Predicting On-Time Delivery: Bangladesh E-Commerce
Keywords:
Predicting On-Time Delivery: Bangladesh E-CommerceAbstract
Timely order fulfillment is a critical determinant of customer satisfaction and operational efficiency in e-commerce logistics, particularly in emerging economies such as Bangladesh. This study examines on-time delivery performance using Daraz import shipment data, representing one of the largest cross-border e-commerce logistics operations in the country. The research develops a machine learning–based predictive framework to identify operational and customer-related factors contributing to delivery delays. Supervised classification models, including tree-based algorithms, are applied to a large shipment dataset encompassing warehouse allocation, shipment mode, product characteristics, customer interactions, and promotional attributes. Model performance is assessed using standard classification metrics to ensure robustness and reliability. The results reveal that shipment mode, product weight, warehouse block, customer care calls, and discount levels significantly influence delivery timeliness. The findings provide actionable insights for e-commerce platforms and logistics managers, offering data-driven recommendations to improve import shipment reliability and enhance last-mile delivery performance in Bangladesh.
References
Ahmed, I., Rabbi, A. F. M. F., & Samira, M. J. A. (n.d.). The role of logistics in driving customer
satisfaction: An empirical investigation in Bangladesh.
Bertsimas, D., Dunn, J., & Pawlowski, C. (2019). Machine learning under a modern optimization
lens. Dynamic Ideas LLC.
Bowersox, D. J., Closs, D. J., & Cooper, M. B. (2013). Supply chain logistics management (4th ed.).
McGraw-Hill/Irwin.
Boysen, N., Fedtke, S., & Schwerdfeger, S. (2021). Last-mile delivery concepts: A survey from an
operational research perspective. OR Spectrum, 43(1), 1–58. https://doi.org/10.1007/s00291-020-
00607-8
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.
org/10.1023/A:1010933404324
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority
over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., & Fosso Wamba, S. (2019). World
class sustainable supply chain management: Critical review and further research directions. The International Journal of Logistics Management, 30(1), 231–265. https://doi.org/10.1108/IJLM-11-
2017-0294
Gevaers, R., Van de Voorde, E., & Vanelslander, T. (2014). Cost modelling and simulation of lastmile characteristics in an innovative B2C supply chain environment with implications on urban
areas and cities. Procedia – Social and Behavioral Sciences, 125, 398–411. https://doi.org/10.1016/j.
sbspro.2014.01.1483
Ghosh, D., & Kankanhalli, A. (2018). Predicting customer satisfaction from customer service
interactions using supervised learning. Information Systems Frontiers, 20(2), 277–288. https://doi.
org/10.1007/s10796-017-9797-4
Hossain, M. A., Rahman, M. S., & Uddin, M. J. (2019). E-commerce adoption in Bangladesh: An
exploratory study. Journal of Asian Business and Economic Studies, 26(3), 164–180. https://doi.
org/10.1108/JABES-12-2018-0106
Huber, J., & Spinler, S. (2018). Simulation-based analysis of peer-to-peer logistics platforms.
International Journal of Physical Distribution & Logistics Management, 48(6), 570–591. https://
doi.org/10.1108/IJPDLM-02-2017-0102
Jiang, L., Yang, Z., & Jun, M. (2013). Measuring consumer perceptions of online shopping convenience.
Journal of Service Management, 24(2), 191–214. https://doi.org/10.1108/09564231311323962
Liu, X., Huang, G. Q., & Mak, K. L. (2018). Machine learning for demand prediction in wearable
logistics networks. International Journal of Production Research, 56(6), 2041–2056. https://doi.or
g/10.1080/00207543.2017.1374573
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In
Advances in Neural Information Processing Systems (Vol. 30, pp. 4765–4774).
Möller, C. (2020). The impact of delivery performance on online customer satisfaction. Journal
of Retailing and Consumer Services, 54, 102031. https://doi.org/10.1016/j.jretconser.2019.102031
Morganti, E., Dablanc, L., & Fortin, F. (2014). Final deliveries for online shopping: The deployment
of pickup point networks in urban and suburban areas. Research in Transportation Business &
Management, 11, 23–3 https://doi.org/10.1016/j.rtbm.2014.03.002
Nguyen, T. M., Tran, V. T., & Duong, N. D. (2020). Predictive analytics for delayed deliveries using
logistic regression and real time data. International Journal of Logistics Systems and Management,
36(4), 450–473.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven
decision making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
Rahman, M., & Alam, M. J. (2020). Logistics performance challenges in Bangladeshi e-commerce.
Journal of Transport and Supply Chain Management, 14(1), 1–12. https://doi.org/10.4102/jtscm.
v14i1.507
Rao, S., Rabinovich, E., & Raju, D. (2014). The role of physical distribution services as determinants
of product returns in Internet retailing. Journal of Operations Management, 32(6), 295–312. https://
doi.org/10.1016/j.jom.2014.06.002
Singh, A., & Yassine, A. (2020). Diagnosing e-commerce delivery failures using historical data analytics. Computers & Industrial Engineering, 147, 106675. https://doi.org/10.1016/j.
cie.2020.106675
Tiwari, M. K., Jain, A., & Vidyarthi, N. (2021). Last mile delivery analytics: A review and research
agenda. International Journal of Production Economics, 233, 107992. https://doi.org/10.1016/j.
ijpe.2020.107992
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution
that will transform supply chain design and management. Journal of Business Logistics, 34(2),
77–84. https://doi.org/10.1111/jbl.12010
Xu, H., & Zhang, Y. (2021). Machine learning applications in e-commerce supply chains:
Forecasting, resource allocation, and delivery optimization. Expert Systems with Applications, 169,
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