A Critical Review of Risk Management Support Tools


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Sobre herramientas para gestión de riesgos
  Dikmen, I, Birgonul, M T and Arikan, A E (2004) A critical review of risk management support tools.  In:  Khosrowshahi, F (Ed.), 20th Annual ARCOM Conference , 1-3 September 2004, Heriot Watt University. Association of Researchers in Construction Management, Vol. 2, 1145-54. A CRITICAL REVIEW OF RISK MANAGEMENT SUPPORT TOOLS Irem Dikmen 1 , M. Talat Birgonul 2  and A. Erdem Arikan3 1,2,3  Department of Civil Engineering, Middle East Technical University,Ankara,06531,Turkey It has been claimed by many researchers that “a risk driven approach” to project management is necessary to increase the success of construction projects. Literature is very rich in conceptual frameworks to overcome the informality of risk management efforts. However, risk management paradigms exist as methodologies rather than systems which can fully support the risk management process. The existing risk management support tools are usually based on quantitative risk analysis whereas the other phases are carried out external to the software. Risk registers and risk assessment tools are proposed as decision support systems which can only be used at specific stages of a construction project for specific purposes such as time/cost estimation at the bidding stage, country risk assessment during international market selection etc. Moreover, the proposed risk management support tools usually do not foster integration of risk management activities between the parties involved in the construction supply chain, do not consider impact of risks on all of the project success criteria, and can not handle subjectivity. In the recent years, the research has shifted to information and process models in which risks and response strategies may be identified, analysed and managed in a formal way by the use of database and model management systems. The major objective of this paper is to make a critical review of existing risk management support tools and propose development of a risk management corporate memory coupled with a decision support tool for successful management of risk. Keywords: decision support systems, information modelling, risk management. INTRODUCTION Risk management (RM) is about definition of objective functions to represent the expected outcomes of a project, measuring the probability of achieving objectives by generating different risk occurrence scenarios and development of risk response strategies to ensure meeting/exceeding the preset objectives. Risk management in construction is a tedious task as the objective functions tend to change during the  project life cycle, and the risk scenarios are numerous due to sensitivity of construction projects to uncontrollable risks stemming from the macro-environment, existence of high number of parties involved in the project value chain, and one-off nature of the construction process. Risk management support tools are required in order to systematise the process, to overcome some of the analytical difficulties such as calculating performance of the project under different scenarios, and finally to incorporate experience from previous projects into the decision making process. In this paper, what has been covered in the construction management literature till date is 1  irar@metu.edu.tr   2   birgonul@metu.edu.tr   3  aearikan@metu.edu.tr     Dikmen, Birgonul and Arikan 1146 discussed as well as what shall be done to improve the risk management process in construction. The aim of the paper is not to discuss all the previous work in this area, rather it is to summarise the general research trend by referring to specific examples. RM IN CONSTRUCTION Literature review shows that research on RM can be grouped in four categories: (1) development of conceptual frameworks and process models for systematic risk management, (2) investigation of risks, risk management trends and perceptions, (3) application of risk identification and analysis techniques in specific projects and (4) development of integrated risk management support tools. In the following parts, some examples are presented to highlight the general aim and scope of work that may fall in each category. Development of conceptual frameworks and process models One of the earliest efforts to define risk management process belonged to Hertz and Thomas (1983), who proposed a step-wise procedure of risk identification, measurement, evaluation and re-evaluation. Further, Hayes et al. (1986), Flanagan and  Norman (1993), Raftery (1994), Edwards (1995) proposed reference frameworks comprising of risk identification, risk analysis, response planning, continuous monitoring, feedback for risk learning and action planning. All of these frameworks imply a systematic approach for management of risk by following a risk identification-analysis-response-monitor loop. Moreover, several institutions provided  procedural, task-based guides for construction risk management. RISKMAN endorsed  by European Community (Carter et al. 1994); Project Risk Analysis and Management Methodology (PRAM) introduced by Association of Project Managers (Chapman 1997); Risk Analysis and Management for Projects Methodology (RAMP) promoted  by Institution of Civil Engineers (1998); and PMBoK guide of Project Management Institute (2000), all attempt to eliminate informality of risk management activities and integrate risk management with other project management functions. With slight differences in model architectures, number of separate phases, level of detail and coverage of project life cycle, all of the above mentioned RM process models and reference frameworks share a common goal and have similar characteristics. A more recent research theme is discussion of critical success factors for the implementation of process models. Researchers proposed different decision support systems and information models to implement the conceptual process models in practice. For example, Tah and Carr (2000) pointed out the vital role of a common language and  proposed an information model for the risk management process. Jaafari (2001) indicated the importance of management information and decision support systems that can integrate all aspects on a real time basis. Similarly, “soft systems” aspects of RM and human problems of implementation of RM in different organisational contexts have also been discussed by researchers (e.g. Edwards and Bowen 1998). Interpersonal communication of risk and learning from risk experiences have been indicated as important as the “hard systems” approach for the implementation of  proposed methodologies. Investigation of risks, RM trends and perceptions Research under this