<![CDATA[The increasing integration of social robots in critical environments such as healthcare, disaster response, and public safety has heightened the need for robust resilience and risk management strategies. This paper explores resilience-building methodologies and risk analysis tools from an industrial engineering perspective to ensure the reliability and safety of social robot systems. Key aspects of resilience, including adaptability, fault tolerance, and recovery, are examined alongside challenges arising from dynamic and unpredictable environments. The paper delves into industrial engineering tools like Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) to address potential system failures and mitigate risks. FMEA is discussed as a proactive approach to identifying failure modes, analysing causes, and prioritizing risks, with case applications in healthcare robotics. FTA is presented as a deductive methodology for tracing system failures to their root causes, with examples in disaster response. The role of social robots in critical environments is also highlighted, emphasizing their application in search and rescue missions, eldercare, and public safety operations. The research identifies gaps in current frameworks for assessing resilience in social robots, particularly in dynamic environments, and emphasizes the need for adaptive and hybrid risk management frameworks. Future opportunities, such as integrating advanced technologies like AI and IoT, are proposed to enhance system resilience and reliability. This paper underscores the importance of industrial engineering principles in advancing the safe and effective deployment of social robots, contributing to improved outcomes in critical and high-stakes scenarios.]]>
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