| Title |
An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0 |
| ID_Doc |
16724 |
| Authors |
Dabo, AA; Hosseinian-Far, A |
| Title |
An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0 |
| Year |
2023 |
| Published |
Logistics-Basel, 7, 4 |
| DOI |
10.3390/logistics7040097 |
| Abstract |
Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability. |
| Author Keywords |
Industry 5.0; logistics; circular economy; binary logistics; decision trees |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001130527900001 |
| WoS Category |
Management; Operations Research & Management Science |
| Research Area |
Business & Economics; Operations Research & Management Science |
| PDF |
https://www.mdpi.com/2305-6290/7/4/97/pdf?version=1702301841
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