Ekruyota OG¹*, Ukpenusiowho D² and Ofili D³

¹Department of Computer Science, Southern Delta University, Ozoro, Nigeria
²Department of Software Engineering, Southern Delta University, Ozoro, Nigeria
³Department of Computer Science, Delta State University, Abraka, Nigeria

*Corresponding Author: Ekruyota OG, Department of Computer Science, Southern Delta University, Ozoro, Nigeria.

Received: April 06, 2026; Published: June 12, 2026

Abstract

This study presents the design and implementation of a comprehensive Sales Management and Analysis System that integrates machine learning to enhance operational efficiency and strategic decision-making in retail contexts. The system employs PHP for backend processing, Python for implementing a Decision Tree algorithm, and MySQL for centralized data management, providing a unified framework for capturing, processing, and analyzing sales and inventory data. By leveraging historical transaction records, the system identifies patterns, generates predictive insights, and supports real-time decision-making for inventory control, sales forecasting, and marketing strategies. The user interface is intuitive, offering streamlined access to functionalities such as sales entry, inventory monitoring, predictive dashboards, and report generation. Security measures, including role-based access and data encryption, safeguard sensitive business information while ensuring reliable system operations. Developed using the Agile methodology, the system benefits from iterative refinement and stakeholder feedback, ensuring adaptability and scalability across diverse retail environments. This study addresses existing gaps in integrated, ML-based sales systems by combining predictive analytics, real-time insights, and robust governance principles, offering a scalable, data-driven solution tailored to the needs of small- and medium-sized enterprises in dynamic markets.

Keywords: Machine Learning; Predictive Analytics; Retail Operation; Sales Management

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Citation

Citation: Ekruyota OG., et al. “An Intelligent Sales Management and Analytics Framework Using Machine Learning for Data-Driven Retail Decision-Mak- ing". Acta Scientific Computer Sciences 8.1 (2026): 19-26.

Copyright

Copyright: © 2025 Ekruyota OG., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.