## 1 Introduction  
In a global economy, the competitiveness of companies is a key factor for their sustainable growth. In many cases, competitiveness is based on the anticipation of new businesses, products, and services with a strong innovation component, which is a crucial point for a promising market position [1]. With the goal of quickly bringing competitive offerings to market to gain market positions, many companies struggle to conduct adequate risk analyses, which in many cases has a negative impact, both financially and on brand image. There are numerous examples of this, from the recurring recalls in the automotive industry to the dangers associated with the use of cell phones, where the explosion of the corresponding batteries endangered human lives [2; 3; 4]. The risk associated with innovation has somewhat slowed down the industry's innovation initiatives. This is mainly because risk assessment and management models lack important features needed in practice, such as ease of use, completeness, a low level of theoretical knowledge, and applicability to cases with limited information [5]. The existing models in the literature are either very simple and provide few practical results, or they are very complex and usually developed for specific cases. Therefore, it is necessary to develop risk management tools that correspond to the reality of the company [6]. In fact, innovation is a key factor for the economic growth of companies and organizations and promotes the creation of wealth and social balance. However, a product, an idea, a method, or a service is only innovative if it is proven in practice. In this sense, a high degree of uncertainty is associated with innovation projects and their corresponding products [7]. To circumvent this uncertainty, risk assessment and risk management tools are used to support decision making under uncertainty. These tools can be divided into three categories, namely, quantitative, qualitative, and mixed risk models, which include quantitative and qualitative approaches [8]. The risk method most commonly used in practice in connection with innovations is Failure Modes and Effects Analysis. It is a qualitative method that can be easily adapted to quantitative data as needed. It is easy to learn and apply in practice. Because of these characteristics, it is very popular in a wide range of industries. It is a method with some limitations, due to the properties of its failure mode prioritization function, called the risk priority number (RPN). There are a number of works in the literature that aim to circumvent these limitations, especially for non-injective and non-surjective problems [9; 10; 11]. However, at a conceptual level, there is another limitation of FMEA related to the detectability variable used in the RPN function. In its original design, the detectability variable is evaluated qualitatively, while taking into account the existing control methods in the risk scenario. It aims to assess the probability of detecting a particular failure mode before it occurs. However, this approach ignores an important aspect, namely, the ability to mitigate or even eliminate a particular failure mode once it has been detected. To the best of the authors' knowledge, this issue has never been addressed, although it clearly has implications for failure mode prioritization. The assessment of risk mitigation capability is not found in the original version of the FMEA model nor in its more recent variants. However, there is a need to develop a methodology that allows FMEA to incorporate the ability to mitigate or eliminate failure modes when they are discovered before they occur. With this in mind, this paper addresses this research question in depth and proposes a new FMEA-based risk model that incorporates the ability to mitigate a particular failure mode into the prioritization of failure modes.