**5. Recommendations**

In acknowledgement of SME feedback, ARMS and ERC methodologies may be explored for means of better identifying the high-risk areas within the MxFACS output. By combining the MxFACS output and fatality data with bowtie models to understand the e ffectiveness of the barriers in place, it would then be possible to develop an ERC score and consequently substantiate the higher risk areas for this data. This would then allow for a maintenance-specific depiction of key risk areas akin to the work of EASA in Figure 9 [4].

Further to this, it would be advisable to create a number of bowtie models for the high-risk areas and continually maintain and update these models as time progresses. This would allow for continual monitoring of the barriers in place for higher risk areas. Additionally, the MxFACS taxonomy and database should also be continually maintained and updated to ensure relevance for new accidents and serious incidents as they evolve.

**Author Contributions:** Conceptualization, J.I. and C.T.; methodology, J.I. and C.T.; formal analysis, J.I.; data curation, J.I.; writing—original draft preparation, J.I.; writing—review and editing, J.I. and C.T.; supervision, C.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** We would like to thank all of the SMEs for their contribution by their advice and opinions; the International Federation of Airworthiness (IFA) for supporting this study and CGE Risk Management Solutions for providing access to BowTieXP. Finally, we would like to recognise the financial support Jenni received from the Royal Aeronautical Society through their Centennial Scholarship for studying the MSc course and producing this thesis as part of her research project.

**Conflicts of Interest:** The authors declare no conflict of interest.
