Abstract
The reach of online grocery services has expanded to encompass new customer segments in recent years. During the early stages of the COVID-19 outbreak, when delivery slots were limited and customer demand was high, click-and-collect models became increasingly popular. In order to keep pace with evolving customer behavior, it is crucial for retailers to maintain a high degree of operational process efficiency within their business model. This research paper proposes a resource planning system for grocery retail delivery services that utilizes machine learning techniques. The system aims to optimize the allocation of resources, such as delivery drivers, and reduce transport costs, improving the overall efficiency and profitability of the delivery operations. The system is designed to capture and analyze data from various sources, including delivery orders, traffic patterns, weather conditions, and driver schedules. The proposed research demonstrates the potential of machine learning techniques to transform resource planning in grocery retail delivery services and highlights the importance of Data-Driven decision-making in today’s highly competitive retail landscape.
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Yakymchuk, B., Liashenko, O. (2023). Modeling the Resource Planning System for Grocery Retail Using Machine Learning. In: Antoniou, G., et al. Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2023. Communications in Computer and Information Science, vol 1980. Springer, Cham. https://doi.org/10.1007/978-3-031-48325-7_22
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