International Journal of Industrial Engineering, Technology & Operations Management
International Journal of Industrial Engineering, Technology & Operations Management (IJIETOM) is an academic, double-blind peer-reviewed scientific journal published 2 times a year, i.e., June and December and focused on the diffusion of articles in the field of Industrial Engineering, Technology and Operations Management. IJIETOM covers theoretical, computational, and experimental investigations of all aspects of the Industrial Engineering, Technology and Operations Management areas. Areas covers (but not limited to) Computational Intelligence in Industrial Engineering, Consumer Product Design, Engineering Economy and Cost Estimation, Facilities Design and Location, Information Systems, Artificial Intelligence, Business and Process Excellence, Construction Management, Data Analytics, Decision Sciences, Energy and Resource Efficiency, Facilities Planning and Management, Healthcare Systems, Manufacturing Applications, Human Factors and Ergonomics, Industry 4.0, Inventory Management, Knowledge Management, Lean and Six Sigma, Logistics, Transport and Traffic Management, Modeling and Simulation, Operations Research, Production Planning and Control, Quality, Reliability and Maintenance, Service Systems and Service Management, Supply Chain Management, Sustainability and Green Systems, Sustainable Manufacturing, Systems Engineering, Maintenance Engineering and Management, Materials Handling, Performance Analysis and Simulation, Production Systems Design, Planning and Control, Productivity and Business Strategies, Project Management, Technology Management and Transfer, Total Quality Management and Quality Technology, Work Measurement and Methods Engineering.
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<![CDATA[Exploring the Role of Artificial Intelligence in Enhancing Lean Manufacturing and Six Sigma for Smart Factories ]]>
<![CDATA[Nwamekwe, Charles Onyeka]]>;
<![CDATA[Edokpia, Raphael Olumese]]>;
<![CDATA[Igbinosa, Eboigbe Christopher]]>
<![CDATA[ International Journal of Industrial Engineering, Technology & Operations Management Vol. 3 No. 1 (2025): June 2025 ]]>
Publisher : <![CDATA[Indonesia Academia Research Society ]]>
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DOI: 10.62157/ijietom.v3i1.61
<![CDATA[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.]]>
<![CDATA[Application of Machine Learning in Predicting Emergency Obstetric Cases in Sub-Saharan Africa: An Early Appraisal ]]>
<![CDATA[Igbokwe , Nkemakonam Chidiebube]]>;
<![CDATA[Nwamekwe, Charles Onyeka]]>
<![CDATA[ International Journal of Industrial Engineering, Technology & Operations Management Vol. 3 No. 1 (2025): June 2025 ]]>
Publisher : <![CDATA[Indonesia Academia Research Society ]]>
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DOI: 10.62157/ijietom.v3i1.78
<![CDATA[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.]]>
<![CDATA[Lean Manufacturing Principles in the Design and Production of Social Robots ]]>
<![CDATA[Nwamekwe, Charles Onyeka]]>;
<![CDATA[Nwabunwanne, Emeka Celestine]]>;
<![CDATA[Okeagu, Fredrick Nnaemeka]]>;
<![CDATA[Ono, Chukwuma Godfrey]]>
<![CDATA[ International Journal of Industrial Engineering, Technology & Operations Management Vol. 3 No. 1 (2025): June 2025 ]]>
Publisher : <![CDATA[Indonesia Academia Research Society ]]>
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DOI: 10.62157/ijietom.v3i1.79
<![CDATA[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.]]>
<![CDATA[Inventory Control Models in Indian SMEs: An Empirical Assessment Based on Enterprise Classification ]]>
<![CDATA[Mohite, Rohit]]>;
<![CDATA[Chaurasiya, Ravi]]>;
<![CDATA[Sharma, Sandeep]]>
<![CDATA[ International Journal of Industrial Engineering, Technology & Operations Management Vol. 3 No. 1 (2025): June 2025 ]]>
Publisher : <![CDATA[Indonesia Academia Research Society ]]>
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DOI: 10.62157/ijietom.v3i1.82
<![CDATA[Inventory control remains a cornerstone of operational efficiency in Small and Medium Enterprises (SMEs) in India, yet systematic evaluation across enterprise classifications—micro, small, and medium has been limited. This study empirically investigates the adoption and effectiveness of key inventory control models, including Economic Order Quantity (EOQ), Just-In-Time (JIT), and ABC analysis, in a cross-sectional sample of 450 SMEs across manufacturing clusters in Maharashtra, Tamil Nadu, and Gujarat. Findings indicate that microenterprises prefer heuristic and manual methods, whereas medium enterprises exhibit higher adoption rates of quantitative models such as EOQ and JIT. A chi-square analysis confirmed significant differences in model adoption across enterprise classifications. The study further identifies the primary drivers and inhibitors of model adoption, including digital readiness, training levels, and working capital constraints. The implications are crucial for policymakers and practitioners aiming to tailor inventory interventions to SME sub-types. This research bridges a critical gap in inventory literature by aligning control models with real operational contexts within the SME spectrum in India.]]>
<![CDATA[Integrating Lean Six Sigma and Industry 4.0 to Enhance Manufacturing Productivity in India ]]>
<![CDATA[Kumar, Navneet]]>;
<![CDATA[Saini, Sachin]]>
<![CDATA[ International Journal of Industrial Engineering, Technology & Operations Management Vol. 3 No. 1 (2025): June 2025 ]]>
Publisher : <![CDATA[Indonesia Academia Research Society ]]>
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DOI: 10.62157/ijietom.v3i1.86
<![CDATA[Small and medium-scale manufacturing enterprises (SMEs) in India face increasing competitive pressure due to rising material costs, stringent quality standards, and limited operational resources. Enhancing productivity while maintaining cost efficiency and delivery reliability remains a significant challenge. This study investigates the impact of integrating Lean Six Sigma (LSS), Total Productive Maintenance (TPM), selective Industry 4.0 technologies, and energy management practices to improve manufacturing performance in an Indian transformer manufacturing SME. Using a structured case study approach, the research implemented a phased Operational Excellence (OPEX) framework over a six-month period. Value Stream Mapping identified process bottlenecks and excessive non-value-added time, while Six Sigma tools addressed quality variation in the coil winding process. TPM initiatives improved equipment reliability, and a low-cost IoT vibration sensor enabled predictive maintenance. Additionally, an energy audit guided targeted efficiency improvements. The results demonstrate substantial performance gains: production lead time decreased by 28.6% (from 28 to 20 days), coil winding defects were reduced by 60% (from 8.5% to 3.4%), on-time delivery improved from 55% to 85%, Overall Equipment Effectiveness increased by 35% (from 52% to 70.2%), and energy cost per unit declined by 20%. The findings confirm that a pragmatic, integrated OPEX framework can deliver multidimensional productivity gains without requiring large-scale capital investment. This study contributes to the manufacturing and operations management literature by providing empirical evidence that selective Industry 4.0 adoption can effectively complement Lean Six Sigma in SME environments, offering a scalable pathway to sustainable, digitally enabled manufacturing excellence.]]>
