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All Journal Culture education and technology research Open Access DRIVERset INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER)
Charles Onyeka Nwamekwe
Industrial/Production Engineering Department, Nnamdi Azikiwe University
Religion Humanities Education Social Sciences Other

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Resilience and Risk Management in Social Robot Systems: An Industrial Engineering Perspective Charles Onyeka Nwamekwe; Igbokwe Nkemakonam Chidiebube; Ono Chukwuma Godfrey; Nwabunwanne Emeka Celestine; Aguh Patrick Sunday
Culture education and technology research (Cetera) Vol. 2 No. 3 (2025): Vol.2 No.3 2025
Publisher : FKIP - Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ctr.v2i2.154

Abstract

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.
Resilience and Risk Management in Social Robot Systems: An Industrial Engineering Perspective Charles Onyeka Nwamekwe; Igbokwe Nkemakonam Chidiebube; Ono Chukwuma Godfrey; Nwabunwanne Emeka Celestine; Aguh Patrick Sunday
Culture education and technology research (Cetera) Vol. 2 No. 3 (2025): Vol.2 No.3 2025
Publisher : FKIP - Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ctr.v2i2.154

Abstract

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.
INTEGRATING ARTIFICIAL INTELLIGENCE AND TIME-SERIES FORECASTING FOR SMART TEXTILE PRODUCTION: TRENDS, CHALLENGES, AND OPPORTUNITIES IN THE INDUSTRY 4.0 ERA Charles Chikwendu Okpala; Ikenna Chimaobi Egwuatu-Elem; Charles Onyeka Nwamekwe
INTERNATIONAL JOURNAL OF SOCIETY REVIEWS Vol. 3 No. 2 (2025): INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER)
Publisher : Adisam Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increasing complexity of textile manufacturing in the industry 4.0 era has intensified the need for forecasting systems that can adapt to dynamic demand patterns, interconnected production networks, and heterogeneous data environments. This article provides a comprehensive review of how Artificial Intelligence (AI) and time-series forecasting techniques are being integrated to enhance operational intelligence within smart textile production. It synthesizes the strengths and limitations of classical statistical models, modern machine-learning architectures, and emerging hybrid approaches that combine linear decomposition with nonlinear learning. The review highlights how interconnected data ecosystems enabled by IoT sensors, RFID tracking, MES/ERP systems, edge–cloud architectures, and digital twins form the backbone of real-time predictive capabilities in contemporary textile factories. In examining recent research and industrial applications, the study identifies key opportunities for sustainability alignment, adaptive learning, and autonomous decision support, alongside persistent challenges related to data quality, interoperability, computational demands, and SME adoption barriers. Finally, the article outlines actionable future directions, including reinforcement-learning-driven forecasting, federated learning, lightweight edge analytics, standardized benchmarks, and sustainability-aware predictive models. By consolidating methodological advances and practical considerations, this review offers a grounded roadmap for deploying intelligent, responsive, and resilient forecasting systems within the evolving landscape of smart textile manufacturing.
Co-Authors Aguh Patrick Sunday Charles Chikwendu Okpala Igbokwe Nkemakonam Chidiebube Ikenna Chimaobi Egwuatu-Elem Nwabunwanne Emeka Celestine Ono Chukwuma Godfrey