Decision Support System For Risk Management Research Articles

An investigation of a risk management decision support system for Iraqi construction projects

Risk management (RM) is one of the critical success factors for the construction of projects, project participants generally do not have sufficient knowledge pertinent to risk management concept and the number of risk management support tools which facilitate the process are rather low. Research problem can be identify by the process of risk management, applying risk management in construction projects, types and identified, assessing and controlling of risk in construction projects. In order to facilitate risk management activities, decision support tools that will enable risk identification, analysis and response strategy formulation should be developed. Decision support tools are necessary for systematic identification of risks, scenario generation, proactive management of risk and integration of risk management activities with other project management functions such as planning, cost estimating and monitoring project success. The proposed decision support system, namely Integrated Risk Management System (IRMS), is designed to support the user at all phases of the risk management process. Major functions of IRMS include, risk analysis by Monte Carlo (MC) simulation (probability and rating degree), risk assessment by risk rating, risk re-assessment, response generation. The applicability of the system has been tested by a real case study (three Iraqi construction cases) and its functionality has been demonstrated using the data associated with these cases, study (three cases). Risk carting (RC) processes are reduced from high risk management to moderate ones for the three project cases.

Scientific, technical and institutional challenges towards next-generation operational flood risk management decision support systems

ABSTRACTThe global dissemination of operational systems for flood forecasting, early warning and risk management is much more heterogeneous than comparable forecasting services found in the meteorological community. This results primarily from the need for local ‘on-the-ground’ knowledge, such as addressing regional hydrological phenomena or the impact of local water resources management, and the challenge for small or weak institutions to integrate and operate such sophisticated systems. In this paper, we summarize and discuss the state-of-the-art of this kind of systems from a scientific, technical and institutional perspective and provide some recent applications. This leads to the development of an improved integration approach of existing building blocks and cross-organizational collaboration aimed at defining a next-generation flood risk management approach. The approach is implemented through the combination of a general platform with a community-driven effort, which substantially relies on local expertise.

An adaptive neuro-fuzzy inference system for assessment of risks to an organization’s information security

Sergey A. Glushenko - Senior Lecturer, Department of Information Systems and Applied Computer Science, Rostov State University of EconomicsAddress: 69, Bolshaya Sadovaya Street, Rostov-on-Don, 344002, Russian FederationE-mail: gs-gears@yandex.ru This article explains the importance of applying risk assessment in the implementation of information security systems. It is considered the most common risk assessment procedure and entails application of fuzzy logic theory for this purpose. The paper describes the proposed fuzzy production model (FPM), which defines seven input linguistic variables describing risk factors, four output linguistic variables that characterize different areas of information security risks, as well as four base rules. It is noted that the FPM is the first approach to the subject area and requires optimization to minimize the model’s output errors. The most common methods of optimization of fuzzy models parameters are examined, and the advantages of applying methods based on neuro-fuzzy networks (NFN) are justified. The article describes the process of converting fuzzy model elements, such as unit fuzzification, rule base unit and unit defuzzification, into fragments of the neural network. The result of this process is a neuro-fuzzy network corresponding to the fuzzy model. Formation of the developed NFN is based on an adaptive neuro-fuzzy inference system (ANFIS), using the specialized Neuro-Fuzzy Designer package of MATLAB software. The model was trained by a hybrid method which represents a combination of the methods of least squares and backpropagation. The result of this process is optimization (setting) the parameters of membership functions of input linguistic variables. Application of neuro-fuzzy modeling made it possible to obtain a more appropriate fuzzy production model which is able to conduct linguistic analysis of the risks of an organization’s information security. The information obtained with its help allows IT managers to determine risk priorities and to develop effective action plans to reduce the impact of the most dangerous threats.[1] This research has been carried out with financial support of RFBR within the framework of scientific project No. 16-31-00285 “Fuzzy logic methods and models in risk management decision support systems”

