Quality quantification is an unavoidable topic in today daily company life. In this paper, the authors review why quality quantification is critical, what are the main difficulties with the current approaches and highlight the qualimetry approach as the solution. After a state of the art on qualimetry and on quality model concept strengthened with polymorphism, the first steps of their applications to automotive embedded systems and software in Renault are showcased. The results are not only the benefits in quality quantification for complex systems, such as homogeneity, consistency and compatibility, but also the highlighted risks with the changes in versions of quality models in Automotive SPICE and how to define a derivable quality model over electronic control units and vehicle. A. The need to evaluate and quantify quality Nowadays Renault is producing automotive systems at a high cadence. These automotive systems are very complex and embed many subsystems. Evaluating and quantifying the level of quality of a system and of each subsystem is important, for different reasons exposed below. First, a company such as Renault has to comply with many standards and regulation. This is obvious when we consider transportations systems such as cars or airplanes where we have to follow functional safety standards such as ISO26262 [1], ARP4754A 5 [2] and DO-178C [3]. Therefore, properly quantifying quality will tell us if we fulfill or not those standards. Moreover, "quality quantification" covers both quality aspects (supporting the identification of the systems main characteristics) and quality models (supporting the organization of these characteristics). Quantification helps optimizing and controlling the large flow of metrics and measurements, and extracting the subset that makes most sense to Renault (or which is the most useful for Renault). We can certainly find many other good reasons why quality quantification is important. However, missing some steps in quality quantification may sometimes turn into catastrophic scenarios. We can quickly cite a few well-known examples: the issue of software update with Therac-25 causing irradiation and death of 6 patients during 1985-1987 [4], Ariane 5 explosion on its first launch on the 4 th of June 1996 [5] due to the reuse of the previous navigation system that was not aligned with the new rocket version velocity and then resulting on the loss of $370 million, on the 26 th of June 2017 Takata's bankrupt happened due to an unaddressed known bug in their airbag [6] and on 2018, Toyota recalled 2.4 million hybrid cars because of a failure in the "failsafe" driving mode linked to an uncaught software issue [7]. Through these four examples, we have four different systems with four different quality quantification contexts, and an obvious demonstration that their consequences, measured in term of people loss and / or budget, were catastrophic, thus highlighting the need to have not only a reliable and accurate quality quantification approach, but also adapted to system usage context. The quality addressed in this paper is the quality of product during its entire life cycle, including development (requirement analysis, design, implementation), maintenance and operation.