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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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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