Marketing Risk Management of Palm Oil Based Biodiesel Agroindustry

Biodiesel is fuel generates from vegetable oils that have properties similar to diesel oil. The advantages of biodiesel compared to diesel is an environmentally friendly fuel because it produces much lower emissions (sulfur free, low smoke number) in accordance with global issues, higher cetane number (> 57) so that the combustion efficiency is better than diesel, lubrication properties of the piston engine; biodegradable, a renewable energy because it is made from natural materials, and improve the independence of fuel supply because it can be produced locally. The purpose of this research is to design the risk management decision support system for agro-industry development biodiesel of oil palm-based. Determination of objectives and risk management strategies using the used to obtain an alternative value in the aspect of marketing. This research resulted in a decision support system that is useful to help decision makers in addressing the risk of agro-bio-diesel. Risk management model is designed in a decision support system (DSS), can be used by industrial users and investors in the field of biodiesel. DSS software development using Microsoft Visual Basic Version 6.0 consists of three main components namely database management system, knowledge base management system and model base management system. Model base management system consists of risk marketing analysis.

Communicating risk through a DSS: A coastal risk centred empirical analysis

Risk communication has recently evolved from the design of unidirectional (from scientists to the public) information flow toward a more integrative deliberative procedures (involving scientists, policymakers, stakeholders and the general public) aimed at reconciling diverging social constructs of risk. Furthermore, risk communication is seen now as an activity that is transverse to the risk governance process as a whole. Risk communication is therefore part of the preassessment, appraisal, characterization/evaluation and management phases of risk governance. At the same time the development of risk management Decision Support Systems are increasingly geared at facilitating decision making while taking into account and streamlining all the phases of the risk governance process. These recent trends lead to a redefinition of the role of risk communication in the context of the development of DSS.This paper explores these issues by analysing how risk communication can be integrated into THESEUS's DSS. A first step of this analysis consists of applying grounded theory to analyse stakeholders' perception in three of THESEUS's application settings. We then compare this theorization to the grounded theorization of the foundational model of THESEUS's DSS. The result of this comparison points to diverging, yet not incompatible, paradigmatic views on the nature of coastal risks. These divergences are further analysed through semi-structured interviews with key informants involved in the development of the DSS.Building on these results we develop a communication scheme that should allow a progressive convergence of paradigmatic views occurring through the use of the DSS; we are thus proposing that the DSS in itself be a locus where risk communication as a deliberative practice occurs. In order to achieve this we propose that the cognitive pathways followed by DSS users be proactively designed and involves integrative exchanges between designers, users and policy makers.

Progress towards and barriers to implementation of a risk framework for US federal wildland fire policy and decision making

In this paper we review progress towards the implementation of a risk management framework for US federal wildland fire policy and operations. We first describe new developments in wildfire simulation technology that catalyzed the development of risk-based decision support systems for strategic wildfire management. These systems include new analytical methods to measure wildfire risk to human and ecological values and to inform fuel treatment investment strategies at national, regional, and local scales. Application of the risk management framework to support wildfire incidents has been dramatically advanced with the Wildland Fire Decision Support System and allowed policy modifications that encourage management of incidents for multiple objectives. The new wildfire risk management decision support systems we discuss provide Federal agencies in the US the ability to integrate risk-informed approaches to a wide range of wildfire management responsibilities and decisions. While much progress has been made, there remain several barriers that need to be addressed to fully integrate risk science into current wildfire management practices. We conclude by identifying five primary issues that if properly addressed could help public land management better realize the opportunities and potential payoffs from fully adopting a risk management paradigm.

Knowledge level modeling for systemic risk management in financial institutions

The current subprime mortgage crisis is a typical case for systemic risk in financial institutions. In order to further our understanding and communication about systemic risk management (SRM) in financial institutions, this paper proposes a knowledge level model (KLM) for systemic risk management in financial institutions. There are two parts considered in the proposed KLM: ontologies and problem solving method (PSM). Ontologies are adopted to represent a knowledge base of KLM, which integrates top level ontology and domain level ontologies. And then the problem solving method is given to show the reasoning process of this knowledge. The symbol level of KLM is also discussed which integrates OWL, SWRL and JESS. Further, the discussion about Lehman Brother’s Minibonds case in 2008 is provided to illustrate how proposed KLM is used in practice. With these, first, they will enhance the interchange of information and knowledge sharing for SRM within a financial institution. Second, they will assist knowledge base development for SRM design, for which a prototype of financial systemic risk management decision support system is given in this study. Third, they will support coordination among different institutions by using standardized vocabularies. And finally, from the design science perspective, the whole proposed framework could be meaningful to models in other domains.