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The RFID-Enabled Reverse Logistics Optimization Model (RE-RLOM): A DFSS-Based Solution for Sustainable Circular Supply Chains

Grace Omotunde Osho Julius Olatunde Omisola Joseph Oluwasegun Shiyanbola

Subject area: Science,Engineering and Technology  ·  Area of research: RFID

Abstract

The RFID-enabled reverse logistics optimization model (RE-RLOM) is a novel framework designed to enhance the efficiency and sustainability of reverse logistics within circular supply chains. By integrating radio frequency identification (RFID) technology with a design for six sigma (DFSS) approach, RE-RLOM optimizes the reverse flow of products, materials, and waste, contributing to the creation of closed-loop supply chains. Reverse logistics, which involves the return, repair, recycling, or disposal of products, is a crucial aspect of achieving sustainability in modern supply chains. However, challenges such as inefficiencies in tracking, handling returns, and managing product lifecycle contribute to operational waste and increased environmental impact. RE-RLOM leverages RFID's real-time tracking capabilities to enhance visibility and control over the reverse logistics process. This technology allows for seamless monitoring of product movements, reducing errors, improving decision-making, and increasing the efficiency of product recovery, recycling, and reuse. The DFSS methodology ensures that each phase of the reverse logistics process, from identifying customer needs to verifying process optimization, is systematically designed to minimize waste, reduce costs, and improve overall operational efficiency. Through the integration of RFID and DFSS, RE-RLOM supports the transition to more sustainable circular supply chains by improving material flow, promoting product lifecycle extension, and enhancing resource recovery. This explores the key components of the RE-RLOM, its application in various industries, and its potential to address the challenges faced by traditional reverse logistics systems. Furthermore, it discusses the benefits, limitations, and future research opportunities for optimizing reverse logistics and supporting the global shift toward a circular economy.

Keywords

RFID-enabled, Reverse logistics optimization model (RE-RLOM), DFSS-based, Sustainable circular, Supply chains

References

[1] Adebisi, B., Aigbedion, E., Ayorinde, O. B., & Onukwulu, E. C. (2021). A Conceptual Model for Predictive Asset Integrity Management Using Data Analytics to Enhance Maintenance and Reliability in Oil & Gas Operations.

[2] Adepoju, P., Austin-Gabriel, B., Hussain, Y., Ige, B., Amoo, O., & Adeoye, N. (2021). Advancing zero trust architecture with AI and data science for.

[3] Afolabi, S. O., & Akinsooto, O. (2021). Theoretical framework for dynamic mechanical analysis in material selection for high-performance engineering applications. Noûs, 3.

[4] Alonge, E. O., Eyo-Udo, N. L., Ubanadu, B. C., Daraojimba, A. I., Balogun, E. D., & Ogunsola, K. O. (2021). Enhancing Data Security with Machine Learning: A Study on Fraud Detection Algorithms.

[5] BALOGUN, E. D., OGUNSOLA, K. O., & SAMUEL, A. (2021). A Cloud-Based Data Warehousing Framework for Real-Time Business Intelligence and Decision-Making Optimization.

[6] Bodkhe, U., Tanwar, S., Parekh, K., Khanpara, P., Tyagi, S., Kumar, N., & Alazab, M. (2020). Blockchain for industry 4.0: A comprehensive review. Ieee Access, 8, 79764-79800.

[7] Chen, J., Wang, H., & Zhong, R. Y. (2021). A supply chain disruption recovery strategy considering product change under COVID-19. Journal of Manufacturing Systems, 60, 920-927.

[8] Clauson, K. A., Breeden, E. A., Davidson, C., & Mackey, T. K. (2018). Leveraging Blockchain Technology to Enhance Supply Chain Management in Healthcare:: An exploration of challenges and opportunities in the health supply chain. Blockchain in healthcare today.

[9] Elujide, I., Fashoto, S. G., Fashoto, B., Mbunge, E., Folorunso, S. O., & Olamijuwon, J. O. (2021). Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases. Informatics in Medicine Unlocked, 23, 100545.

