scholarly journals PROJECT RISK EVALUATION BY USING A NEW FUZZY MODEL BASED ON ELENA GUIDELINE

2018 ◽  
Vol 24 (4) ◽  
pp. 284-300 ◽  
Author(s):  
Pezhman Asadi ◽  
Javad Rezaeian Zeidi ◽  
Toraj Mojibi ◽  
Abdolreza Yazdani-Chamzini ◽  
Jolanta Tamošaitienė
Keyword(s):  
Risk Management ◽  
Fuzzy Inference System ◽  
Risk Evaluation ◽  
Fuzzy Inference ◽  
Management Process ◽  
Project Risk ◽  
Inference System ◽  
Proposed Model ◽  
Risk Management Process ◽  
Risky Projects

The complexity and dynamics of the executive projects have coped contractors with substantial hazards and losses. Project risk management is a critical tool for authority to improve its performance and secure the success of the organization. However, a number of standards and approaches have been developed to formulate the projects based on their risks. The Elena guideline is a systematic standard developed by Iran Project Management Association. This guideline provides the full cycle of the risk management process. Risk evaluation is the key part of the risk management process. On the other hand, different techniques have been developed to model a risk evaluation problem. Fuzzy inference system is one of the most popular techniques that is capable of handling all types of the uncertainty involved in projects. This paper proposes a three-stage approach based on the fuzzy inference system under the environment of the Elena guideline to cope with the risky projects. Finally, an illustrative example of the risk evaluation is presented to demonstrate the potential application of the proposed model. The results show that the proposed model evaluates the risky projects efficiently and effectively.

Failure mode, effects and criticality analysis improvement by using new criticality assessment and prioritization based approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ammar Chakhrit ◽  
Mohammed Chennoufi
Keyword(s):  
Failure Mode ◽  
Conventional Method ◽  
Fuzzy Inference System ◽  
Risk Evaluation ◽  
Failure Modes ◽  
Fuzzy Inference ◽  
Decision Makers ◽  
Inference System ◽  
Content Type ◽  
Criticality Analysis

Purpose This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios. Design/methodology/approach To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method. Findings This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method. Originality/value This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

Customisable framework for project risk management

2017 ◽  
Vol 17 (1) ◽  
pp. 68-89 ◽  
Author(s):  
Jennifer Firmenich
Keyword(s):  
Risk Management ◽  
Management Practices ◽  
New Institutional Economics ◽  
Future Research ◽  
Project Risk Management ◽  
Management Process ◽  
Project Risk ◽  
Content Type ◽  
Risk Management Process ◽  
Management Concepts

Purpose The purpose of this paper is to emphasise on the need for efficient and effective project risk management practices and to support project managers in increasing the cost certainty of projects by proposing a new framework for project risk management. Design/methodology/approach The author adopts a “constructivist” methodology, drawing on practices common in construction management sciences and new institutional economics. Findings The author presents a holistic and customisable project risk management framework that is grounded in both practice and academia. The framework is holistic because, amongst others, all steps of the typical risk management process are addressed. The framework is customisable, because it allows for alternative ways of implementing the project risk management steps depending on the project-specific circumstances. Research limitations/implications The framework does not address the potential unwillingness of the project players to set up a project risk management process, at all. The proposed framework has not yet been tested empirically. Future research will seek to validate the framework. Originality/value The framework is designed to account for the difficult circumstances of a complex construction project. It is intended to support decision makers in customising a practical yet comprehensive project risk management concept to the characteristics of the unique project. Although many other project risk management concepts are designed based on the assumption that actors are perfectly rational and informed, this framework’s design is based on the opposite assumption. The framework is dynamic and should adapt over time.

scholarly journals Exploring the Project Risk Management: Highlighting the Soft Side of Project Management

2021 ◽  
Author(s):  
Muhammad T. Hatamleh
Keyword(s):  
Risk Management ◽  
Project Management ◽  
Leadership Style ◽  
Risk Attitude ◽  
Management Process ◽  
Participative Leadership ◽  
Project Risk ◽  
Analytical Skills ◽  
Risk Management Process ◽  
Process Groups

