Design and Implementation of a Self-Service Kiosk with Embedded Analytics and Demand Forecasting for Small Retail Decision Support

Background: Small-scale retail kiosks commonly deploy transactional point-of-sale (POS) systems that capture sales data but lack integrated analytical and forecasting capabilities for operational decision support. This gap limits the ability of small-and-medium enterprise (SME) operators to respond proactively to demand fluctuations. Methods: This study presents the structured analysis, design, implementation, and evaluation of a cloud-deployed self-service kiosk system embedding interactive analytics and demand forecasting modules. The system integrates a Django-based backend, a PostgreSQL relational database, RESTful APIs, a structured demand simulation engine, and three forecasting models: Seasonal ARIMA (SARIMA), XGBoost Regressor, and Scikit-Learn Gradient Boosting Regressor. Forecasting performance was evaluated using rolling backtesting with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results: The Gradient Boosting Regressor achieved the highest predictive accuracy (MAE = $93.74, RMSE = $112.65, MAPE = 8.9%), outperforming both XGBoost (MAPE = 10.0%) and SARIMA (MAPE = 10.2%). The proposed architecture demonstrates that Systems Analysis and Design principles can guide the development of an integrated decision-support platform for small retail environments. Machine learning ensemble models more effectively capture nonlinear demand patterns generated by growth and seasonality dynamics than classical time-series models. The system is deployed as a proof-of-concept cloud application accessible at the address listed in the Data Availability section.