Revolutionizing Product Return Management: Harnessing Supply Community Network for Enhanced Customer Experience

Revolutionizing Product Return Management: Harnessing Supply Community Network for Enhanced Customer Experience

  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-3
Year of Publication : 2024
Author : Mohamed Omar Abdullahi, Abdukadir Dahir Jimale, Yahye Abukar Ahmed, Abdulaziz Yasin Nageye
DOI : 10.14445/22315381/IJETT-V72I3P116

How to Cite?

Mohamed Omar Abdullahi, Abdukadir Dahir Jimale, Yahye Abukar Ahmed, Abdulaziz Yasin Nageye, "Revolutionizing Product Return Management: Harnessing Supply Community Network for Enhanced Customer Experience," International Journal of Engineering Trends and Technology, vol. 72, no. 3, pp. 177-183, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I3P116

Abstract
Ensuring a seamless and customer-friendly return process drives customer satisfaction and influences repeat purchases. This study explores the impact of proactive communications and a flexible return process with transparent options to create an exceptional returns experience. Leveraging the Supply Community Network (SCN) approach, enriched with social Internet of Things capabilities and humanoid social networking behavior assumptions, we propose a more effective and efficient application of product return processes. Our research extends the applicability of the SCN approach to encompass a product return scenario, thereby mirroring real-world interactions and distinct SCN configurations. Through a case study, we assess the approach's effectiveness and highlight key endeavors for its future use and refinement. Furthermore, we emphasize how the SCN approach effectively mitigates product return challenges by presenting a detailed application scenario. This approach demonstrates its potential to revolutionize product return management, paving the way for enhanced customer experiences and fostering lasting customer loyalty.

Keywords
Product return, Customer experience, Internet of Things, Social Internet of Things, Supply Community Network.

References
[1] Marcia Kaplan, The Growing Problem of Customer Returns, PracticalEcommerce, 2019. [Online]. Available: https://www.practicalecommerce.com/the-growing-problem-of-customer-returns
[2] Kevin J. Ryan, $74 Billion Worth of Merchandise Gets Returned Around the Holidays, This Startup is Helping Retailers Deal with it Santa Monica-Based Happy Returns is Changing the Way Both Online and Brick-and-Mortar Shops Handle Returns, Inc.com, [Online]. Available: https://www.inc.com/kevin-j-ryan/happy-returns-retail-season.html
[3] Faith Moyo, Brenda Scholtz, and Mohammed Alhassan, “A Data-Driven Decision-Making Model for the Third-Party Logistics (3PL) Industry,” Kalpa Publications in Computing, vol. 12, pp. 213-226, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Enhou Zu et al., “Management Problems of Modern Logistics Information System Based on Data Mining,” Artificial Intelligence and Edge Computing in Mobile Information Systems, vol. 2021, pp. 1-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Gabriel Koman et al., “Benefits of Industry 4.0 for Logistics and Decision-Making of Managers,” LOGI-Scientific Journal on Transport and Logistics, vol. 10, no. 2, pp. 33-41, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Arkadiusz Kawa, and Magdalena Wałęsiak, “Marketplace as a Key Actor in E-Commerce Value Networks,” LogForum, vol. 15, no. 4, pp. 521-529, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Wojciech Piotrowicz, and Richard Cuthbertson, “Introduction to the Special Issue Information Technology in Retail: Toward Omnichannel Retailing,” International Journal of Electronic Commerce, vol. 18, no. 4, pp. 5-16, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Mariem Masmoudi, Mounir Benaissa, and Habib Chabchoub, “Optimisation of E-Commerce Logistics Distribution System: Problem Modelling and Exact Resolution,” International Journal of Business Performance and Supply Chain Modelling, vol. 6, no. 3, pp. 358- 375, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Glenn C. Parry et al., “Operationalising IoT for Reverse Supply: The Development of Use-Visibility Measures,” Supply Chain Management: An International Journal, vol. 21, no. 2, pp. 228-244, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Martha C. Cooper, Douglas M. Lambert, and Janus D. Pagh, “Supply Chain Management: More Than a New Name for Logistics,” The International Journal of Logistics Management, vol. 8, no. 1, pp. 1-14, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Kumaraguru Mahadevan, “Reverse Logistics: From Chitty, Chitty, Bang, Bang to Fast and the Furious,” ANZAM 2019 Conference-Wicked Solutions to Wicked Problems: The Challenges Facing Management Research and Practice, Cairns, Queensland, Australia, pp. 396-416, 2019.
[Google Scholar]
[12] Kumaraguru Mahadevan, “Investigation of Collaborative Supply Chain Practices through Integration, Visibility and Information Sharing: Theoretical and Industry Perspective,” Western Sydney University, pp. 1-521, 2013.
[Google Scholar] [Publisher Link]
[13] Reinaldo Fagundes dos Santos, and Fernando Augusto Silva Marins, “Integrated Model for Reverse Logistics Management of Electronic Products and Components,” Procedia Computer Science, vol. 55, pp. 575-585, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Mohammed Shaik, and Walid Abdul‐Kader, “Performance Measurement of Reverse Logistics Enterprise: A Comprehensive and Integrated Approach,” Measuring Business Excellence, vol. 16, no. 2, pp. 23-34, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Amir M. Sharif et al., “Evaluating Reverse Third-Party Logistics Operations Using a Semi-Fuzzy Approach,” International Journal of Production Research, vol. 50, no. 9, pp. 2515-2532, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Sibel A. Alumur et al., “Multi-Period Reverse Logistics Network Design,” European Journal of Operational Research, vol. 220, no. 1, pp. 67-78, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[17] A. Niknejad, and D. Petrovic, “Optimisation of Integrated Reverse Logistics Networks with Different Product Recovery Routes,” European Journal of Operational Research, vol. 238, no. 1, pp. 143-154, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Fotis Kitsios, and Maria Kamariotou, “Decision Support Systems for Strategic Information Systems Planning: An Approach for Logistics Strategic Management,” International Journal of Decision Support Systems, vol. 3, no. 3-4, pp. 207-221, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Lucineide Bispo dos Reis Luz, and Claudio Parisi, “Trade-Off in the Decision-Making Process: Research with Logistics Area,” Brazilian Strategy Magazine, vol. 11, no. 3, pp. 387-403, 2018.
[Google Scholar] [Publisher Link]
[20] Kuldip Singh Sangwan, “Key Activities, Decision Variables and Performance Indicators of Reverse Logistics,” Procedia CIRP, vol. 61, pp. 257-262, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Dennis Stindt, “An Environmental Management Information System for Improving Reverse Logistics Decision-Making,” International Conference on Computational Logistics, pp. 163-177, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Marta Kadłubek, “Information Areas in Logistics Supply Chain Management,” Organizations and Performance in a Complex World: 26th International Economic Conference of Sibiu (IECS), pp. 79-920, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Dmitriy Muzylyov, Natalya Shramenko, and Vitalii Ivanov, “Management Decision-Making for Logistics Systems Using a Fuzzy-Neural Simulation,” Advances in Industrial Internet of Things, Engineering and Management, pp. 175-192, 2021.
[CrossRef] [Google Scholar] [Publisher Link]