Onyekachi Marcel Egwuagu1 and Charles Chikwendu Okpala2
1 Associate Professor, Department of Mechatronics Engineering, Enugu State University of Science and Technology, Enugu, Nigeria.
2 Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Nigeria.
Abstract
The transition towards sustainable manufacturing requires production systems that simultaneously deliver high operational performance, material circularity, and deep decarbonization. This study presents a data-driven and multidisciplinary engineering framework for the Factory of the Future that integrates real-time industrial data, digital twins, dynamic life cycle assessment, and multi-objective optimization to operationalize circular economy and net-zero objectives at the factory level. Unlike conventional approaches that treat sustainability as a retrospective reporting function, the proposed framework embeds lifecycle environmental metrics directly into production planning and control. The framework is evaluated through representative case studies in automotive components manufacturing, electronics assembly, and energy-intensive process manufacturing. The results reveal consistent reductions of 38–40% in lifecycle greenhouse gas emissions, 20-23% in energy intensity, and approximately 30% in virgin material input, alongside improvements of 6–8% in overall equipment effectiveness relative to baseline configurations. Additional analysis demonstrates that circular material strategies contribute up to 35% of material savings and 18-27% of total emission reductions, while sensitivity analyses confirm robustness under varying energy mixes and demand volatility. The findings provide quantitative evidence that data-driven engineering enables synergistic gains in sustainability and productivity, and thus offer a scalable blueprint for the design and operation of circular and net-zero production systems in the Factory of the Future.
Keywords: Factory of the Future, Data-driven manufacturing, Digital twins, Circular economy, Net-zero manufacturing, Life cycle assessment (LCA), Multi-objective optimization
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