ANALYSIS OF PHARMACEUTICAL LOGISTICS FORECASTING BY ACT CLASSIFICATION METHODS - Muhamad Bayhaqi; Pipit Festi Wiliyanarti; Muhamad Ilham Hidayatullah; Yenny Rimbawan (Global Journal of Health Administration, 2025)
Objective: This study aims to evaluate the effectiveness and compare the accuracy of the Exponential Smoothing and Moving Average methods in forecasting pharmaceutical logistics needs at BB Hospital. Methods: A quantitative approach with a descriptive- comparative design was used. The data analyzed were monthly pharmaceutical logistics needs over a 12-month period, categorized into solid, liquid, and topical dosage forms. Forecasting was performed using Exponential Smoothing and Moving Average methods. Statistical analysis was conducted using ANOVA and independent t-tests to examine the significance of differences in forecasting accuracy. Results: Both methods showed satisfactory accuracy, as reflected in comparable values of Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The ANOVA test revealed a statistically significant difference between the methods (p = 0.000); however, the independent t-test showed a significance value of 0.756, indicating no significant difference in the average forecasting results between the two methods. Conclusion: Both Exponential Smoothing and Moving Average methods are effective for forecasting pharmaceutical logistics needs. Since no significant difference was found in their average forecasting performance, either method can be applied flexibly based on the hospital’s specific requirements. These findings provide practical insights for strategic decision- making in pharmaceutical inventory management.