An Evaluation of Total Project Risk Based on Fuzzy Logic

Doskočil, Radek

doi:10.3846/btp.2016.534

Cited by 22 publications

(13 citation statements)

References 22 publications

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“…The current approach, for example, in the field of risk engineering, applied either numerical values of probability and impact or worked with classical sharp jurisdiction of these values into certain sets. It was not appropriate for many applications and did not correspond to the actual perception of risk [32].…”

Section: Risk Management: Fuzzy Risk Quantificationmentioning

confidence: 99%

Risk Management and Knowledge Management as Critical Success Factors of Sustainability Projects

Doskočil

Lacko

2018

Sustainability

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The paper is focused on the analysis of the key aspects of sustainability projects, namely advanced risk management and project knowledge. These aspects are recommended to the attention of institutions and project managers when designing and executing new projects simultaneously with quality and project status management. The aim of the paper is to point out the critical factors that have recently affected the success of sustainability projects, which is also its contribution. Empirical research focused on the identification of the application level of the post-project phases in project management in the Czech Republic in 2016 and 2017 was performed. The research was performed as qualitative research employing observation and inquiry methods in the form of a controlled semistructured interview. The research identified 21 most common reasons for not executing post-project phases. Ensuring good and efficient progress of post-project phases, in particular by the means of post-implementation system analysis and compilation of a set of improvement suggestions for subsequent project management, forms the practical background for application of knowledge management and project management principles. A case study focused on the application of fuzzy logic in project risk assessment has been elaborated. In practice, current project management requires the application of advanced risk analysis methods that will replace the simple risk values estimated by calculations of separate risk components. •Advanced risk management of sustainability development projects. • Utilization of knowledge management by employing post-project phases in the project life cycle.

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Section: Risk Management: Fuzzy Risk Quantificationmentioning

confidence: 99%

Risk Management and Knowledge Management as Critical Success Factors of Sustainability Projects

Doskočil

Lacko

2018

Sustainability

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“…For example, artificial neural networks have been applied to estimation of production costs [31], prediction of product reliability performance [33], and cost estimation of the product life cycle in conceptual design [34]. Fuzzy logic-based approaches can support the decision makers in estimating market demand for new product development [35], estimating reliability improvement during product development [36], selecting suppliers for new product development [37], evaluating project risk, and status [38][39][40]. In turn, fuzzy neural systems can be used to create innovative product concepts [41], generate customer satisfaction model [42], or select the most promising NPD portfolio [43].…”

Section: Computational Intelligence Techniques In New Product Developmentioning

confidence: 99%

Computational Intelligence for Estimating Cost of New Product Development

Relich

2016

Foundations of Management

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The main aim of the work is to assess physical parameters of forest woodchips and their impact on the prices achieved by the supplier in transactions with a power plant. During fragmentation of logging residue, high content of green matter and contaminants negatively impacts the quality parameters that serve as basis for settlements. The analysis concerns data on the main parameters -water content, fuel value, sulphur and ash content -from 252 days of deliveries of forest chips to a power plant. The deliveries were realised from forested areas on an average about 340 km from the plant. Average water content and the resultant fuel value of forest chips was within 27-47% and 8.7-12.9 GJ×Mg −1 (appropriately), respectively. They depend on the month in which they are delivered to the power plant. The threshold values for the above-mentioned parameters are set by the plant at a real level and the suppliers have no problems with meeting them. The parameter that is most frequently exceeded is ash content (11.5% of cases). The settlement system does not differentiate on the basis of the transport distance but gives possibility to lower the settlement price when the quality parameters are not met but provides no reward for deliveries with parameters better than the average ones. On the basis of results obtained, it was calculated that average annual settlement price is lower than the contract price by about 0.20 PLN×GJ −1 , which in case of the analysed company may translate into an average daily loss of about 700 PLN.

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“…Therefore, fuzzy logic provides a simple method to reach a definite conclusion based on vague, ambiguous, imprecise or missing information. This is especially useful way to assess risk levels in cases where experts (participants) do not have enough reliable data (Iliadis, 2005;Jiang et al, 2009;Doskočil, 2015). The use of fuzzy sets in Crisis management is not a rare approach.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Approach for Group Decision-Making in Crisis Situations

Škoda

Flégl

Lozano

2021

Business: Theory and Practice

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The importance of correct and clear decisions during a complex and difficult situation is very easy to understand, but not so easy to achieve. Especially in situations where decision-makers must decide under time pressure and uncertainty. For instance, typical crisis situations have such characteristics. Given the uncertainty, subjectivity and ambiguity of human knowledge, crisis situations are also characterized by conflicting interests. In this article, we propose an approach based on fuzzy set theory to help decision-makers to find the collective decision considering weights of each member of the decision-making group. More specifically, the proposed approach uses new and innovative transformation of fuzzy numbers through α-level cuts. The key role in the transformation process is played by the shape and position of fuzzy numbers. Additionally, the Hamming distance will be used for the final interpretation of the results, in order to minimize the loss of information caused by defuzzification.

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An Evaluation of Total Project Risk Based on Fuzzy Logic

Cited by 22 publications

References 22 publications

Risk Management and Knowledge Management as Critical Success Factors of Sustainability Projects

Risk Management and Knowledge Management as Critical Success Factors of Sustainability Projects

Computational Intelligence for Estimating Cost of New Product Development

Fuzzy Approach for Group Decision-Making in Crisis Situations

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