IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v7y2023i4p97-d1298002.html
   My bibliography  Save this article

An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0

Author

Listed:
  • Al-Amin Abba Dabo

    (Department of Business Systems & Operations, University of Northampton, Northampton NN1 5PH, UK)

  • Amin Hosseinian-Far

    (Department of Business Systems & Operations, University of Northampton, Northampton NN1 5PH, UK)

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.

Suggested Citation

  • Al-Amin Abba Dabo & Amin Hosseinian-Far, 2023. "An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0," Logistics, MDPI, vol. 7(4), pages 1-26, December.
    • Handle: RePEc:gam:jlogis:v:7:y:2023:i:4:p:97-:d:1298002
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/7/4/97/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/7/4/97/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Simona Bolis & Rico Maggi, 2003. "Logistics Strategy and Transport Service Choices: An Adaptive Stated Preference Experiment," Growth and Change, Wiley Blackwell, vol. 34(4), pages 490-504, September.
    2. Rui Li & Yali Chen & Jinzhao Song & Ming Li & Yu Yu, 2023. "Multi-Objective Optimization Method of Industrial Workshop Layout from the Perspective of Low Carbon," Sustainability, MDPI, vol. 15(16), pages 1-23, August.
    3. L. Lombardo & M. Cama & C. Conoscenti & M. Märker & E. Rotigliano, 2015. "Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messi," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(3), pages 1621-1648, December.
    4. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    5. Ziyuan Liu & Zhi Li & Weiming Chen & Yunpu Zhao & Hanxun Yue & Zhenzhen Wu, 2020. "Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune–Ant Colony Algorithm," IJERPH, MDPI, vol. 17(16), pages 1-18, August.
    6. Daugherty, Patricia J. & Richey, R. Glenn & Genchev, Stefan E. & Chen, Haozhe, 2005. "Reverse logistics: superior performance through focused resource commitments to information technology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(2), pages 77-92, March.
    7. Zhiguo Wang & Lufei Huang & Cici Xiao He, 2021. "A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 785-812, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dangxing Chen & Weicheng Ye & Jiahui Ye, 2022. "Interpretable Selective Learning in Credit Risk," Papers 2209.10127, arXiv.org.
    2. Rahman, Shams & Subramanian, Nachiappan, 2012. "Factors for implementing end-of-life computer recycling operations in reverse supply chains," International Journal of Production Economics, Elsevier, vol. 140(1), pages 239-248.
    3. Gunasekaran, Angappa & Subramanian, Nachiappan & Papadopoulos, Thanos, 2017. "Information technology for competitive advantage within logistics and supply chains: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 99(C), pages 14-33.
    4. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    5. John Martin & Sona Taheri & Mali Abdollahian, 2024. "Optimizing Ensemble Learning to Reduce Misclassification Costs in Credit Risk Scorecards," Mathematics, MDPI, vol. 12(6), pages 1, March.
    6. Shruti Sachdeva & Tarunpreet Bhatia & A. K. Verma, 2018. "GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1399-1418, July.
    7. Yang Liu & Fei Huang & Lili Ma & Qingguo Zeng & Jiale Shi, 2024. "Credit scoring prediction leveraging interpretable ensemble learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 286-308, March.
    8. Peihua Fu & Bailu Jing & Tinggui Chen & Jianjun Yang & Guodong Cong, 2020. "Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions," IJERPH, MDPI, vol. 17(18), pages 1-29, September.
    9. Feo, María & Espino, Raquel & García, Leandro, 2011. "An stated preference analysis of Spanish freight forwarders modal choice on the south-west Europe Motorway of the Sea," Transport Policy, Elsevier, vol. 18(1), pages 60-67, January.
    10. Valerio Gatta & Edoardo Marcucci, 2013. "Ex-Post Implications Of Ex-Ante Data Acquisition Strategies In Multiagent-Type Urban Freight Policy Evaluation," Working Papers 0613, CREI Università degli Studi Roma Tre, revised 2013.
    11. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    12. Shruti Sachdeva & Bijendra Kumar, 2020. "A Comparative Study between Frequency Ratio Model and Gradient Boosted Decision Trees with Greedy Dimensionality Reduction in Groundwater Potential Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4593-4615, December.
    13. Feo-Valero, María & Arencibia, Ana Isabel & Román, Concepción, 2016. "Analyzing discrepancies between willingness to pay and willingness to accept for freight transport attributes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 151-164.
    14. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    15. Zhaojun Yang & Jun Sun & Yali Zhang & Ying Wang, 2018. "Peas and carrots just because they are green? Operational fit between green supply chain management and green information system," Information Systems Frontiers, Springer, vol. 20(3), pages 627-645, June.
    16. Ilkka Ritola & Harold Krikke & Marjolein C.J. Caniëls, 2020. "Learning from Returned Products in a Closed Loop Supply Chain: A Systematic Literature Review," Logistics, MDPI, vol. 4(2), pages 1-13, April.
    17. Masiero, Lorenzo & Hensher, David A., 2010. "Analyzing loss aversion and diminishing sensitivity in a freight transport stated choice experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 349-358, June.
    18. Das, Kanchan & Chowdhury, Abdul H., 2012. "Designing a reverse logistics network for optimal collection, recovery and quality-based product-mix planning," International Journal of Production Economics, Elsevier, vol. 135(1), pages 209-221.
    19. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).
    20. Dangxing Chen & Weicheng Ye, 2022. "Generalized Gloves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance," Papers 2209.10082, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlogis:v:7:y:2023:i:4:p:97-:d:1298002. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.