category is directed towards identification of risk factors specific to different projects, project delivery systems, international markets and investigation of risk perception of people within the construction industry. Thus, questionnaires, interviews and case-studies constitute the major research methodology in this   A critical review of risk management support tools 1147 category. As there is no single categorisation of risk agreed upon by all researchers and different typologies are proposed serving different purposes, numerous questionnaire studies have been conducted using different typologies. As an example for investigation of risks in specific projects, Tiong (1995) reviewed risks and guarantees in build operate transfer projects by referring to questionnaire findings. As an example for research on risk perceptions, Kangari (1995) investigated risk management perceptions and trends in U.S. construction industry by a questionnaire study. About risk management trends, Simister (1994) reported results of a survey aimed to identify perceptions of people on the benefits of risk management and utilisation rate of different risk assessment techniques in UK. Moreover, there are a number of studies about certain risk categories which are hard to define and measure in construction such as political risk (Ashley and Bonner 1987) and cultural risk (Levitt et al. 2004). Application of risk identification and analysis techniques The research under this category is comprised of application of different techniques of risk identification and analysis in construction projects. Researchers demonstrated how the risk management process may be carried out more systematically and efficiently by the use of different techniques. Applicability of various risk assessment techniques has been demonstrated by many researchers. Influence diagramming method for political risk assessment (Ashley and Bonner 1987), cross impact analysis for international risk assessment (Han and Diekmann 2001) and fuzzy event tree analysis for identification of events that may cause failures in underground construction projects (Choi et al. 2004) fall into this category. Also, quantitative risk analysis techniques may be categorised into three groups; probabilistic techniques, fuzzy sets and multi-attribute rating technique. Applications of probabilistic risk analysis techniques, particularly Monte Carlo Simulation (e.g. Bennett and Ormerod 1984; Tummala and Burchett 1999; Ozdogan and Birgonul 2000; Nasir et al. 2003) are widely seen in literature as well as research on shortcomings of and difficulties in implementing Monte Carlo Simulation (e.g. Beeston 1986). Also, several research studies exist in RM literature which applied fuzzy set theory to different decision making problems. For example, Kangari (1988) developed an integrated knowledge- based system (Expert-Risk) for risk management using fuzzy sets, Paek et al. (1993) used fuzzy sets for assessment of bidding prices, Carr and Tah (2001) proposed a software prototype for project risk assessment based on fuzzy logic. There are also applications of multi-attribute rating technique such as Analytical Hierarchy Process (AHP) application of Hastak and Shaked (2000) for international construction risk assessment. Risk rating by multiplying the probability with severity/impact of each identified risk factor and adding them up to find an overall risk score has been utilised  by many researchers (e.g. Jannadi and Almishari 2003; AbouRizk and Er 2004) as an effective and simple risk analysis tool in different projects. As well as specific applications, necessary software are also developed by researchers to facilitate application of the proposed techniques. ERIC-S for schedule risk analysis by Nasir et al. (2003) and RAM by Jannadi and Almishari (2003) for quantification of hazard risk are examples for these software. Integrated risk management support tools Although, there are numerous models/software that support individual phases of RM, the number of support tools that are integrated with other project management functions, which can be used during the whole project life cycle, and which can support all phases of RM is rather low. Risk Management Support System developed   Dikmen, Birgonul and Arikan 1148  by Aleshin (2001) for international projects in Russia; a generic model, IFE (Integrated Facility Engineering) designed by Jaafari (2001); and the software  prototype developed by Carr and Tah (2001) may be listed among potential integrated support systems. Moreover, in Table 1, some of the commercial software used to support risk management process are listed. It is evident from Table 1 that, there are also limited number of software which may provide a full support for an integrated risk management system. As a result, it can be claimed that, literature is very rich in conceptual frameworks to overcome the informality of risk management efforts. However, risk management  paradigms exist as methodologies rather than systems which can fully support the RM  process. The existing risk management support tools are usually based on quantitative risk assessment and analysis whereas the other phases are carried out external to the software. Table 1 : Some of the commercial RM software Tool Developer Where it can be used Which analysis techniques are used Which RM activities are supported Predict!Risk Controller Risk Decisions Construction of risk registers, integration of risk info with WBS, risk monitoring Risk identification and monitoring Risk Radar Software Program Managers Network Risk identification and  prioritisation Risk rating Risk identification and monitoring RiskID Pro KLCI Risk identification, monitoring impact of different mitigation  plans, risk reporting Risk identification and monitoring @Risk Palisade Europe Project cost/schedule risk estimation Monte Carlo Simulation Risk analysis ACE/RI$K ACEIT Cost/schedule risk analysis and technical risk assessment Latin Hypercube sampling Risk analysis CRIMS Expert choice Comparison of alternatives according to preset criteria Analytical Hierarchy Process Risk analysis Decision Pro Vanguard Software Setting up a project model for scenario  building Monte Carlo Simulation, Decision Tree Risk analysis Crystal Ball Decisioneering Probabilistic modelling of project variables, estimation of cost, time etc. Monte Carlo Simulation, sensitivity testing Risk analysis iDecide Decisive tools Construction of project models, risk assessment Monte Carlo Simulation, influence diagramming Risk analysis Monte Carlo Primavera Modelling project variables with  probability distributions, integrated with various planning software Monte Carlo simulation Risk analysis
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