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p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Industrial Engineering, Technology & Operations Management
Charles Onyeka Nwamekwe
Department of Industrial and Production Engineering, Faculty of Engineering, Nnamdi Azikiwe University, P.M.B. 5025, Awka, Nigeria
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Application of Machine Learning in Predicting Emergency Obstetric Cases in Sub-Saharan Africa: An Early Appraisal Nkemakonam Chidiebube Igbokwe; Charles Onyeka Nwamekwe
International Journal of Industrial Engineering, Technology & Operations Management Vol. 3 No. 1 (2025): June 2025
Publisher : Indonesia Academia Research Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62157/ijietom.v3i1.78

Abstract

This study investigates the effectiveness of machine learning (ML) in predicting emergency obstetric emergencies in Sub-Saharan Africa to improve maternal health outcomes. By examining the relevant literature, the study highlights issues that impede efficient decision-making and interventions, such as a lack of high-quality healthcare data. While machine learning models such as logistic regression, decision trees, support vector machines, neural networks, and random forests can achieve high accuracy in controlled environments, they face practical challenges, including inconsistent data quality, limited access to technology, and a shortage of trained personnel. For ML to be implemented equitably, ethical factors such as algorithmic bias and data privacy are essential. The transformative potential of machine learning in emergency obstetric care is highlighted by its benefits in early detection, individualized care, resource management, and data-driven decision-making. To fully reap these advantages, however, implementation issues and data quality must be resolved. The rapid expansion of biomedical data calls for innovative approaches to help healthcare professionals effectively analyse large datasets and reach well-informed conclusions. To maximize resource allocation, enhance patient care, and continually improve clinical outcomes, future research should focus on developing novel machine learning algorithms, improving data integration and interoperability, and fostering a data-driven culture.
Lean Manufacturing Principles in the Design and Production of Social Robots Charles Onyeka Nwamekwe; Emeka Celestine Nwabunwanne; Fredrick Nnaemeka Okeagu; Chukwuma Godfrey Ono
International Journal of Industrial Engineering, Technology & Operations Management Vol. 3 No. 1 (2025): June 2025
Publisher : Indonesia Academia Research Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62157/ijietom.v3i1.79

Abstract

The integration of Lean Manufacturing principles in the design and production of social robots represents a pivotal advancement in the robotics industry, addressing the dual challenges of efficiency and sustainability. This paper explores the application of core Lean concepts, including waste reduction, continuous improvement (Kaizen), and process optimization, to streamline production workflows and enhance the scalability of social robots. A comprehensive review of methodologies such as Value Stream Mapping (VSM), Kanban, and Total Quality Management (TQM) illustrates their potential to minimize waste, improve quality, and optimize resource utilization. Case studies highlight successful implementations, showcasing tangible benefits such as reduced assembly times, lower inventory costs, and fewer defects. Furthermore, the paper delves into the unique challenges of producing social robots, including high customization requirements, precision demands, and cost constraints, and offers tailored Lean solutions to overcome these hurdles. Applications of Lean principles in service industries, including healthcare, education, and hospitality, are discussed, emphasizing their role in fostering innovation, enhancing customer satisfaction, and contributing to sustainability. The research also addresses limitations, including resistance to change and scalability issues, proposing future directions that leverage digital transformation and hybrid methodologies to advance Lean frameworks for the robotics sector. By synthesizing insights from academic literature and industry practices, this paper underscores the transformative potential of Lean Manufacturing in the design and production of social robots, offering a roadmap for achieving operational excellence and sustainability in this rapidly evolving field.
Co-Authors Chukwuma Godfrey Ono Emeka Celestine Nwabunwanne Fredrick Nnaemeka Okeagu Nkemakonam Chidiebube Igbokwe