Predictive Damage Reduction Modeling in E-Commerce Fulfillment Using Gradient Boosted Trees
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I3P112Keywords:
Damage Prediction, Gradient Boosting, E-Commerce Fulfillment, Predictive Modeling, SHAP Values, Feature Engineering, Supply Chain Analytics, Machine Learning, Logistics Optimization, Model InterpretabilityAbstract
Product damage in ecommerce fulfillment is a continuing issue which impacts customer satisfaction, boosts reverse logistics charges, and drives supply chain network stock losses. With the increasing complexity of fulfillment processes and the rise of order volumes, companies need mechanisms that can proactively detect and address damage risks before products hit the end-user. The present study introduces a predictive damage reduction framework that is able to employ Gradient Boosted Tree (GBT) classification models to classify historical operational data from fulfillment processes into high-risk scenarios. The proposed approach combines various data sources such as order characteristics, product parameters, packaging models, warehouse handling parameters, carrier routing information, transportation parameters, etc., to build a comprehensive damage risk prediction model. A systematic approach of feature engineering is presented, and it is used to capture individual and interaction effects sof operational variables. Specific attention is given to engineered features like fragility classification, packaging void fill ratios, transit distance metrics, and handling frequency indicators, which are found to have a significant impact on damage outcomes. The Gradient Boosted Tree model is trained and tested with large-scale fulfillment data, and performs well in detecting orders at high-risk of damage. SHAP (SHapley Additive exPlanations) analysis is used to give both global and local interpretability of the model prediction, helping to enable fulfillment managers to understand the key drivers of the damage risk and to implement targeting corrective actions. Operational deployment results show that product damage rates have decreased, packaging decisions have improved, and significant cost savings achieved with the proactive intervention strategies. Overall, the results highlight the potential of machine learning-based predictive analytics to enhance fulfillment reliability, streamline logistics processes, and enable data-driven decision-making in today's e-commerce supply chains.
Downloads
References
[1] Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
[2] Islam, S., & Amin, S. H. (2020). Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques. Journal of big data, 7(1), 65.
[3] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
[4] Dolgui, A., & Ivanov, D. (2021). Ripple effect and supply chain disruption management: new trends and research directions. International Journal of Production Research, 59(1), 102-109.
[5] Kawa, A. (2017). Fulfillment service in e-commerce logistics. LogForum, 13(4).
[6] Iranitalab, A., & Khattak, A. J. (2017). Comparison of four statistical and machine learning methods for crash severity prediction. Accident Analysis & Prevention, 108, 27–36. https://doi.org/10.1016/j.aap.2017.08.008
[7] Kumar, M. S., & Yuvaraj, N. (2020). Building a Privacy-Aware Customer Data Foundation: A Governance-First Approach to Digital Service Systems. International Journal of Emerging Research in Engineering and Technology, 1(4), 55-68.
[8] Hammervoll, T. (2011). Dealing with damage in supply chain relationships. Journal of Business-to-Business Marketing, 18(2), 127-154.
[9] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
[10] Yang, H., Li, E., Cai, Y. F., Li, J., & Yuan, G. X. (2021). The extraction of early warning features for predicting financial distress based on XGBoost model and shap framework. International Journal of Financial Engineering, 8(03), 2141004.
[11] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis.
[12] Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2, pp. 1-800). Cambridge: MIT press.
[13] Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
[14] Chopra, S., & Meindl, P. (2001). Strategy, planning, and operation. Supply Chain Management, 15(5), 71-85.
[15] Shi, H., Li, H., Zhang, D., Cheng, C., & Cao, X. (2018). An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. Computer Networks, 132, 81-98.
[16] Abdellah, A., Belaid, B., & Rachid, L. (2018). Clustering prediction techniques in defining and predicting customers defection: The case of e-commerce context. International Journal of Electrical and Computer Engineering, 8(4), 2367.
[17] Xu, G., Qiu, X., Fang, M., Kou, X., & Yu, Y. (2019). Data-driven operational risk analysis in E-Commerce Logistics. Advanced Engineering Informatics, 40, 29-35.
[18] Levi, D. S., Chen, X., & Bramel, J. (2014). The logic of logistics: Theory, algorithms, and applications for logistics management.
[19] Sheffi, Y. (2007). The resilient enterprise: overcoming vulnerability for competitive advantage. MIT press.
[20] Hatwell, J., Gaber, M. M., & Azad, R. M. A. (2021). gbt-hips: Explaining the classifications of gradient boosted tree ensembles. Applied Sciences, 11(6), 2511.
[21] Mishra, M., Sidoti, D., Avvari, G. V., Mannaru, P., Ayala, D. F. M., Pattipati, K. R., & Kleinman, D. L. (2017). A context-driven framework for proactive decision support with applications. IEEE Access, 5, 12475-12495.
[22] Holzinger, A., Saranti, A., Molnar, C., Biecek, P., & Samek, W. (2020, July). Explainable AI methods-a brief overview. In International workshop on extending explainable AI beyond deep models and classifiers (pp. 13-38). Cham: Springer International Publishing.
[23] Xu, Z., Huang, G., Weinberger, K. Q., & Zheng, A. X. (2014, August). Gradient boosted feature selection. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 522-531).
