Development of an Inventory Management System on a Mobile Application using Machine Learning Technology
Keywords:
demand forecasting, machine learning, Point of Sale System, inventory managementAbstract
This research focuses on enhancing inventory management efficiency to reduce operational expenses and increase profitability for businesses. The objectives of the study are (1) to develop a mobile application integrated with a Point of Sale: POS system for barcode scanning and in-store sales and (2) to implement a predictive purchasing system using machine learning technology. The Gradient Boosting Regression model was found to yield the best performance, achieving MAE=1.4320, MSE=3.7344, RMSE=1.9270, R²=0.7148, and MAPE=0.4953; (3) to increase profits and reduce unnecessary costs caused by excess inventory and hardware installation, including complex software systems that are not user-friendly for inexperienced staff; and (4) to evaluate system performance and user satisfaction. The evaluation was conducted with 30 targeted small business owners who currently operate without automated inventory systems. Five aspects were assessed: (1) inventory management and adjustment (=3.64, SD=0.54); (2) POS system usability (
= 4.12, SD= 0.17); (3) forecasting-based purchasing decisions (
=3.97, SD=0.07); (4) system accuracy and data security (
=3.80, SD= 0.20); and (5) user interface design (
=4.22, SD=0.22). The results indicate that the application effectively meets user needs, particularly in terms of design and usability. Therefore, the developed application is well-suited for small businesses seeking to adapt to the rapidly evolving marketplace.
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