Analyzing Delivery Area/Zone Tagging Techniques Within Fulfillment Centres For Last Mile Delivery Orders
DOI:
https://doi.org/10.58344/jws.v2i7.340Keywords:
delivery zone, delivery area, last-mile, fulfillment center, logisticsAbstract
Last-mile delivery in e-commerce logistics is crucial and difficult, affecting consumer happiness and operational efficiency. Fulfillment centers use delivery area/zone marking to ease this operation. This study examines fulfillment center methods for optimizing last-mile delivery orders. This research first examines delivery area/zone labeling methods. These methods break geographical regions into smaller manageable parts for resource allocation and route optimization. Grid-based zoning, distance-based tagging, and contemporary machine learning methods for dynamic and adaptive zone identification will be investigated. The study then examines delivery area tagging implementation factors. Zone tagging success depends on population density, order frequency, traffic patterns, and delivery time windows. Emission regulations and sustainability targets will also be examined. Delivery area/zone tagging technology and tools are also examined. GPS tracking, GIS mapping, and real-time data analytics enable effective monitoring and modifications. IoT devices and predictive analytics will also be assessed for their impact on delivery performance. This study concludes with the benefits and drawbacks of delivery area/zone labeling. Delivery time, operational expenses, and customer experience improve. Fulfillment focuses face data privacy, algorithmic biases, and system scalability issues. In conclusion, this study examines fulfillment center delivery area/zone labeling for last-mile delivery orders. E-commerce and logistics stakeholders may maximize last-mile delivery by knowing the different methods, technology, and factors affecting them.
References
Carotenuto, P., Gastaldi, M., Giordani, S., Rossi, R., Rabachin, A., & Salvatore, A. (2018). Comparison of various urban distribution systems supporting e-commerce. Point-to-point vs. collection-point-based deliveries. Transportation Research Procedia, 30, 188–196. https://doi.org/10.1016/j.trpro.2018.09.021
Castillo, V. E., Mollenkopf, D. A., Bell, J. E., & Esper, T. L. (2022). Designing technology for on?demand delivery: The effect of Customer tipping on crowdsourced driver behavior and last mile performance. Journal of Operations Management, 68(5), 424–453. https://doi.org/10.1002/joom.1187
De Maio, A., & Laganà, D. (2020). The effectiveness of Vendor Managed Inventory in the last-mile delivery: an industrial application. Procedia Manufacturing, 42, 462–466. https://doi.org/10.1016/j.promfg.2020.02.047
Jucha, P., & Corejova, T. (2021). We are ensuring the logistics of the last mile from the perspective of distribution companies. 14th International Scientific Conference on Sustainable, Modern and Safe Transport, TRANSCOM 2021, 55, 482–489. https://doi.org/10.1016/j.trpro.2021.07.012
Lee, C.-W., & Wong, W.-P. (2022). Last-mile drone delivery combinatorial double auction model using multi-objective evolutionary algorithms. Soft Computing. https://doi.org/10.1007/s00500-022-07094-9
Lim, S. F. W. T., & Winkenbach, M. (2018). Configuring the Last-Mile in Business-to-Consumer E-Retailing. California Management Review, 61(2), 132–154. https://doi.org/10.1177/0008125618805094
Ma, B., Wong, Y. D., & Teo, C.-C. (2022). Parcel self-collection for urban last-mile deliveries: A review and research agenda with a dual operations-consumer perspective. Transportation Research Interdisciplinary Perspectives, 16, 100719. https://doi.org/10.1016/j.trip.2022.100719
Ni, M., He, Q., Liu, X., & Hampapur, A. (2019). Same-Day Delivery with Crowdshipping and Store Fulfillment in Daily Operations. Transportation Research Procedia, 38, 894–913. https://doi.org/10.1016/j.trpro.2019.05.046
Nogueira, G. P. M., de Assis Rangel, J. J., & Shimoda, E. (2021). Sustainable last-mile distribution in B2C e-commerce: Do consumers care? Cleaner and Responsible Consumption, 3, 100021. https://doi.org/10.1016/j.clrc.2021.100021
Ovezmyradov, B. (2022). Product availability and stockpiling in times of pandemic: causes of supply chain disruptions and preventive measures in retailing. Annals of Operations Research, 1–33. https://doi.org/10.1007/s10479-022-05091-7
Rahman, M. A., Basheer, M. A., Khalid, Z., Tahir, M., & Uppal, M. (2022). Last Mile Logistics: Impact Of Unstructured Addresses On Delivery Times. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-4/W5-2022, pp. 3–8. https://doi.org/10.5194/isprs-archives-xlviii-4-w5-2022-3-2022
Rodrigue, J.-P. (2020). The distribution network of Amazon and the footprint of freight digitalization. Journal of Transport Geography, 88, 102825. https://doi.org/10.1016/j.jtrangeo.2020.102825
Shi, Y., Lin, Y., Li, B., & Yi Man Li, R. (2022). A bi-objective optimization model for the simultaneous pickup and delivery of medical supplies with drones. Computers & Industrial Engineering, p. 171, 108389. https://doi.org/10.1016/j.cie.2022.108389
Tian, Z., & Zhang, G. (2021). Multi-echelon fulfillment warehouse rent and production allocation for online direct selling. Annals of Operations Research, 304(1–2), pp. 427–451. https://doi.org/10.1007/s10479-021-04202-0
Tran, N. A. T., Nguyen, H. L. A., Nguyen, T. B. H., Nguyen, Q. H., Huynh, T. N. L., Pojani, D., Nguyen Thi, B., & Nguyen, M. H. (2022). Health and safety risks faced by delivery riders during the Covid-19 pandemic. Journal of Transport and Health, 25. https://doi.org/10.1016/j.jth.2022.101343
Wang, C.-N., Nguyen, N.-A.-T., Dang, T.-T., & Hsu, H.-P. (2021). Evaluating Sustainable Last-Mile Delivery (LMD) in B2C E-Commerce Using Two-Stage Fuzzy MCDM Approach: A Case Study from Vietnam. IEEE Access, 9, 146050–146067. https://doi.org/10.1109/access.2021.3121607
Xiao, Z., Wang, J. J., Lenzer, J., & Sun, Y. (2017). Understanding the diversity of final delivery solutions for online retailing: A case of Shenzhen, China. Transportation Research Procedia, 25, 985–998. https://doi.org/10.1016/j.trpro.2017.05.473
Zhang, Y., Fan, X., & Zhou, L. (2019). Analysis and research on the "last mile" distribution innovation model of e-commerce express delivery. Journal of Physics: Conference Series, 1176(4), 42044. https://doi.org/10.1088/1742-6596/1176/4/042044
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