Implementasi Model ARIMA untuk Peramalan Reorder Point dalam Supply Chain Management - Alexandra, Andrea Cellista; Hartomo, Kristoko Dwi (Universitas Budi Darma, 2025)
This research analyzes the patterns and trends of reorder points in inventory management over a two-year period (2023-2024), utilizing weekly time series data generated from daily data resampling. The ARIMA (Autoregressive Integrated Moving Average) method was applied to forecast future reorder point values. An Augmented Dickey-Fuller (ADF) stationarity test revealed that the initial data was non-stationary but became stationary after a single differencing operation. Parameter identification using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots indicated that the ARIMA(1,1,1) model was the best choice, based on the lowest Akaike Information Criterion (AIC). Model accuracy was evaluated using Mean Absolute Percentage Error (MAPE), yielding a value of 0.02%, signifying an excellent level of prediction accuracy. Consequently, the ARIMA model is demonstrated to be reliable for forecasting reorder points, supporting more precise decision-making in inventory management.