Generating project risk response strategies based on CBR: A case study
Introduction
Project execution is always accompanied by risks. For example, there may exist some risks during the execution of an engineering project, such as management risk, cost risk and so on. Therefore, it is necessary to conduct project risk management (PRM). In general, PRM includes three phases: risk identification, risk assessment and risk response (Fan, Lin, & Sheu, 2008). Risk identification refers to recognizing and documenting associated risks. Risk assessment refers to examining the identified risks, refining the description of the risks, and estimating the value of the risks. Risk response refers to generating and implementing proper strategies to prevent and control the risks. Once risks of the project have been identified and assessed, proper risk response strategies must be generated and adopted (Zou, Zhang, & Wang, 2007). So far, many studies on risk identification and assessment have been found, whereas risk response has seldom been addressed in the existing studies (Seyedhoseini, Noori, & AliHatefi, 2008). Hence, an in-depth study on risk response is necessary.
In the existing studies, the methods for generating project risk response strategies can be mainly classified into four types (Zhang & Fan, 2014): the zonal-based method (Elkjaer and Felding, 1999, Flanagan and Norman, 1993, Jordan et al., 2013, Marcelino-Sádaba et al., 2014, Miller and Lessard, 2001, Piney, 2002, Sumit, 2001), the trade-off method (Chapman and Ward, 1996, Kujawski, 2002, Pipattanapiwong and Watanabe, 2000), the work breakdown structure (WBS)-based method (Chapman, 1979, Klein et al., 1994, Seyedhoseini et al., 2009) and the optimization-model method (Ben-David and Raz, 2001, Fan et al., 2008, Hu et al., 2013, Hu et al., 2013, Kayis et al., 2007). The detailed elaborations of the above four types of methods can been seen from Zhang and Fan (2014). The four types of methods have made significant contributions to generating project risk response strategies from different perspectives. However, it can be seen that the existing methods have some limitations in practical applications. For example, the key of using the zonal-based method is to form a two-axis graph composed of multiple zones for the risks. If more than two criteria concerning the risks are considered, it will be difficult to form the graph. Likewise, the trade-off method only applies to the situation of two criteria considered. In addition, there are some limitations in the use of the optimization-model method because it is difficult to quantify some project features (e.g., project size or technical complexity) in the process of risk analysis and modeling. Moreover, it is no easy task to determine the WBS for some projects with complicated characteristics. Thus, it will be difficult to generate risk response strategies for the projects using the WBS-based method. Besides, using the WBS-based method, it is unlikely to know whether the obtained strategies are the desirable ones for risk response.
Given the limitations of the exiting methods, it is necessary to conduct further research on how to tackle project risk response problems from a new perspective. Some studies in recent years show that it is feasible to solve the decision-making problems using the case-base decision analysis methods (Amailef and Lu, 2013, Chen et al., 2008, Ma, 2012, Pla et al., 2013). Thus, to solve the project risk response problem, a way of case-base decision analysis may be considered. That is, the project manager can retrieve the available information and knowledge on risk response from case base. Then appropriate risk response strategy or strategies for the current project can be generated by analyzing and reusing the retrieved information and knowledge. As is known to all, the case-based reasoning (CBR) technique is good at solving problems by retrieving and reusing information and knowledge of the similar historical cases (Aamodt and Plaza, 1994, Abelson and Schank, 1977, Hansen et al., 1994). Over the decades, CBR was widely applied in various areas such as medicine (El-Fakdi et al., 2014, Guessoum et al., 2014, Ting et al., 2010, Zhuang et al., 2009), manufacturing industry (Kuo, 2010, Wu et al., 2008) and business (Carmona et al., 2013, Li et al., 2011), etc. It can been found that there are some studies on risk management based on CBR (Aarts, 1998, Bajo et al., 2012, Chang et al., 2010, Dingwei and Xinping, 2011, Goh and Chua, 2009, Jung et al., 1999, Kumar and Viswanadham, 2007, Li et al., 2013, Lu et al., 2013, Yao et al., 2014). For example, Kumar and Viswanadham (2007) develop a CBR-based framework of the decision support system to support the risk management of construction supply chains. Dingwei and Xinping (2011) develop an audit decision aid system based on analytical hierarchy process and CBR to assess the management fraud risk. Bajo et al. (2012) develop a CBR-based multiagent system for web-based risk management in small and medium business. Lu et al. (2013) develop a CBR system which includes a detailed case representation scheme and an automated retrieval mechanism to analyze safety risk on subway operation. Yao et al. (2014) identify the ship repair risk by analyzing causes and consequences of the risk, and propose a CBR-based method for assessing the ship repair risk. Obviously, it is a good way to apply the CBR technique to the risk management for project risk. However, little attention has been paid to problems of generating project risk response strategies in the existing studies. Especially, the study on using the CBR technique to solve the project risk response problem is seldom found. Therefore, it is necessary to investigate the CBR-based method for generating project risk response strategies.
