An Evaluation of Total Project Risk Based on Fuzzy Logic

By Doskocil, Radek | Business: Theory and Practice, March 2016 | Go to article overview

An Evaluation of Total Project Risk Based on Fuzzy Logic


Doskocil, Radek, Business: Theory and Practice


Introduction

Project management is nowadays a widely discussed theme. This fact is substantiated by numerous scientific articles, books and publications dealing with these problems (Bergantinos, Vidal-Puga 2009; Perez et al. 2005; Rosenau 2007; Schwable 2011). This discipline is also included in the courses of numerous faculties focusing on economy both in the Czech Republic and abroad. Experts are also associated in various professional organizations or associations (International Project Management Association 2014; Project Management Institute 2014; AXELOS 2015).

Project risk management is the responsibility of the entire life cycle of project. Risk management of the project consists of process of risk analysis and process of risk monitoring. Project manager or other members of project team can for risk project analysis use some methods (Rais, Smejkal 2013): scoring method (Podmolik 2006), FRAAP method--Facilitated Risk Analysis and Assessment Process (Peltier 2005), RIPRAN method (Lacko 2004; Dolezal et al. 2012) and more.

Risks evaluations are essential activities of management of proj ect risk. It is directly determines the success or failure of a project. It is often based on vague, inconsistent, partially subjective data (knowledge) items of interdisciplinary nature.

For this reason, in risk management used different approaches, techniques and tools, both traditional and advanced. For example expert method, brainstorming, simulation, fuzzy sets, e.g. methods of fuzzy numbers and fuzzy logic.

The authors Banaitiene et al. present a research in area of construction projects. The aim of the research is to discover how construction companies perceive the significance of the construction proj ects risks they face and the extent to which they employ potential risk responses (Banaitiene et al. 2011). The article "Construction Project Risk Assessment Model" of the authors Zhang and Li presents the use of fuzzy mathematical theory and gray relational analysis method in the risk evaluation of construction project (Zhang, Li 2011).

The authors Park et al. present a systematic framework for risk management is proposed for handling risk factors, risk degrees, integrated risk degree, and responding activities with corresponding data flow diagrams in their article "A Risk Management System Framework for New Product Development (NPD)" (Park et al. 2011).

The research by the authors Liu and Ye presented models for comprehensive evaluating modelling of investment project risk with trapezoid fuzzy linguistic information. A practical example for evaluating the investment project risk is used to verify the developed approach (Liu, Ye 2015).

The method of fuzzy logic applied to the risk management process is described in (Nasirzadeh et al. 2014). The authors present an integrated fuzzy system dynamic modelling for quantitative risk assessment. The values of the various factors, which are characterized by the nature of uncertainty, are defined by fuzzy numbers. The proposed model was simulated at different levels of risk; the optimum level of risk is determined by the point at which the minimum cost of the project. See e.g. (Nasirzadeh et al. 2014).

The same risk issues of construction projects are presented by authors Yao-Chen Kuo and Shih-Tong Lu. This study deals with a fuzzy multiple criteria decision-making (FMCDM) approach to systematically assess risk for a metropolitan construction project where twenty risk factors were identified. Triangular fuzzy sets are used for describing of identified factors. The overall risk level of the project depends on the individual impact of individual risk factors; the scheme was evaluated based on the relative impact and likelihood. They note that the suggested model for risk assessment is more reliable, more convenient than traditional statistical methods, and that this model can be used to efficiently identify risks metropolitan construction projects. …

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