## 2 State of the Art  
A large number of quantitative risk models can be found in the literature. These models use probabilistic functions and require a considerable amount of data, which makes them unsuitable for the analysis of innovation risks, as little or no data is available in this context [12]. Indeed, the most commonly used risk assessment and management tools rely heavily on quantitative data. This characteristic makes them useless in cases where little or no in-formation is available, as they require access to event logs to obtain stochastic information, which are difficult to obtain in innovation projects. The alternative to overcome this problem is the use of qualitative risk assessment tools that take advantage of the experience of experts to evaluate risk scenarios and corresponding failure modes [13]. However, there are a number of criticisms against these models in the literature. One example is the criticism of models based on FMEA (Failure Modes and Effects Analysis). FMEA is a qualitative risk assessment tool developed by the U.S. Army in the middle of the last century that aims to minimize potential failures in systems, processes, projects, or services [14]. It has been used in a variety of industries and research centers, e.g., NASA [15]. The FMEA approach is suitable for use in design and product development and can be divided into 3 types, namely, functional, design, and process. This framework is simple and easy to implement, which are characteristics that have encouraged its use in recent decades [16]. Despite its popularity, FMEA has some weaknesses that have led to disagreements about its applicability. Most of these disagreements have been caused by the limitations of the risk priority number function (RPN), whose shortcomings in prioritizing failure modes and its dependence on expert experience for its estimates are most often cited in the literature [10; 17; 18]. Notwithstanding these criticisms, FMEA-based models continue to be widely used in industries because of their applicability and ease of use [19]. However, the results are sometimes ambiguous and require alternative measures to reconcile the results with the logical interpretation of the risk scenario. Other criticisms of FMEA-based models include the inability to account for relative importance among risk variables; obtaining the same risk index for scenarios with different risks; difficulty in expert evaluation of risk variables; non-injective and non-surjective risk prioritization functions with duplicate values; values that never appear in the risk assessment; scales for assessing risk variables with different paradigms that make it difficult to standardize the contribution of different risk variables; the inability to effectively measure risk reduction after corrective actions; the inability to consider the interdependence of failure modes; and the inability to model the risk of complex scenarios be-cause only three risk variables are considered in these modes, which are not sufficient in certain cases [10]. The models proposed in the literature to overcome these limitations are mostly based on models of multicriteria analysis (AHP, ANP, Fuzzy TOPSIS, DEMATEL, Gray theory) and artificial intelligence (rule-based systems, DEA, Fuzzy DEA). These models are very complex and require prior knowledge of their structures and the underlying theories in each case before they can be used in practice for risk assessment [11; 20; 21; 22; 23; 24; 25]. There are also criticisms of these new models, especially those based on fuzzy theory. The large number of membership functions required by these models calls into question their applicability to real industry cases. In practice, fuzzy theory is often used to over-come consensus and ambiguity problems in the interpretation of expert opinions, but in terms of risk assessment, the level of complexity is high. The maximum number of risk variables considered in these models is still three, although in practice it is necessary to include additional variables, such as cost and impact sub-variables.