M. Risk assessment of outsourcing e-procurement services: Integrating SWOT analysis with a modified ANP-based fuzzy inference system. Prod. Plan. Control 2016, 27, 1171–1190. [Google Scholar] [CrossRef]
  48. Ramkumar, M. A modified ANP and fuzzy inference system based approach for risk assessment of in-house and third party e-procurement systems. Strateg. Outsourc. 2016, 9, 159–188. [Google Scholar] [CrossRef]
  49. Wang, Y.M.; Liu, J.; Elhag, T.M.S. An integrated AHP–DEA methodology for bridge risk assessment. Comput. Ind. Eng. 2008, 54, 513–525. [Google Scholar] [CrossRef]
  50. Taylan, O.; Kabli, M.R.; Porcel, C.; Herrera-Viedma, E. Contractor selection for construction projects using consensus tools and big data. Int. J. Fuzzy Syst. 2018, 20, 1267–1281. [Google Scholar] [CrossRef]
  51. Taylan, O.; Bafail, A.O.; Abdulaal, R.M.S.; Kabli, M.R. Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Appl. Soft Comput. 2014, 17, 105–116. [Google Scholar] [CrossRef]
  52. Chen, P.; Wang, J. Application of a fuzzy AHP method to risk assessment of international construction projects. In Proceedings of the 2009 International Conference on Electronic Commerce and Business Intelligence, Beijing, China, 6–7 June 2009; pp. 459–462. [Google Scholar] [CrossRef]
  53. Ebrahimnejad, S.; Mousavi, S.M.; Seyrafianpour, H. Risk identification and assessment for build–operate–transfer projects: A fuzzy multi attribute decision making model. Expert Syst. Appl. 2010, 37, 575–586. [Google Scholar] [CrossRef]
  54. Lam, K.C.; Wang, D.; Lee, P.T.; Tsang, Y.T. Modelling risk allocation decision in construction contracts. Int. J. Proj. Manag. 2007, 25, 485–493. [Google Scholar] [CrossRef]
  55. Tah, J.H.; Carr, V. Knowledge-based approach to construction project risk management. J. Comput. Civ. Eng. 2001, 15, 170–177. [Google Scholar] [CrossRef]
  56. Carr, V.; Tah, J.H. A fuzzy approach to construction project risk assessment and analysis: Construction project risk management system. Adv. Eng. Softw. 2001, 32, 847–857. [Google Scholar] [CrossRef]
  57. Abdelgawad, M.; Fayek, A.R. Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP. J. Constr. Eng. Manag. 2010, 136, 1028–1036. [Google Scholar] [CrossRef]
  58. KarimiAzari, A.; Mousavi, N.; Mousavi, S.F.; Hosseini, S. Risk assessment model selection in construction industry. Expert Syst. Appl. 2011, 38, 9105–9111. [Google Scholar] [CrossRef]
  59. Zegordi, S.; Nik, E.R.; Nazari, A. Power plant project risk assessment using a fuzzy-ANP and fuzzy-TOPSIS method. Int. J. Eng.-Trans. B Appl. 2012, 25, 107–120. [Google Scholar] [CrossRef]
  60. Karsak, E.E.; Tolga, E. Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments. Int. J. Prod. Econ. 2001, 69, 49–64. [Google Scholar] [CrossRef]
  61. Jaganathan, S.; Erinjeri, J.J.; Ker, J.I. Fuzzy analytic hierarchy process based group decision support system to select and evaluate new manufacturing technologies. Int. J. Adv. Manuf. Technol. 2007, 32, 1253–1262. [Google Scholar] [CrossRef]
  62. Project Management Institute. Project Management Body of Knowledge (PMBOK), 5th ed.; Project Management Institute: Newtown Square, PA, USA, 2013. [Google Scholar]
  63. Saaty, T.L. Decision-making with the AHP: Why is the principal eigenvector necessary. Eur. J. Oper. Res. 2003, 145, 85–91. [Google Scholar] [CrossRef]
  64. Abel, E.; Mikhailov, L.; Keane, J. Group aggregation of pairwise comparisons using multi-objective optimization. Inf. Sci. 2015, 322, 257–275. [Google Scholar] [CrossRef]
  65. Ishizaka, A.; Labib, A. Review of the main developments in the analytic hierarchy process. Expert Syst. Appl. 2011, 38, 14336–14345. [Google Scholar] [CrossRef]
