Development of a Red Flag Detection Model in Daily Operations Using a Combination of Rule-Based Approach and Logistic Regression at Terminal Coffee - Malsad, Gredinov Sumanta; Sibarani, Mentiana (Sekolah Tinggi Ilmu Ekonomi Amkop Makassar, 2025)
This study develops an early warning system for detecting operational anomalies (red flags) in the food and beverage industry, specifically for businesses lacking historical fraud data, using Terminal Coffee as a case study. The system integrates rule-based methods and logistic regression, combining classification logic and probabilistic prediction. The initial stage applies a rule-based approach by setting statistical thresholds (mean ± standard deviation) for eight operational indicators, including COGS ratio, electricity cost, average items and sales per transaction, and discount-to-sales ratio. From a total of 1,449 shift-level observations collected over one year, 436 (30.09%) were classified as red flags. These classifications were then used as the dependent variable in a binary logistic regression model. The estimation results identified four statistically significant predictors (p < 0.05): COGS per item, average sales per transaction, average sales per item, and discount ratio. The final model demonstrated strong classification performance, with 92% accuracy, 83% sensitivity, 95.3% specificity, 86.7% precision, and an AUC of 0.957—indicating excellent discriminatory ability. These findings suggest that combining rule-based logic and logistic regression effectively builds a reliable and adaptive early warning system for operational monitoring, even in the absence of historical fraud records. The proposed system is applicable for integration into managerial dashboards as a data-driven decision support tool to facilitate proactive, objective, and timely interventions in daily operational oversight. Key Words : red flags, rule-based, logistic regression, anomaly detection, early warning system.