The majority of the approaches to managing project risk follow the logic of process groups. Project Management Institute (PMI) has 29 tools and techniques related to risk management process groups. Consequently, engineering and business schools have been accused of educating managers with sharp analytical skills but little understanding of social problems. The literature suggests that too much attention is focused on learning the techniques and formalities of risk management but not enough on the advanced issues of management. Also, the literature argues that there are two approaches to project management (hard and soft). The hard side only covers part of the managerial aspects which helps to manage foreseeable uncertainties. However, unforeseeable uncertainties need skills that related to soft side approaches such as emotional intelligence, navigating the organization’s culture, risk attitude, participative leadership style, and managing the relationship with stakeholders. This study provides an intensive review of the literature to discuss the need for integrating the hard and soft sides of management to achieve an effective risk management process. In addition, it proposes a conceptual framework that provides guidelines to enhance overall risk management efficiency.

scholarly journals Evaluation of treated municipal wastewater effluent for agricultural irrigation purposes using the fuzzy effluent quality index (FEQI)

2019 ◽  
Vol 20 (1) ◽  
pp. 148-156
Author(s):  
Seyed Hesam Alihosseini ◽  
Ali Torabian ◽  
Farzam Babaei Semiromi
Keyword(s):  
Fuzzy Inference System ◽  
Quality Index ◽  
Municipal Wastewater ◽  
Fuzzy Inference ◽  
Treatment Plant ◽  
Wastewater Effluent ◽  
Inference System ◽  
Effluent Quality ◽  
Proposed Model ◽  
Treated Municipal Wastewater

Abstract The issues of freshwater scarcity in arid and semi-arid areas could be reduced via treated municipal wastewater effluent (TMWE). Artificial intelligence methods, especially the fuzzy inference system, have proven their ability in TMWE quality evaluation in complex and uncertain systems. The primary aim of this study was to use a Mamdani fuzzy inference system to present an index for agricultural application based on the Iranian water quality index (IWQI). Since the uncertainties were disregarded in the conventional IWQI, the present study improved this procedure by using fuzzy logic and then the fuzzy effluent quality index (FEQI) was proposed as a hybrid fuzzy-based index. TMWE samples of the Gheitarie wastewater treatment plant in Tehran city recorded from 2011 to 2017 were taken into consideration for testing the ability of the proposed index. The results of the FEQI showed samples categorized as ‘Excellent’ (21), ‘Good’ (10), ‘Fair’ (4), and ‘Marginal’ (1) for the warm seasons, and for the cool seasons, the samples categorized as ‘Excellent’, ‘Good’ and ‘Fair’ were 17, 18 and 1, respectively. Generally, a comparison between the IWQI and proposed model results revealed the FEQI's superiority in TMWE quality assessment.

Risk Management Metrics

2022 ◽  
pp. 56-66
Author(s):  
Rimsy Dua ◽  
Samiksha Sharma ◽  
Rohit Kumar
Keyword(s):  
Risk Management ◽  
Project Management ◽  
Software Project ◽  
Management Process ◽  
Project Risk ◽  
Proactive Approach ◽  
Uncertainty Factor ◽  
The Past ◽  
Risk Management Process ◽  
Future Events

This chapter describes how risk management deals with the detection, the evaluation and the precedence of the risks in the process of project management. There is always an uncertainty factor related to the decisions of an investment while managing a project. Risk management is a proactive approach to deal with such future events that can lead to slow performance of the software project management. For successful risk management; there are different metrics that have been used in the past and are being getting used in the present for inspecting the progress of a project at specific points in a timeline that help in reducing the amount of risk. For the adoption of effective metrics for risk management, data is required. All of the metrics can be applied to the different domains of project, process and product. The chapter also covers strategies to advance, distinguish, estimate, and forecast the risk management process. A review of the key point indicators (KPIs) are also integrated along with the project metrics to signify the future and the present renderings.

scholarly journals Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

2017 ◽  
Vol 6 (2) ◽  
pp. 45 ◽  
Author(s):  
Ravi Kumar Sharma ◽  
Dr. Parul Gandhi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Software Systems ◽  
Data Sets ◽  
Component Reliability ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Estimate Reliability

There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate esti-mation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.

Sign in / Sign up

Export Citation Format

Share Document