A Smart Business Solution for Online Ordering, Billing, and Sales Performance Tracking
In a world where traditional ordering systems continue to rely mainly on pens and paper, substantial difficulties such as lost orders, inaccurate information delivery, and inefficiencies in managing huge volumes of transactions remain. Manual order processing takes time and is prone to human error, resulting in delayed customer service and potential revenue loss. In response to these problems, there is a rising emphasis on real-time, digital solutions to improve operational efficiency. This study investigates “A Smart Business Solution for Online Ordering, Billing, and Sales Performance Tracking,” which aims to solve the inefficiencies inherent in manual processes. By switching to an electronic platform, this approach enhances order accuracy and processing speed while lowering the risk of misinterpretation associated with handwritten orders. The system includes a userfriendly electronic menu that gives clients a clear knowledge of their options, expediting the ordering process. The solution also includes extensive sales monitoring tools, which allow for real-time transaction tracking and analysis. It has a file maintenance mechanism to ensure data protection and the ability to generate detailed reports with statistical and graphical representations. These features not only help you make better decisions, but they also improve the client experience by speeding up service and eliminating errors. Overall, this advanced system represents a disruptive approach to updating buying and invoicing procedures, with significant advantages for both customers and enterprises. It replaces outmoded manual techniques with a streamlined, efficient digital solution that safeguards data, improves accuracy, and speeds up service, resulting in increased operational effectiveness and customer happiness.
Authors
- John Ivan C. Maurat
- G. Romano
Institutions
- University of the Philippines Manila (PH)
Publication Details
- Published
- 2025-05-24
- DOI
- https://doi.org/10.1109/iscaie64985.2025.11081127
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00