The objective of this paper, taking risk response of the subway project in S city, China as a case problem, is to develop a CBR-based method for generating project risk response strategies. In the method, firstly, the current project risk response problem is regarded as the target case and the available historical cases are retrieved from the case base by judging whether the project risks involved in each historical case cover or are the same as those in the target case. Then, the similar historical cases are retrieved from the available historical case set by measuring similarity between each available historical case and the target case. On the basis of this, by revising the inapplicable risk response strategies involved in the similar historical cases, the candidate risk response strategy sets for the target case are set up. Further, the overall evaluation value concerning each candidate risk response strategy set is calculated. Finally, the desirable risk response strategies for the target case are generated according to the obtained overall evaluation values.
The rest of this paper is organized as follows. Section 2 formulates the case problem of the subway project risk response, along with the solution framework for generating project risk response strategies based on the CBR. To solve the case problem, Section 3 presents a CBR-based method for generating project risk response strategies. In Section 4, an empirical analysis on the case of generating risk response strategies for the subway station project in S city, China is given to illustrate the use of the proposed method. Finally, the conclusions of this study and the directions for the future research are presented in Section 5.
Section snippets
Case problem and solution framework
Subway, as a fast and convenient vehicle, has many advantages, such as low energy consumption, low pollution and less affected by weather, etc (Zhao & Hao, 2011). Thus, it has been adopted by more and more cities worldwide. In China, subway construction has stepped into an era of accelerating development. So far, the subways have been put into operation in more than ten cities and the subway constructions of many other cities are in progress (Chen, Wang, Song, & Zhao, 2013).
S city is one of the
The CBR-based method for generating project risk response strategies
Based on the above solution framework, we give a CBR-based method for generating project risk response strategies in this section. First, the case representation and resolution procedure are presented. Then, the specific description of each part of the procedure is given.
Empirical analysis
This section focuses on the case problem mentioned in Section 2. An empirical analysis of generating the risk response strategies for the Changan Road station project is given to illustrate the use of the method mentioned above. It is known that the investment amount of this project is 22.3 million CNY, the construction area is 530.11 square meter and the construction cycle is half a year. The subway station is designated as a second level station and will be constructed in the type of two
Conclusions and future works
This paper presents a CBR-based method for generating project risk response strategies using the risk response of the subway project as a background. In the method, firstly, the target case and the historical cases are represented. Then, the available historical cases are retrieved from the case base by judging whether the risks involved in each historical case cover or are the same as those in the target case. Afterwards, the similar historical cases are retrieved from the available historical
Acknowledgments
This work was partly supported by the National Science Foundation of China (Project Nos. 71271051, 71071029 and 71471032), Program for New Century Excellent Talents in University of MOE of China (Project No. NCET-11-0084) and the Research Fund for the Doctoral Program of Higher Education of China (Project No. 20130042110030).
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