A Web-Based Data Driven Analytics System for Income and Operations Management Using Linear Regression for Modern Concept Prints

This project, titled “A Web-Based Data Driven Analytics System for Income and Operations Management Using Linear Regression for Modern Concept Prints,” was developed to address the inefficiencies of manual income tracking and operations management in small enterprises. Income management, which includes tracking revenue, managing expenses, and ensuring profitability, is crucial for financial planning and long-term sustainability. Operations management, which involves the efficient handling of day-to-day processes such as task delegation, inventory monitoring, and order fulfillment, is equally essential for business optimization. When these areas are not integrated or managed manually, it becomes difficult to control costs, optimize performance, and make informed decisions.
This study follows an applied research approach, focusing on the development of a Web-Based System to solve real-world business challenges faced by Modern Concept Prints, a local printing business. The business had been relying on spreadsheets and verbal coordination for its operations, leading to frequent delays, inaccurate records, and limited forecasting capabilities. To address these issues, the researchers designed and implemented a centralized platform that automates key business processes, including Sales Monitoring, Inventory Management, and Task Tracking, while utilizing Linear Regression for Income Prediction based on historical data. The system also integrates Predictive Analytics to forecast future income, thus enhancing decision-making. The project followed the Spiral Model as its software development methodology, allowing for iterative development, continuous risk assessment, and frequent refinement of the system based on user feedback.
Developed using PHP, MySQL, HTML, CSS, and JavaScript, the system offers a dynamic, user-friendly interface that supports real-time data analysis and visualization. Evaluation results, guided by ISO 25010 quality standards, showed high satisfaction among both technical and user respondents in terms of System Usability, Data Security, functionality, reliability, and security. The system significantly improved operational workflows, reduced manual errors, and enhanced financial planning through automated income prediction and sales monitoring. The project demonstrates how integrating automation, Predictive Analytics, Linear Regression, and business management can help small businesses optimize decision-making, productivity, and long-term sustainability. Future recommendations include adding accounting and payroll modules, mobile compatibility, and advanced forecasting algorithms to further enhance scalability and performance.

Web-Based System, Business Management, Income Prediction

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