Abstract
Supply chain is a cornerstone of the eCommerce industry and is a key component in its growth. Supply chain data analytics and risk management in the eCommerce space have picked up steam in recent times. With the availability of suitable & capable resources for big data and artificial intelligence, predictive analytics has become a significant area of interest to achieve organizational excellence by exploiting data available and developing data-driven support systems. The existing literature in supply chain risk management explain various methods assisting to identify & mitigate risks using big data and machine learning (ML) techniques across industries. Although ML techniques are used in various industries, not many aspects of eCommerce had utilized predictive analytics to their benefit. In the eCommerce industry, delivery is paramount for the business. During COVID-19 pandemic, needs changed. Reliable delivery services are preferred to speedy delivery. Multiple parameters involve delivering the product to a customer as per promised due date. This research will try to predict the risks of late deliveries to online shopping customers by analyzing the historical data using machine learning techniques and comparing them by multiple performance metrics. As a part of this comparative study, a new hybrid technique which is a combination of Logistic Regression, XGBoost, Light GBM, and Random Forest is built which has outperformed all the other ensemble and individual algorithms with respect to accuracy, specificity, precision, and F1-score. This study will benefit the eCommerce companies to improve their customer satisfaction by predicting late deliveries accurately and early.
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References
Baryannis, G., Validi, S., Dani, S., Antoniou, G.: Supply chain risk management and artificial intelligence: state of the art and future research directions. Int. J. Prod. Res. 57(7), 2179–2202 (2019)
Dubey, R., et al.: Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. Int. J. Prod. Econ. 226, 107599 (2020)
Gunasekaran, A., et al.: Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res. 70, 308–317 (2017). http://www.springer.com/lncs. Accessed 21 Nov 2016
Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., Lin, Y.: Big data analytics in SCM: A state-of-the-art literature review. Comput. Oper. Res. 98, 254–264 (2018)
Goldman, S.: Post-pandemic e-commerce: The unstoppable growth of online shopping. The future of customer engagement and experience (2021)
Weingarten, J., Spinler, S.: Shortening delivery times by predicting customers’ online purchases: a case study in the fashion industry. Inf. Syst. Manage. 384, 287–308 (2021). https://www.tandfonline.com/doi/full/10.1080/10580530.2020.1814459
Bag, S., Wood, L.C., Xu, L., Dhamija, P., Kayikci, Y.: Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Res. Conserv. Recycl. 153, 104559 (2020)
Islam, S., Amin, S.H.: Prediction of probable backorder scenarios in the supply chain using distributed RF and GB ML techniques. J. Big Data 71, 65 (2020)
John, S., Shah, B.J., Kartha, P.: Refund fraud analytics for an online retail purchases. J. Bus. Anal. 31, 56–66 (2020)
Malviya, L., Chittora, P., Chakrabarti, P., Vyas, R.S. and Poddar, S.: Backorder prediction in the supply chain using machine learning. Mater. Today: Proc. (2021)
Gyulai, D., Pfeiffer, A., Nick, G., Gallina, V., Sihn, W., Monostori, L.: Lead time prediction in a flow-shop environment with analytical and machine learning approaches. IFAC-PapersOnLine 51(11), 1029–1034 (2018)
Zhu, Y., Zhou, L., Xie, C., Wang, G.-J., Nguyen, T.V.: Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. Int. J. Prod. Econ. 211, 22–33 (2019)
Pawłowski, M.: Machine learning based product classification for eCommerce. J. Comput. Inf. Syst. 66(4), 1–10 (2021)
Constante, F.: DataCo smart supply chain for big data analysis. Mendeley (2019). https://data.mendeley.com/datasets/8gx2fvg2k6/5
Bergstra, J., Yamins, D., Cox, D.D.: Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th Python in Science Conference, p. 20. Citeseer (2013)
Nogueira, F.: Bayesian optimization: open source constrained global optimization tool for Python (2014). https://github.com/fmfn/BayesianOptimization
Velmurugan, M., Ouyang, C., Moreira, C., Sindhgatta, R.: Evaluating fidelity of explainable methods for predictive process analytics. In: Nurcan, S., Korthaus, A. (eds.) Intelligent Information Systems. CAiSE 2021. Lecture Notes in Business Information Processing, vol. 424, pp.64–72. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79108-7_8
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Lolla, R. et al. (2023). Machine Learning Techniques for Predicting Risks of Late Delivery. In: Wah, Y.B., Berry, M.W., Mohamed, A., Al-Jumeily, D. (eds) Data Science and Emerging Technologies. DaSET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-99-0741-0_25
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DOI: https://doi.org/10.1007/978-981-99-0741-0_25
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