  66. Kang, J.K.; Min, B.C. Theory and Practice of AHP; Intervision: Seoul, Korea, 2008. [Google Scholar]
  67. Saaty, T.L. Theory and Applications of the Analytic Network Process: Decision Making with Benefits, Opportunities, Costs, and Risks; RWS Publications: Pittsburgh, PA, USA, 2005. [Google Scholar]
  68. Siraj, S.; Mikhailov, L.; Keane, J. A heuristic method to rectify intransitive judgments in pairwise comparison matrices. Eur. J. Oper. Res. 2012, 216, 420–428. [Google Scholar] [CrossRef]
  69. MathWorks, Inc.; Wang, W.-C. Fuzzy Logic Toolbox: For Use with MATLAB: User’s Guide; Mathworks Incorporated: Natick, MA, USA, 1998. [Google Scholar]
  70. Kim, Y.G.; Lee, E.B. Optimal Investment Timing with Investment Propensity Using Fuzzy Real Options Valuation. Int. J. Fuzzy Syst. 2018, 20, 1888–1900. [Google Scholar] [CrossRef]
Figure 1. Research Methodology.
Figure 1. Research Methodology.
Sustainability 10 04780 g001
Figure 2. Pairwise Comparison Survey Example (R13).
Figure 2. Pairwise Comparison Survey Example (R13).
Sustainability 10 04780 g002
Figure 3. Membership Function.
Figure 3. Membership Function.
Sustainability 10 04780 g003
Figure 4. Final Risk Scoring Flow Diagram.
Figure 4. Final Risk Scoring Flow Diagram.
Sustainability 10 04780 g004
Table 1. Risk Factors of Project External Environment (R1) [16,22,23].
Table 1. Risk Factors of Project External Environment (R1) [16,22,23].
Level 1Level 2Level 3
Project External Environment
R11Characteristics of local governmentR111Business practices and consistency of laws and policies
R112Local government regulations on the industry
R113Need for localization
R12Economy, market situationR121The economic situation of the country to be promoted
R122Changes in economic indicators (exchange rate, inflation rate, interest rate, etc.)
R123Market demand for the target product and competition
R124Downstream industry and material prices volatility
R13Social and cultural characteristicsR131Social stability
R132Characteristics of local labor force
R133Cultural feature
R134Local awareness of the project
R14Geography/Climate and infrastructure conditionsR141Climate characteristics
R142Characteristics of soil
R143Distance from home country
R144Status and plans of Infrastructure and utility
R15Legal standards (regulations)R151Legal standards of design and licensing criteria
R152Tariff standard
R153Environmental regulations
R154Procedures and criteria for repatriation of profits
R155Regulations on transfer of technology in home country
Table 2. Risk Factors of Project Feasibility and Planning (R2) [16,22,23].
Table 2. Risk Factors of Project Feasibility and Planning (R2) [16,22,23].
Level 1Level 2Level 3
Project Feasibility and Planning
R21Members of the projectR211Characteristics of a local joint venture
R212Capabilities of sub-contractor and material supplier
R213Features of lender (requirements)
R22Coal, raw materials, cokeR221Conditions of coal, ore, and raw materials
R222Procurement plan of coal, ore, and raw materials
R23Scope and requirements for completion of the ProjectR231Characteristics (process composition) and capacity of target product
R232Schedule of the project
R233Suitability and validity of the applied process and technology
R234Documents and outputs related to the project
R235Performance requirements
R24Economics (profitability)R241Investment costs
R242Operating expenses
R243Revenue (product sales and prices)
R244Financing plan
R245Components and scale of license fees
Table 3. Risk Factors of Project Contract (R3) [16,22,23].
