Literature Review on Risk and Risk Management of Logistics Projects
3.1 Methodology Background
The logistics risk relationship is defined as the ‘participants usually building or adjusting their business relationships through cooperation, sharing, and practice to achieve a long-term relationship of cooperation and win–win situation’ (Whipple et al., 2010). Sun (2007) reports that the early research on risk dates back to the mid-14th century, when marine insurance at ports along the Mediterranean opened a prelude to human exploration (Daugherty et al., 2006). In the 19th century, Henri Fayol, the founder of French management theory, initially listed risk management as one of the important functions of business management (Hazen and Byrd, 2012). In 1931, the American Insurance Association’s insurance department advocated risk management and began to study risk management and insurance issues (Richey et al., 2010). The establishment of the New York City Brokers Association in 1932 marked the rise of risk management at that time. On August 13, 1953, General Motors’s transmission failure caused a fire, which resulted in the company to lose as much as $50 million (Barratt, 2004; Ireland and Bruce, 2000). The fire shocked the American business community and academia, making it an opportunity for the development of risk management science (Richey et al., 2010). By the 1960s, a new set of management science–risk management was formally established in the United States. Since then, risk management has grown rapidly (Daugherty, 2011; Ellinger and Richey, 2013; Stank et al., 2011).
On the basis of the argument of Cao and Zhang (2011) and Sun (2007), the theoretical study of risk management in terms of logistics projects has been developed with the formation of the international engineering construction market (Cao and Zhang, 2011;
Chen et al., 2013; Michael et al., 2018; Xu et al., 2018). As early as the Second World
６１
War, risk analysis techniques have been applied in the field of systems engineering and operations research. With the reconstruction of the Western society, especially the economic recovery in Western Europe, numerous large aerospace, hydropower, energy and transportation projects were established in Europe (Cooper et al., 1997; Ellram and Cooper, 1990; Min et al., 2005). The large investment makes project managers pay increasing attention to the issue of risk management. However, several project uncertainties still exist due to the complex project environment. How to quantitatively predict the effect of uncertainty on project risk is a great concern for managers (Michael et al., 2018; Whipple et al., 2010; Zarbakhshnia et al., 2018). Thus far, numerous project risk assessment techniques have been developed and studied by scholars, such as early project planning review techniques, subsequent sensitivity analysis and simulation techniques (Daugherty, 2011; Daugherty et al., 2006; Michael et al., 2018; Xu et al., 2018).
In an initial study (Braunscheidel and Suresh, 2009; Flynn et al., 2010; Ralston et al., 2015; Swink et al., 2007), only one effect is described and evaluated using mathematical statistics and probabilities, such as factors that influence project objectives, which are changes in time or cost (Chen et al., 2009). With the emergence of new evaluation methods, risk analysis has also developed in a comprehensive and multidimensional way. One of the earliest and most successful practical applications were the ‘Beihai Oilfield Development Project’ in Europe in the 1960s and 1970s (Creswell, 2013; Denzin and Lincoln, 1994; Glaser and Strauss, 2012; Pagell and Wu, 2009; Sabath and Fontanella, 2002). The project lasted for more than 10 years and was invested in billions of dollars completed by numerous international contracting companies. For this project, experts attempted several and different risk management methods and gained some experiences and achievements (Barratt, 2004; Cao and Zhang, 2011; Devaraj et al., 2007; Kahn and Mentzer, 1996). After decades of theoretical research and exploration and preliminary application in practice, the international academic community has reached an agreement on project risk management theory as follows (Ellinger, 2000;
Esper et al., 2010):
Project risk management is a systematic project involving all aspects of project
６２
management, including risk identification, analysis, assessment, control and decision making. Its objective is designed to reduce losses and control costs by studying and controlling the environmental uncertainty of the project (Creswell, 2013; Glaser and Strauss, 2012; Pagell and Wu, 2009; Sabath and Fontanella, 2002; Denzin and Lincoln, 1994).
All of the existing methods are based on the assumptions that have no restrictions on resources (Michael et al., 2018; Strauss and Corbin, 1998; Zarbakhshnia et al., 2018).
The focus is on mathematical analysis and analytical calculations, which simplify the correlation between the risk problem and the complexity, thereby limiting the practical application of these methods to a certain extent (Glaser and Strauss, 2012). The scientific methods of project management mainly include decision tree, probability distribution, mathematical, simulation and random network methods (Charmaz, 2001).
These methods focus on the use of advanced computational techniques to analyse the effects of various risk factors on investment decision parameters and management objectives under limited resource conditions (Rousseau et al., 2008), thereby avoiding complex mathematical analyses and helping investors make scientific and effective investment risk decisions (Denyer and Tranfield, 2009).
As discussed in Chapter 2, the risk of logistics projects depends on many issues, including environmental ones. Thus, people can provide scores on each risk and then perform the analysis. Moreover, collecting data for logistics projects is difficult. To overcome this shortcoming, Fuzzy Comprehensive Measurement Method (FCMM) is selected in this thesis.19 The logistics project risk measurement treats in this application a j |j = 1,2,…,p as an optimization variable, making it a complex nonlinear optimization problem, where a j can be profit or risk. Many studies (Denyer and Tranfield, 2009; Xu et al., 2018) solve it via the conventional optimization method; however, this method may
19In terms of risk assessment on the logistics projects, using absolute value to accurately describe the objective reality is difficult due to the complexity of evaluation factors, the hierarchy of evaluation objects and the ambiguity of evaluation criteria. A vague phenomenon often exists, and its description is often expressed in natural language, where the most important feature of natural language is its ambiguity, and using a classical mathematical model to measure the ambiguity uniformly is difficult (See https://baike.baidu.com/item/FCMM/2162444?fr=aladdin). Therefore, in this thesis, FCMM based on fuzzy sets is used to comprehensively judge the subordinate status of the risk from multiple indicators.
６３
be difficult due to nonlinear issues. Thus, this thesis proposes a risk identification and analysis method for the logistics projects based on PSO and AHP,20 where this thesis not only combines AHP and PSO, but also uses WBS together to build the risk indicator system, which is the base of PSO-AHP method, while Huang et al. (2011) only focus on the combination of AHP and PSO, and use it to maximize the value of product updating so as to extend the product life cycle. The remaining parts of this chapter focus on the introduction and detailed discussion of the two methods.