**1. Introduction**

Although over the years, industrial plants have improved their safety management processes, it is evident that safety systems need to be further improved [1]. This need is underlined by the many accidents that have occurred in industrial plants over recent years, arising from human causes, technical causes, or natural causes. Traditional safety management models are designed to identify negative factors and develop systems to mitigate their impact. These models allow to analyze different critical situations, but they seem ineffective for today's business needs [2]. Particularly, in modern industrial plants, only a few functions are independent of each other. Thus, analyzing them individually may not be the best model. In general, due to the complexity of the systems it is necessary to analyze all functions and tasks. In this perspective, Resilience Engineering (RE) is a useful approach to manage complex systems. This approach is a new way to think about safety and risk management [3]. Unlike the classic risk management approaches that are based on the analysis of a posteriori causes by adopting a linear cause-and-effect approach, the RE adopts a perspective that refers to the theory of complexity. RE aims to revise the analysis models to create processes that are flexible and robust. Functional Resonance Analysis Method (FRAM) proposed by Hollnagel defines complex systems through their functions and studies the interactions between these functions [4]. The main *strength* of FRAM method is based on the principle that a variation in the conditions in which an action takes place can lead to *improvements* or *worsening* that ultimately

lead to its success or failure. However, this approach leads to a *qualitative result* aimed at highlighting how multiple variables combined can change the outcome of an action in a dynamic environment. The points in favor of this method and of resilience engineering are evident, but they still pose obstacles, sometimes even technical ones to overcome.

Thus, in the present research the FRAM method is used in conjunction with Analytic Hierarchy Process (AHP) to overcome the limits of the FRAM. AHP is a well-known multi-criteria decision support technique developed in the 1970s by the Prof. Thomas L. Saaty [5]. The proposed model overcomes the qualitative limits of the resilience engineering models proposed in the literature. The AHP helps to assess the subjective probability of an event or trigger cause. Furthermore, through the integration of the AHP it allows to evaluate the strength of relationship between the variability of human performance and influence of the external environment. The preset study is a pilot research. The proposed process will be tested in other situations and industrial settings. In fact, the model is extremely flexible and can be applied in different scenario.

The rest of the paper is organized as follows. Section 2 presents a general overview on resilience engineering approach and a brief state of art. Section 3 describes the proposed model based on FRAM and AHP. Section 4 describes a real case study in a petrochemical industry and its results. Finally, in Section 5 conclusion of the proposed "model" and the future research are summarized.