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All Journal Jurnal Teknik Indonesia (JU-TI) Journal Majelis Paspama
Chukwumuanya Emmanuel Okechukwu
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Carbon Accounting at the Shop-Floor: The Integration of Real-Time Energy Monitoring, Process Modeling and LCA for Net-Zero Targets Chukwumuanya Emmanuel Okechukwu; Okpala Charles Chikwendu; Udu Chukwudi Emeka
Jurnal Teknik Indonesia Vol. 4 No. 01 (2025): Jurnal Teknik Indonesia (JU-TI) 2025
Publisher : SEAN Institute

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Abstract

Achieving net-zero emissions in manufacturing requires operational-level methods that are capable of capturing energy use and carbon intensity with high resolution. Traditional carbon accounting and Life Cycle Assessment (LCA) approaches often lack the temporal granularity required to guide shop-floor decisions. This study introduces a framework that integrates real-time energy monitoring, process modeling, and dynamic LCA to support decarbonization strategies in production environments. The framework was applied to three case studies: CNC machining, injection molding, and additive manufacturing. Results showed that non-productive energy accounted for 18–35% of total consumption, but targeted optimization reduced energy use by 12–23% and emissions by 10–23%. Dynamic LCA improved accuracy, lowering uncertainty by 14–16% compared to static methods. These findings demonstrate that shop-floor-focused carbon accounting can directly contribute to net-zero targets by linking real-time data with sustainability outcomes. The framework not only provides immediate efficiency gains, but also advances Industry 4.0 by embedding carbon intelligence into digital manufacturing systems. Future research should extend validation to energy-intensive sectors and explore integration with digital twins for comprehensive decision support
Ergonomics-Aware Scheduling: Biomechanical Models with Production Planning Integration for Musculoskeletal Risk Reduction Chukwumuanya Emmanuel Okechukwu; Okpala Charles Chikwendu; Onukwuli Somto Kenneth
Journal Majelis Paspama Vol. 3 No. 01 (2025): Journal Majelis Paspama, January 2025
Publisher : Journal Majelis Paspama

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Work-related Musculoskeletal Disorders (MSDs) remain a significant challenge in industrial environments, as they contribute to lost productivity, high compensation costs, and reduced workforce well-being. Traditional production scheduling practices often prioritize throughput and efficiency, overlooking ergonomic considerations. This study proposes an ergonomics-aware scheduling framework that integrates biomechanical modeling with production planning to reduce MSD risks while sustaining operational performance. Using case studies in an assembly line, a machining workshop, and a packaging section, the framework applied biomechanical risk models to inform scheduling algorithms. The results showed substantial reductions in both peak and cumulative physical loads which are 23% and 19% in assembly, 28% and 21% in machining, and 31% and 26% in packaging, while maintaining productivity with minimal trade-offs. Qualitative benefits included improved inclusivity, greater adaptability to demand fluctuations, and enhanced worker-task matching. The discussion highlights the implications for industry, and emphasize how ergonomics-aware scheduling supports sustainable productivity, lowers injury risks, and also aligns with Industry 4.0 through real-time monitoring and digital twins. The limitations include simplified biomechanical models, limited case study scope, and data requirements. Future research should explore machine learning integration, broader validation across sectors, and longitudinal studies on long-term organizational outcomes. Overall, ergonomics-aware scheduling provides a promising pathway towards the designing of production systems that are not only efficient, but also safe, inclusive, and sustainable.
Co-Authors Okpala Charles Chikwendu Onukwuli Somto Kenneth Udu Chukwudi Emeka