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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in April 2026 (Volume 30, Issue 1) Submit manuscript

Data-driven analytics and modelling of circular supply chains for net zero manufacturing

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  • Data-driven analytics and modelling of circular supply chains for net zero manufacturing

Chinedu C. Onyeje 1, *, Adeyemi Zaheed Oshilalu 2 and Busola Fadojutimi 3

1 Department of Economics and Decision Sciences, Western Illinois University, USA.
2 Senior Research Consultant, Energhx, UK.
3 Senior Supply Chain Analyst, Elanco US Inc.
 
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 1097-1121
Article DOI: 10.30574/wjarr.2024.23.3.2752
DOI url: https://doi.org/10.30574/wjarr.2024.23.3.2752
 
Received on 30 July 2024; revised on 06 September 2024; accepted on 09 September 2024
 
This study aims to explore the application of data-driven analytics and modelling, using Convolutional Neural Networks (CNN) and MATLAB, to develop circular supply chains that support net zero manufacturing. As industries face growing pressure to reduce their environmental impact, circular supply chains, which focus on resource reuse, waste reduction, and sustainable production, are becoming essential. By integrating CNN models for data analysis and optimization, this research enhances the ability to identify inefficiencies, forecast demand, and optimize resource flows, contributing to a reduction in carbon emissions. Key findings demonstrate that circular supply chain strategies, enhanced by CNN-driven analytics, significantly reduce carbon footprints in manufacturing processes. The application of CNN, executed in MATLAB, enables advanced pattern recognition for optimizing material reuse, predicting logistical demands, and improving lifecycle management. These data-driven insights result in lower emissions, cost savings, operational efficiencies, and enhanced supply chain resilience. The implications of these findings suggest a transformative impact on the manufacturing industry. By adopting CNN-based analytics powered through MATLAB for circular supply chains, companies can achieve net zero goals while improving competitiveness. This approach fosters a shift towards sustainable manufacturing by minimizing reliance on finite resources and reducing waste, aligning the industry with global sustainability objectives.
 
Circular Supply Chains; Convolutional Neural Networks (CNN); Net Zero Manufacturing; Data-Driven Analytics; Sustainable Production
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2024-2752.pdf

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Chinedu C. Onyeje, Adeyemi Zaheed Oshilalu and Busola Fadojutimi. Data-driven analytics and modelling of circular supply chains for net zero manufacturing. World Journal of Advanced Research and Reviews, 2024, 23(3), 1097-1121. Article DOI: https://doi.org/10.30574/wjarr.2024.23.3.2752

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