This is a review paper of studies that have employed the functional resonance analysis method (FRAM). FRAM is a relatively new systemic method for modeling and analyzing complex socio‐technical systems. This review aims to address the following research questions: (a) Why is FRAM used? (b) To what domains has FRAM been applied? (c) What are the appropriate data collection approaches in practice? (d) What are the deficiencies of FRAM? A review of 52 FRAM‐related studies published between 2010 and 2020 revealed that FRAM‐based models can be used as a basis for improving safety management, accident/incident investigation, hazard identification/risk management, and complexity management in complex socio‐technical systems. The outcomes also showed that healthcare was the most common domain that employed FRAM (31% of the investigated studies). The results of exploring data collection methods indicated a mixed method (interview, focus group, observation) was employed in 52% of the analyzed studies, and the accident investigation report was the most popular approach in aviation‐related studies. An investigation of the deficiencies of the FRAM showed that it should be upgraded by exploiting supplementary methods to enhance its analytical and computational capacity to help risk analysts and safety managers in complex socio‐technical systems.

The main purpose of this study was to model and analyze hospital to home transition processes of frail older adults in order to identify the challenges within this process. A multi-phase, multi-sited and mixed methods design was utilized, in which, Phase 1 included collecting semi-structured interviews and focus group data, and Phase 2 consisted of six patient/caregiver dyad prospective case studies. This study was conducted in three hospitals in three cities in a single province in Canada. The Functional Resonance Analysis Method (FRAM) was employed to model daily operations of the transition process. The perspectives of both healthcare providers and patients/caregivers were used to build the FRAM model. The transition model was then tested using a customized version of the FRAM. The six patient/caregiver cases were used in the process of testing the FRAM model. The results of building the FRAM model showed that five categories of functions contributed to the transition model, including admission, assessment, synthesis, decision-making, and readmission. The outcomes of using the customized version of the FRAM revealed challenges affecting the transition process including waitlists for geriatric units, team-based care, lack of a discharge planner, financial concerns, and follow-up plans. The findings of this study could assist managers and other decision makers to improve the transition processes of frail older adults by addressing these challenges. The FRAM method employed in this study can be applied widely to identify work practices that are more or less successful, so that procedures and practices can be adapted to nudge healthcare processes towards paths that will yield better outcomes.

This research investigated the effects of safety climate and psychosocial safety climate factors on safety performance of employees in a process industry. Operators and supervisors of control rooms participated in this study, and the collected data were analyzed by multi-criteria decision-making (MCDM) approaches. The entropy approach was employed to prioritize safety climate and psychosocial safety climate factors. Moreover, the technique for order preference by similarity to an ideal solution (TOPSIS) got applied to rank the alternatives. The findings obtained by the entropy approach showed that “rule breaking” (among safety climate factors) and “organizational communication” (among psychosocial safety climate factors) had the greatest influence on safety performance of employees, respectively. TOPSIS results revealed that supervisors experienced a greater degree of safety performance than operators. The findings of this study demonstrated that safety professionals should consider both safety climate and psychosocial safety climate factors to promote safety performance in high-hazard process facilities.

Functional Resonance Analysis Method (FRAM) is a function-based approach to model complex socio-technical systems and to manage variability. The current FRAM related tools are unable to capture qualitative and quantitative characteristics of variability as well as temporal variations. This study presents in detail a dynamic FRAM-based tool, which is called DynaFRAM. It is introduced to address the variability-related deficiencies of the FRAM related tools. It aims to capture variability in complex operations. It is a dynamic tool developed to capture time related variations in complex operations. This increases the attractiveness of the DynaFRAM for complex operations where specialists and practitioners make decisions in complicated situations. The ability of the DynaFRAM is demonstrated by examining a healthcare related case study. Although the ability of the DynaFRAM is assessed through capturing variations in healthcare operations, it can be applied to other domains in a similar manner. • The DynaFRAM is a dynamic FRAM-based tool. • It is able to captures different characteristics of variability. • It facilitates understanding and analysis of variability in complex operations.