Abstract: With the exponential growth of the online food delivery industry, ensuring timely deliveries has become paramount to customer satisfaction and business success. However, the persistent challenge of delayed delivery continues to plague food delivery companies, resulting in customer dissatisfaction and potential revenue loss. This project delves into the domain of food services to investigate the complexities surrounding the accurate estimation of delivery times. Through the exploration of predictive models and the analysis of various factors influencing delivery time estimation, including geographical variables, traffic patterns, order complexity, and operational dynamics, the study aims to develop robust forecasting mechanisms. By leveraging historical data and employing advanced analytical techniques, this research seeks to uncover insights that can enhance operational efficiency and mitigate delivery delays effectively. Ultimately, the outcomes of this study are poised to contribute valuable insights to the online food delivery industry, fostering improved customer satisfaction, retention, and sustained growth in this dynamic market landscape.
Keywords: Delivery time estimation, Customer satisfaction, Predictive models, Geographical variables, Traffic patterns
Cite:
Dr. Ayyappa Chakravarthi M, Shaik Eesa Ruhulla Haq, Madapakula Venkata Anil, Bathula Venkata Vamsi, Gogireddy Venkata Reddy, "Forecasting Food Delivery Time: An Exploration of Predictive Models and Factors Impacting Delivery Time Estimation", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13318.
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DOI:
10.17148/IJARCCE.2024.13318
