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All Journal Letters in Information Technology Education (LITE) International Journal of Industrial Engineering, Technology & Operations Management Siber International Journal of Digital Business
Nwamekwe, Charles Onyeka
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Supply Chain Risk Management: Leveraging AI for Risk Identification, Mitigation, and Resilience Planning Nwamekwe, Charles Onyeka; Igbokwe , Nkemakonam Chidiebubea
International Journal of Industrial Engineering, Technology & Operations Management Vol. 2 No. 2 (2024): December 2024
Publisher : Indonesia Academia Research Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62157/ijietom.v2i2.38

Abstract

This study explores the critical role of Supply Chain Risk Management (SCRM) in today's interconnected and dynamic global economy, focusing on leveraging Artificial Intelligence (AI) for risk identification, mitigation, and resilience planning. As supply chains face increasing vulnerabilities due to geopolitical tensions, natural disasters, and technological disruptions, traditional risk-management approaches have proven insufficient in addressing these challenges. This paper comprehensively analyses how AI, through predictive analytics, machine learning, and autonomous systems, transforms SCRM by enabling real-time risk detection and response capabilities. The study also examines AI applications across various industries, including manufacturing, retail, and logistics, showcasing its potential in optimizing operational efficiency, enhancing supply chain visibility, and improving decision-making processes. Furthermore, the paper highlights the benefits and limitations of integrating AI with emerging technologies such as IoT and blockchain to enhance supply chain resilience. The findings contribute to understanding AI's growing impact on global supply chain management, providing insights into future trends and practical recommendations for managers seeking to strengthen their risk management strategies.
The Role of Digital Twins in Optimizing Renewable Energy Utilization and Energy Efficiency in Manufacturing Igbokwe, Nkemakonam Chidiebube; Nwamekwe, Charles Onyeka; Ono, Chukwuma Godfrey; Nwabunwanne, Emeka Celestine; Aguh, Patrick Sunday
Siber International Journal of Digital Business (SIJDB) Vol. 1 No. 4 (2024): (SIJDB) Siber International Journal of Digital Business (April - June 2024)
Publisher : Siber Nusantara Review & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/sijdb.v1i4.262

Abstract

Digital twin (DT) technology is revolutionizing manufacturing by bridging the gap between physical and virtual environments, enabling real-time monitoring, simulation, and optimization of processes. This paper explores the pivotal role of DTs in enhancing renewable energy utilization and energy efficiency within manufacturing ecosystems. The study delves into how DTs facilitate renewable energy forecasting, resource scheduling, and integration into manufacturing operations. Through real-time energy flow analysis, DTs aid in identifying inefficiencies, optimizing production processes, and implementing waste heat recovery systems. Specific applications in automotive and electronics manufacturing underscore the transformative impact of DTs, showcasing reductions in energy consumption and operational costs while improving resilience against energy variability. Case studies highlight successful integrations of DTs with renewable energy systems, such as photovoltaic installations, which strategically align energy-intensive activities with peak energy availability. Moreover, this research examines the challenges associated with DT adoption, including high implementation costs, data integration complexities, and organizational resistance, alongside emerging solutions tailored for scalability, particularly for small and medium-sized enterprises (SMEs). Future directions emphasize the incorporation of blockchain and artificial intelligence to enhance energy transaction security, data-driven decision-making, and operational autonomy. The paper also advocates for the development of global standards and supportive policies to foster widespread DT adoption. By showcasing both the current applications and future potential of DTs, this review underscores their critical role in driving sustainability, operational efficiency, and energy resilience in the manufacturing sector.
Digital Twin-Driven Lean Manufacturing: Optimizing Value Stream Flow Nwamekwe, Charles Onyeka; Vitalis, Ewuzie Nnamdi; Chidiebube, Igbokwe Nkemakonam; Nwabunwanne, Emeka Celestine; Ono, Chukwuma Godfrey
Letters in Information Technology Education (LITE) Vol 8, No 1 (2025)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v8i12025p1-13

Abstract

This research investigates the integration of Digital Twin (DT) technology within Lean Manufacturing frameworks to optimize value stream flow, minimize waste, and enhance real-time decision-making capabilities. By synthesizing foundational concepts of Lean Manufacturing and DT, the paper examines the layered DT architecture, covering the physical, virtual, and communication interfaces, alongside Lean tools like Kaizen, Kanban, and Just-in-Time (JIT) that facilitate continuous process improvement. Case studies, particularly in the automotive sector, demonstrate DT's ability to increase production efficiency through predictive maintenance and simulation-based scenario planning, supporting Lean's waste reduction objectives. However, the paper identifies key implementation challenges, including legacy system integration, workforce adaptation, and data interoperability. Additionally, cybersecurity and data integrity concerns are analysed to highlight essential protocols for safe DT deployment. Future research directions propose advancements like AI-powered DTs, blockchain for enhanced traceability, and edge computing for low-latency applications. Key insights from industry case studies underscore the transformative impact of DTs on production efficiency, organizational resilience, and sustainable manufacturing outcomes, positioning Digital Twin technology as a cornerstone for next-generation lean manufacturing systems
Exploring the Role of Artificial Intelligence in Enhancing Lean Manufacturing and Six Sigma for Smart Factories Nwamekwe, Charles Onyeka; Edokpia, Raphael Olumese; Igbinosa, Eboigbe Christopher
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.v3i1.61

Abstract

The integration of Artificial Intelligence (AI) into Lean Manufacturing and Six Sigma methodologies marks a transformative advancement in smart factory operations. This research explores the pivotal role of AI in enhancing efficiency, quality, and sustainability across manufacturing processes. Case studies demonstrate how AI technologies, such as predictive maintenance and real-time monitoring, have significantly reduced downtime, optimized resource utilization, and improved product quality. AI-driven analytics and machine learning models further enable proactive decision-making, aligning Lean's waste-reduction principles and Six Sigma's quality-improvement goals. However, challenges such as high implementation costs, data privacy concerns, and workforce skill gaps impede widespread adoption. This paper discusses these barriers, proposes strategies to overcome them, and highlights opportunities to integrate AI into continuous improvement frameworks. Future research directions include developing scalable AI-driven methodologies, addressing ethical considerations, and exploring the role of AI in advancing sustainable manufacturing practices. The findings underscore AI's transformative potential to redefine Lean Six Sigma paradigms, driving innovation and operational excellence in the era of Industry 4.0.
Application of Machine Learning in Predicting Emergency Obstetric Cases in Sub-Saharan Africa: An Early Appraisal Igbokwe , Nkemakonam Chidiebube; Nwamekwe, Charles Onyeka
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 Nwamekwe, Charles Onyeka; Nwabunwanne, Emeka Celestine; Okeagu, Fredrick Nnaemeka; Ono, Chukwuma Godfrey
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 Aguh, Patrick Sunday Chidiebube, Igbokwe Nkemakonam Edokpia, Raphael Olumese Igbinosa, Eboigbe Christopher Igbokwe , Nkemakonam Chidiebube Igbokwe , Nkemakonam Chidiebubea Igbokwe, Nkemakonam Chidiebube Nwabunwanne, Emeka Celestine Okeagu, Fredrick Nnaemeka Ono, Chukwuma Godfrey Vitalis, Ewuzie Nnamdi