## 1 Introduction
Some of these limitations include:  
* Different combinations of probability and severity of consequences may produce exactly the same value of RPN, but their hidden risk implications may be totally different. * Interdependencies among various failure modes and effects are not taken into account. * The relative importance of risk factors is not taken into consideration. * The risk factors are difficult to be precisely evaluated. In turn, due to the highly subjective nature and the lack of information, it is usually difficult to precisely measure risk parameters. A reasonable and suitable way to express these parameters is to use qualitative linguistic variables, particularly during experts' judgments to estimate the occurrence likelihood (for example: very low, low, medium, high, and very high) and to assess the consequence severity (variables as slight, minor, moderate, critical, and catastrophic). In that sense, some studies have focused on improving the methods by reducing the uncertainty and subjectivity in information utilizing fuzzy sets, evidence theory, Bayesian approach, and other techniques. This has been one of the main drivers for developing hybrid models. Fuzzy theory has proven to be effective and efficient in handling these types of uncertainties [24; 25; 26; 27; 22; 14]. Several RM frameworks are presented in the literature and a consensus exist that the main stages of SCRM involve four sequential stages: risk identification, risk assessment, risk treatment, and risk monitoring [4; 15; 28]. According to [PERSON] et al. [29], even though several studies in the SC literature have discussed RM, a limited number of studies discussed the entire management cycle of risks. Most of the studied researches are limited to some (mostly two) processes in the SCRM cycle, such as risk identification and assessment [30], risk assessment and mitigation [31], and risk identification and mitigation [32; 33]. According to [PERSON] et al. [16], the risk monitoring process has received less attention than the other processes. It is important to develop an early alert monitoring system with adaptive risk indicators for different SC types. Hence, more attention should be given to pre-SCRM and post-SCRM [16]. However, only a few number of research studies [34; 35] consider in their approaches a previous analysis of SC. For example, [PERSON] and [PERSON] [36] considered the relationship of risks with the strategic objectives of the organization, but did not include the possibility of prior process improvement. The main limitations of previous studies are (1) focus on limited stages of the RM process, and (2) ignoring SC process analysis and their alignment with strategic management as a holistic system. [PERSON] and [PERSON] [36] and [PERSON] and [PERSON] [21] suggest that SCRM can be considered as a process that supports the achievement of SC management strategic objectives. In addition, [PERSON] et al. [7] and [PERSON] et al. [28] affirm that many existing measurement models only result in local optimally and underestimate the effects of triggering risks due to the causal structure within the supply network. In the reviewed literature, several researches discuss the importance of investigating the joint impact of risk events [7; 16; 17; 37]. In relation to the above, [PERSON] et al. [27] revealed: \"Although a number of quantitative tools and techniques have already been developed for managing supply chain risks, there is a limited focus on introducing holistic frameworks that not only integrate all stages of the risk management process but also capture the cascading effects of common risk triggers. Also, existing frameworks generally focus on optimising a single objective (performance measure) without exclusively modelling the trade-off between conflicting and interdependent objectives\" [27] (p. 37). In that sense, an integrated analysis leads to effective mitigation strategies and the integration with the strategy of the organization is extremely important.