[10] Elumilade, O. O., Ogundeji, I. A., Achumie, G. O., Omokhoa, H. E., & Omowole, B. M. (2021). Enhancing fraud detection and forensic auditing through data-driven techniques for financial integrity and security. Journal of Advanced Education and Sciences, 1(2), 55-63.

[11] Ewim, C. P.-M., Omokhoa, H. E., Ogundeji, I. A., & Ibeh, A. I. (2021). Future of Work in Banking: Adapting Workforce Skills to Digital Transformation Challenges. Future, 2(1).

[12] EZEANOCHIE, C. C., AFOLABI, S. O., & AKINSOOTO, O. (2021). A Conceptual Model for Industry 4.0 Integration to Drive Digital Transformation in Renewable Energy Manufacturing.

[13] Griffiths, H., Grisoni, L., Manfredi, S., Still, A., & Tzanakou, C. (2020). The Spinout Journey: Barriers and Enablers to Gender Inclusive Innovation: Oxford: Oxford Brookes University.

[14] Gunasekaran, A., Subramanian, N., & Rahman, S. (2015). Supply chain resilience: role of complexities and strategies. In (Vol. 53, pp. 6809-6819): Taylor & Francis.

[15] Hassan, Y. G., Collins, A., Babatunde, G. O., Alabi, A. A., & Mustapha, S. D. (2021). AI-driven intrusion detection and threat modeling to prevent unauthorized access in smart manufacturing networks. Artificial intelligence (AI), 16.

[16] Kanyoma, K. E., Khomba, J. K., Sankhulani, E. J., & Hanif, R. (2013). Sourcing strategy and supply chain risk management in the healthcare sector: A case study of malawi's public healthcare delivery supply chain. Journal of Management and Strategy, 4(3), 16.

[17] Lund, S., DC, W., & Manyika, J. (2020). Risk, resilience, and rebalancing in global value chains.

[18] Natarajarathinam, M., Capar, I., & Narayanan, A. (2009). Managing supply chains in times of crisis: a review of literature and insights. International journal of physical distribution & logistics management, 39(7), 535-573.

[19] Odunaiya, O. G., Soyombo, O. T., & Ogunsola, O. Y. (2021). Economic incentives for EV adoption: A comparative study between the United States and Nigeria. Journal of Advanced Education and Sciences, 1(2), 64-74.

[20] Ogbeta, C., Mbata, A., & Katas, K. (2021). Innovative strategies in community and clinical pharmacy leadership: Advances in healthcare accessibility, patient-centered care, and environmental stewardship. Open Access Research Journal of Science and Technology, 2(2), 16-22.

[21] Otokiti, B. O., Igwe, A. N., Ewim, C. P.-M., & Ibeh, A. I. (2021). Developing a framework for leveraging social media as a strategic tool for growth in Nigerian women entrepreneurs. Int J Multidiscip Res Growth Eval, 2(1), 597-607.

[22] Paul, P. O., Abbey, A. B. N., Onukwulu, E. C., Agho, M. O., & Louis, N. (2021). Integrating procurement strategies for infectious disease control: Best practices from global programs. prevention, 7, 9.

[23] Ponis, S. T., & Koronis, E. (2012). Supply Chain Resilience? Definition of concept and its formative elements. The journal of applied business research, 28(5), 921-935.

[24] Sam-Bulya, N. J., Omokhoa, H. E., Ewim, C. P.-M., & Achumie, G. O. Developing a Framework for Artificial Intelligence-Driven Financial Inclusion in Emerging Markets.

[25] Scala, B., & Lindsay, C. F. (2021). Supply chain resilience during pandemic disruption: evidence from healthcare. Supply Chain Management: An International Journal, 26(6), 672-688.

[26] Trakadas, P., Simoens, P., Gkonis, P., Sarakis, L., Angelopoulos, A., Ramallo-González, A. P., . . . Pariente, T. (2020). An artificial intelligence-based collaboration approach in industrial iot manufacturing: Key concepts, architectural extensions and potential applications. Sensors, 20(19), 5480.

