Design and Implementation of a Web-Based Used Car Electronic Catalog with Decision Support System Using the Waterfall Model and SAW Method

Abstract

Managing used car inventories poses significant operational challenges for showrooms, particularly regarding stock tracking inefficiencies and subjective multi-criteria evaluations during customer consultations. This study develops a web-based e-catalog and decision-support application engineered using the Laravel framework and MySQL database to streamline showroom operations. The system embeds the Simple Additive Weighting (SAW) method to provide objective, data-driven vehicle recommendations based on five critical operational variables: price, production year, inventory age, tax validity, and transmission type. To preserve operational integrity, the application enforces role-based access control distributed across three distinct user profiles: Marketing, Sales, and Owner. Systematic computational validation confirms the correct algorithmic execution of the backend scoring process, successfully generating automated, mathematical priority rankings that match the system's responsive user interfaces. The implementation proves that integrating a standardized decision-support algorithm into a scalable framework mitigates administrative bottlenecks, removes human subjectivity, and enhances executive data transparency in secondary automotive commerce.