# Resilience Analysis Grid-Rasch Rating Scale Model for Measuring Organizational Resilience Potential
## 1 Introduction  
Managing complexity within sociotechnical systems is essential for effectively implementing occupational safety, where adaptive and resilient systems are needed to handle dynamic, emergent challenges [1, 2, 3, 4]. Ergonomics and human factors experts, safety scientists, as well as resilience engineers are exploring research streams investigating new ways of designing methods capable of managing such challenges [5, 6, 7], most of which are AI-based [8, 9]. Therefore, to leverage digital transformation for enhanced occupational health and safety (OHS) in manufacturing, AI can support critical decision-making processes, which are essential for enhancing both safety protocols and effective risk management in high-stakes work environments and for managing information [10, 11]. Integrating AI-driven tools within the Safety-II paradigm offers a promising pathway for enhancing OHS management in sociotechnical systems [12, 13]. Safety-II represents a novel approach to OHS management [14]. It is not solely concerned with the prevention of accidents;rather, it also seeks to comprehend and promote the positive aspects of organizational functioning [2]. In contrast to the traditional Safety-I approach, which is predicated on the analysis of errors and non-conformities, Safety-II seeks to comprehend the manner in which routine operational procedures contribute to an organization's success and resilience [15]. Even though it has not been free from fierce criticism [16], Safety-II signifies a fundamental transformation in the conceptualization of safety management, moving away from a reactive stance centered on incident avoidance toward a proactive orientation that fosters the enhancement in effective organizational practices and adaptive ability [17; 18]. By shifting the focus from merely preventing accidents to understanding and supporting successful work practices, AI can help monitor, analyze, and predict the patterns that contribute to both safe and effective operations [19; 20]. This proactive approach not only strengthens resilience within dynamic work environments but also aids in adapting safety protocols to real-time information and emergent challenges, ultimately fostering a safer and more adaptive workplace [21; 22; 23]. Safety-II and resilience engineering gave rise to the question of how resilience can be measured [24; 25; 26]. The Resilience Analysis Grid (RAG), in its original form, provides four sets of questions that can be tailored by any organization, given that there is no one questionnaire solution that is universally applicable [27; 28]. The questions are based on the principles of resilience engineering [29; 30]. These questions assist in the identification of resilience potential [31]. The questionnaire, when specifically tailored, offers insights into the organization's potential resilience in OHS. Though the RAG is an off-the-shelf tool, some complications make its implementation impractical. For example, it is difficult to assess the weights to be assigned to the items in specific questionnaires. To overcome this limitation, a specific approach was developed [32; 33]. These two articles present an analytical framework for assessing organizational resilience through the integration of RAG with [PERSON]'s analytic hierarchy process (AHP). The objective of the RAG-AHP is to provide an objective method for measuring resilience potential. Specifically, the AHP is employed in the second phase of the RAG, facilitating a systematic approach to the identification and assignment of the weights of the individual items of the four cornerstones of resilience. This hierarchy-based approach facilitates the discernment of the relative importance of each factor and the effective synthesis of expert judgments.