[27] Tyagi, A. K., Aswathy, S., & Abraham, A. (2020). Integrating blockchain technology and artificial intelligence: Synergies perspectives challenges and research directions. Journal of Information Assurance and Security, 15(5), 1554.

[28] Yu, Z., Razzaq, A., Rehman, A., Shah, A., Jameel, K., & Mor, R. S. (2021). Disruption in global supply chain and socio-economic shocks: a lesson from COVID-19 for sustainable production and consumption. Operations Management Research, 1-16.

How to cite this paper

Grace Omotunde Osho, Julius Olatunde Omisola, Joseph Oluwasegun Shiyanbola "The RFID-Enabled Reverse Logistics Optimization Model (RE-RLOM): A DFSS-Based Solution for Sustainable Circular Supply Chains" Iconic Research And Engineering Journals Volume 6 Issue 10 2023 Page 1077-1094
Grace Omotunde Osho, Julius Olatunde Omisola, Joseph Oluwasegun Shiyanbola "The RFID-Enabled Reverse Logistics Optimization Model (RE-RLOM): A DFSS-Based Solution for Sustainable Circular Supply Chains" Iconic Research And Engineering Journals, vol. 6, no. 10, Apr. 2023
Grace Omotunde Osho, Julius Olatunde Omisola, Joseph Oluwasegun Shiyanbola (2023). The RFID-Enabled Reverse Logistics Optimization Model (RE-RLOM): A DFSS-Based Solution for Sustainable Circular Supply Chains. Iconic Research And Engineering Journals, 6(10).
Grace Omotunde Osho, Julius Olatunde Omisola, Joseph Oluwasegun Shiyanbola "The RFID-Enabled Reverse Logistics Optimization Model (RE-RLOM): A DFSS-Based Solution for Sustainable Circular Supply Chains" Iconic Research And Engineering Journals, vol. 6, no. 10, Apr. 2023.
@article{1704259,
      author = {Grace Omotunde Osho, Julius Olatunde Omisola, Joseph Oluwasegun Shiyanbola},
      title = {The RFID-Enabled Reverse Logistics Optimization Model (RE-RLOM): A DFSS-Based Solution for Sustainable Circular Supply Chains},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {10},
      pages = {1077-1094},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704259.pdf},
      abstract = {The RFID-enabled reverse logistics optimization model (RE-RLOM) is a novel framework designed to enhance the efficiency and sustainability of reverse logistics within circular supply chains. By integrating radio frequency identification (RFID) technology with a design for six sigma (DFSS) approach, RE-RLOM optimizes the reverse flow of products, materials, and waste, contributing to the creation of closed-loop supply chains. Reverse logistics, which involves the return, repair, recycling, or disposal of products, is a crucial aspect of achieving sustainability in modern supply chains. However, challenges such as inefficiencies in tracking, handling returns, and managing product lifecycle contribute to operational waste and increased environmental impact. RE-RLOM leverages RFID's real-time tracking capabilities to enhance visibility and control over the reverse logistics process. This technology allows for seamless monitoring of product movements, reducing errors, improving decision-making, and increasing the efficiency of product recovery, recycling, and reuse. The DFSS methodology ensures that each phase of the reverse logistics process, from identifying customer needs to verifying process optimization, is systematically designed to minimize waste, reduce costs, and improve overall operational efficiency. Through the integration of RFID and DFSS, RE-RLOM supports the transition to more sustainable circular supply chains by improving material flow, promoting product lifecycle extension, and enhancing resource recovery. This explores the key components of the RE-RLOM, its application in various industries, and its potential to address the challenges faced by traditional reverse logistics systems. Furthermore, it discusses the benefits, limitations, and future research opportunities for optimizing reverse logistics and supporting the global shift toward a circular economy.},
      keywords = {RFID-enabled, Reverse logistics optimization model (RE-RLOM), DFSS-based, Sustainable circular, Supply chains},
      month = {April},
  }