Prediksi Retur Produk Farmasi dan Klasifikasi Risiko Menggunakan Model ARIMA
Abstract
Pharmaceutical product distribution faces specific challenges, particularly in managing product returns that can affect logistics efficiency and service quality. This study aims to predict the return quantity of pharmaceutical products using the ARIMA (Autoregressive Integrated Moving Average) model and to classify bad goods risk based on the prediction results. The data used consists of monthly return records from a Pharmaceutical Wholesaler (PBF) for a products—Paracetamol Syrup—during the period from January 2023 to December 2024. The research methodology includes data preprocessing, ARIMA model identification and estimation, residual diagnostics, forecasting, and risk classification. The results show that the ARIMA(1,1,1) model provides sufficiently accurate forecasts for Paracetamol Syrup, with predicted returns over the next six months falling into the medium-risk category. These findings offer valuable insights for pharmaceutical wholesalers to anticipate potential losses due to damaged or expired products and to design distribution strategies that are more responsive to return patterns.
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