scholarly journals USING FUZZY ANALYTIC NETWORK PROCESS AND ISM METHODS FOR RISK ASSESSMENT OF PUBLIC-PRIVATE PARTNERSHIP: A CHINA PERSPECTIVE

2019 ◽  
Vol 25 (2) ◽  
pp. 168-183 ◽  
Author(s):  
Yan Li ◽  
Xinyu Wang
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Assessment Tool ◽  
Analytic Network Process ◽  
Interest Rate Risk ◽  
Public Private Partnership ◽  
Interpretive Structural Modeling ◽  
Private Partnership ◽  
Network Process ◽  
Fuzzy Analytic Network Process

The public-private partnership (PPP) has been adopted globally to meet intensifying demands for public facilities and services. However, PPP projects contain a variety of risks which may lead to project failure. Many researchers have explored risk factors associated with PPP projects in developing countries. However, these investigations have limited their aim to understanding risk impact without considering the interactions of these factors. Hence, to fill this gap, this study proposes a risk assessment method, addressing vital interrelationships and interdependencies. Two methodologies, fuzzy analytic network process (F-ANP) and interpretive structural modeling (ISM), were applied to avoid vagueness and data inaccuracies. The primary contributions of this paper were considering the relationships among risk factors and risk priority; and offering a risk analysis approach based on linguistic scales and fuzzy numbers to reflect different neutral, optimistic and pessimistic viewpoints from expert respondents’ judgments. Results from this analysis showed that legal and policy risk was the most influential and interdependent risk, and interest rate risk was the most essential risk in Chinese PPP projects. The ISM structure diagram demonstrated that most of 35 identified risk factors had high driving and dependence power. This study proposed a systematic and practical method to identify and assess PPP risk factors, utilizing an integrated approach consisting of F-ANP and ISM, which has not been used for risk assessment in the construction field. This paper provides a new risk assessment tool and a basis for risk management strategies in the construction engineering and management field.

AN INTEGRATED INTERPRETIVE STRUCTURAL MODELING–FUZZY ANALYTIC NETWORK PROCESS–BENEFITS, OPPORTUNITIES, COSTS AND RISKS MODEL FOR SELECTING TECHNOLOGIES

2011 ◽  
Vol 10 (05) ◽  
pp. 843-871 ◽  
Author(s):  
AMY H. I. LEE ◽  
HE-YAU KANG ◽  
CHAO-CHENG CHANG
Keyword(s):  
New Product ◽  
Structural Modeling ◽  
Analytic Network Process ◽  
Appropriate Technology ◽  
Evaluation Framework ◽  
Technology Evaluation ◽  
Interpretive Structural Modeling ◽  
Network Process ◽  
Multiple Experts ◽  
Fuzzy Analytic Network Process

Technology evaluation has been increasingly important because of the pressing needs of new product introduction in a competitive global market. To select the most appropriate technology, a firm needs to have a robust technology evaluation framework to evaluate several technology candidates based on multiple criteria and evaluated by multiple experts. Thus, this paper presents an integrated model for evaluating various technologies for New Product Development (NPD). A network that takes into account the benefits, opportunities, costs, and risks (BOCR) aspects of different technologies is constructed first, and interpretive structural modeling (ISM) is applied next to determine the interrelationships among the factors. Finally, fuzzy analytic network process (FANP) is used to facilitate the evaluation process of decision makers under an uncertain environment with interrelated factors. The proposed model is applied in a flat panel manufacturer in selecting the most suitable panel technology.

scholarly journals Developing an AHP Risk Assessment Model for PPP Projects in Jordan

2021 ◽  
Author(s):  
Maram Almuhisen ◽  
◽  
Hüseyin Gökçekuş ◽  
Tahir Çelik ◽  
◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Success Factors ◽  
Quantitative Model ◽  
Public Private Partnership ◽  
Assessment Model ◽  
Study Phase ◽  
Private Partnership ◽  
Factors Affecting ◽  
Goods And Services

For the Jordanian government, meeting the growing demand for goods and services on its own is very difficult, leading to the increased dependence on other sectors of society. This research is aimed at understanding the critical success factors of local public-private partnership projects, identifying the most vital risk factors affecting projects, and establishing a quantitative model for risk assessment. The model can assist public-private partnership contributors by transforming the basic risk assessment principles into a more facilitated and systematic arithmetical based approach. The results showed that the risk factors with the highest ranks (respectively) are transfer phase, organizational risks, financing phase, project management risks, and feasibility study phase. The research is ultimately aimed at developing a framework for the risk evaluation of public-private partnerships within the construction industry in Jordan.

Risk assessment of public-private partnership in Russia based on the fuzzy logic method

2020 ◽  
pp. 132-143
Author(s):  
A. N. Savrukov ◽  
N. Т. Savrukov ◽  
E. A. Kozlovskaya
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Fuzzy Logic ◽  
Public Private Partnership ◽  
Risk Level ◽  
Project Implementation ◽  
Private Partnership ◽  
Aggregate Risk ◽  
Fuzzy Logic Method ◽  
Project Level

This paper is the first to assess the integral level of risks in public-private partnership (PPP) projects being implemented in Russia, taking into account the stages of project implementation. The results of empirical assessments have shown that the aggregate risk level is now estimated by experts as high. The key risks are incorrect assessment of project parameters, possible change of tariffs as well as risks related to the construction stage and demand for services. The majority of risk factors in PPP projects are of sectoral, project-level nature and are not related to economic, political or legal conditions. The study has shown differences in the structure of risks that partnership subjects are willing to take and transfer to another party, which has allowed to justify the distribution of risks among participants.

A Combined Fuzzy Analytic Network Process and Fuzzy-TOPSIS Model for Project Risk Assessment

2011 ◽  
pp. 275-283 ◽  
Author(s):  
Ebrahim Rezaee Nik ◽  
Seyed Hessameddin Zegordi ◽  
Ahad Nazari ◽  
Masatoshi Sakawa ◽  
Fereydoon Honari Choobar
Keyword(s):  
Risk Assessment ◽  
Fuzzy Topsis ◽  
Analytic Network Process ◽  
Project Risk ◽  
Network Process ◽  
Fuzzy Analytic Network Process ◽  
Topsis Model ◽  
Project Risk Assessment

scholarly journals AN ANALYTIC NETWORK PROCESS BASED RISK ASSESSMENT MODEL FOR PPP HYDROPOWER INVESTMENTS

2021 ◽  
Vol 27 (4) ◽  
pp. 268-277
Author(s):  
Emre Caner Akcay
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Analytic Network Process ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Hydropower Project ◽  
Risk Rating ◽  
Legal Risks ◽  
Network Process

The number of public-private partnership (PPP) projects has gone up especially in developing countries. The risk assessment of PPP projects is essential in ensuring project success. The objective of this study is to develop an Analytic Network Process (ANP) based risk assessment model for hydropower investments, and a tool to facilitate quantification of risk ratings based on this model. The results show that the three most important risk factors that affect the overall risk rating of a PPP hydropower investment are legal risks, contractor/subcontractor risks, and operator risks. In addition, the three most important risk clusters were identified as stakeholders, government requirements, and resources, whereas market was the least important cluster. The tool that measures the risk rating of a PPP of hydropower project was tested on ten real cases, and satisfactory results were obtained in terms of its predictive capability. The contributions of this research include (1) identification of the risk factors and clusters of factors associated with PPP hydropower investments; (2) determination of the priority of each risk factor and cluster; (3) development a tool that guides the investors through the risk assessment of PPP hydropower investments.

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