Table 3. Risk Factors of Project Contract (R3) [16,22,23].
Level 1Level 2Level 3
Project Contract
R31Clarity of contractR311Experience with similar contracts
R312Clarification of criteria on LD (liquidated damages)
R313Ambiguous contract terms (imperfection)
R314Specification of force majeure
R32License contractR321Infringement of intellectual property rights of third parties
R322Prohibition of license transfer
R33Technology protectionR331Technology spill prevention plan
R332Excessive requirements on the joint venture (or licensee) related to the technology
R333Access to operational records and ownership of developed technologies after completion
R34O&M contractR341Excessive O&M expenses
R342Poor plant availability and performance
O&M = Operation and Maintenance.
Table 4. Risk Factors of EPC (R4) [16,22,23].
Table 4. Risk Factors of EPC (R4) [16,22,23].
Level 1Level 2Level 3
R412Specification of major equipment
R413Timeliness of design
R414Design faults (errors) and omissions
R42ProcurementR421Manpower procurement plan
R422Procurement plan of major equipment
R43ConstructionR431Selection of suitable construction method
R432Transportation and quality assurance of construction materials and equipment
R433Collaboration with partners and local businesses
R434Worker’s safety management and construction safety facility
Table 5. Details of Case Study Projects.
Table 5. Details of Case Study Projects.
Project AProject B
CompanyNational Steel CompanyTrading company
Project3 million tons of integrated steel mill using new steel technology3 million tons of integrated steel mill using new steel technology
FinancingEquity to Debt = 40:60
Technology provider to Acquirer = 49:51
Equity to Debt = 30:70
Technology provider to Acquirer = 20:80
FeaturesDemand for steel in the region is expected to increase due to Western development strategies.
Eco-friendly steel mill with new technology is established in accordance with the government’s environmental regulations
New investments are made in steel plants as economic sanctions are lifted.
Local abundant natural gas can be used
Table 6. Risk Probability-Impact Matrix.
Table 6. Risk Probability-Impact Matrix.
Degree of Influence VLLMHVH
Likelihood of Occurrence
VL: very low; L: low; M: medium; H: high; VH: very high.
Table 7. Linguistic Variable and Membership Function Parameter.
Table 7. Linguistic Variable and Membership Function Parameter.
Linguistic VariableGaussian MF Parameter
Center (c)Sigma (σ)
Very Low010.5
Very High100
Table 8. Weights and Rankings of Risk Factors in Level 1 and Level 2.
Table 8. Weights and Rankings of Risk Factors in Level 1 and Level 2.
Level 1WeightRankLevel 2Local WeightGlobal WeightRank
R1Project External Environment0.1944R11Characteristics of local government0.264.9910
R12Economy, market situation0.203.9714
R13Social and cultural characteristics0.112.2016
R14Geography/Climate and infrastructure conditions0.173.3015
R15Legal standards (regulations)0.264.9611
R2Project Feasibility and Planning0.2841R21Project stakeholder0.144.0313
R22Coal, ore, and raw materials0.277.546
R23Scope and requirements for completion of the Project0.195.378
R24Economics (profitability)0.4011.471
R3Contract0.2782R31Clarity of contract0.349.433
R32License contract0.205.647
R33Technology protection0.287.715
R34O&M contract0.185.009
Table 9. Table9. Top 12 Level 3 Risk Factors.
Table 9. Table9. Top 12 Level 3 Risk Factors.
RankWeightRisk factor (Level 3)
24.75Design faults (errors) and omissions
33.75Poor plant availability and performance
43.22Access to operational records and ownership of developed technologies after completion
53.04Technology spill prevention plan
63.02Ambiguous contract terms (imperfection)
73.02Clarification of criteria on LD (liquidated damages)
82.91Investment costs
92.88Infringement of intellectual property rights by third parties
102.79Revenue (product sales and prices)
112.76Prohibition of license transfer
122.7Conditions for coal, ore, and raw materials
Table 10. Top Risk Factors for Case Study Projects.
