Aplicación móvil para dispositivos android aplicando modelos de predicción para la gestión de pedidos en Ramones Restaurant.

Abstract

This research project focuses on the development and implementation of an Android mobile application using predictive models for order management at Ramones Restaurant. The primary objective was to optimize the company's customer service process by integrating technology into its traditional order management system. An in-depth analysis of the existing order management procedures at Ramones Restaurant was conducted, revealing significant inefficiencies and areas for improvement. Various predictive models were evaluated, with a focus on their adaptability, scalability, and potential for integration into the restaurant’s operational structure. Artificial neural networks were identified as the most suitable due to their flexible architecture. The mobile application was developed using robust software frameworks, facilitating a faster and more efficient development process. The mobile application was developed using the Scrumban agile methodology, to ensure adaptability to changing requirements and unforeseen challenges. The implementation of the application demonstrated the feasibility and benefits of introducing advanced technologies in small and medium-sized enterprises (SMEs). The findings suggest that such integrations can be successfully achieved with careful planning and consideration of business-specific complexities. The study recommends further comparative analysis of predictive models to optimize the application’s performance. Future research should explore the broader impact of technology implementation in SMEs. Additionally, a post-implementation evaluation plan for Ramones Restaurant is advised to ensure continuous improvement and adaptation of the application to meet evolving business needs and customer expectations