Table 10. Top Risk Factors for Case Study Projects.
RankInitial Rank by PriorityProject AProject B
1Procurement plan of coal, ore, and raw materialsProcurement plan of coal, ore, and raw materialsProcurement plan of coal, ore, and raw materials
2Design faults (errors) and omissionsDesign faults (errors) and omissionsDesign faults (errors) and omissions
3Poor plant availability and performanceConditions for coal, ore, and raw materialsPoor plant availability and performance
4Access to operational records and ownership of developed technologies after completionTechnology spill prevention planConditions for coal, ore, and raw materials
5Technology spill prevention planInvestment costInvestment cost
6Ambiguous contract terms (imperfection)Ambiguous contract terms (imperfection)Ambiguous contract terms (imperfection)
7Clarification of criteria on LD (liquidated damages)Clarification of criteria on LD (liquidated damages)Clarification of criteria on LD (liquidated damages)
8Investment costPoor plant availability and performanceFinancing plan
9Infringement of intellectual property rights by third partiesAccess to operational records and ownership of developed technologies after completionTechnology spill prevention plan
10Revenue (product sales and prices)Specification of major equipmentSpecification of major equipment
11Prohibition of license transferProcurement plan of major equipmentProcurement plan of major equipment
12Conditions for coal, ore and raw materialsRequirements for preliminary commissioning and takeoverRevenue (product sales and prices)
Table 11. Responses to Top Risk Factors.
Table 11. Responses to Top Risk Factors.
Risk FactorResponse Mitigation Measures
Procurement plan of coal, ore, and raw materialsUnderstanding the status of available raw materials
Review of location and logistics
Review of feedstock supply agreement strategy
Design faults (errors) and omissionsCreation of design output checklist
Sharing design output by discipline and reinforcement of crosschecks
Strengthening communication with local companies
Conditions of coal, ore, and raw materialsPreliminary review and test of locally procured coal, ore, and raw materials
Technology spill prevention planPackaging design output and sharing only final output
Adjustment of scope of project output at contract
Investment costAdjustment of project scope
Optimization of equipment and design
Localization of equipment and design
Estimating the preliminary cost considering fluctuation such as exchange rates
Poor plant availability and performanceDocumentation of O&M techniques for existing plant
Improvement in availability and performance at the design stage
Configuration and application of proven facilities
Table 12. Risk Factors after Risk Response in Project A.
Table 12. Risk Factors after Risk Response in Project A.
1st Risk Assessment2nd Risk Assessment (After Response)
Risk Rank72.9702/10066.9258/100
WeightScore WeightScore
1Procurement plan of coal, ore, and raw materials4.854.86Procurement plan of coal, ore, and raw materials4.852.62
2Design faults (errors) and omissions4.753.69Technology spill prevention plan3.042.50
3Conditions of coal, ore and raw materials2.703.10Ambiguous contract terms (imperfection)3.022.44
4Technology spill prevention plan3.042.71Design faults (errors) and omissions4.752.30
5Investment cost2.912.58Clarification of criteria on LD (Liquidated damages)3.022.30
Table 13. Risk Factors after Risk Response in Project B.
Table 13. Risk Factors after Risk Response in Project B.
1st Risk Assessment2nd Risk Assessment (After Response)
Risk Score70.0003/10064.4484/100
WeightScore WeightScore
1Procurement plan of coal, ore, and raw materials4.853.77Ambiguous contract terms (imperfection)3.022.43
2Design faults (errors) and omissions4.752.93Clarification of criteria on LD (Liquidated damages)3.022.33
3Poor plant availability and performance3.752.69Financing plan2.602.29
4Conditions of coal, ore and raw materials2.702.53Technology spill prevention plan3.042.23
5Investment cost2.912.48Design faults (errors) and omissions4.752.02
Back to TopTop