Penentuan Strategi Peramalan Volume Barang Kiriman outgoing PT Pos Indonesia (Persero) KCU Purwokerto - Cindy Dwi Novita Sari; Ratih Windu Arini; Cindy Malinda Uscha (Lembaga Riset Ilmiah, 2025)
Pengelolaan kiriman outgoing merupakan aspek penting dalam menjaga kelancaran proses distribusi di PT Pos Indonesia (Persero) KCU Purwokerto (POS KCU Purwokerto). Berdasarkan data internal, rata-rata keterlambatan (overtime) pengiriman mencapai 3.56% dari total volume kiriman dari September 2024 hingga September 2025. Oleh karena itu dibutuhkan sistem perencanaan berbasis analisis data melalui peramalan volume kiriman outgoing agar perusahaan dapat mengantisipasi lonjakan permintaan dan mengoptimalkan kapasitas armada serta tenaga kerja. Peramalan menggunakan pendekatan time series dengan metode Naïve, Moving Average, Single Exponential Smoothing, dan Autoregressive Integrated Moving Average (ARIMA). Hasil pengujian menunjukkan bahwa model ARIMA (2,1,1) merupakan model terbaik dengan tingkat kesalahan terkecil, yaitu Mean Absolute Deviation (MAD) sebesar 1867.87, Mean Squared Error (MSE) sebesar 7846634.40, dan Mean Absolute Percentage Error (MAPE) sebesar 7.001% dan sesuai dengan pola data permintaan historis. Hasil peramalan ini memberikan acuan yang akurat bagi manajemen dalam pengaturan kapasitas armada, penjadwalan distribusi, dan alokasi tenaga kerja sehingga dapat meminimalkan keterlambatan pengiriman akibat ketidakseimbangan antara kapasitas dan beban kerja serta meningkatkan efisiensi dan keberlanjutan operasional perusahaan di masa mendatang. Outgoing shipment management is a crucial aspect in maintaining the smooth distribution process at POS Purwokerto Branch. Based on internal data, the average delay (overtime) in shipments reached 3.56% of the total shipment volume from September 2024 to September 2025. Therefore, a data analysis-based planning system is needed through forecasting the volume of outgoing shipments so that the company can anticipate spikes in demand and optimize fleet and workforce capacity. Forecasting uses a time series approach with the Naïve, Moving Average, Single Exponential Smoothing, and Autoregressive Integrated Moving Average (ARIMA) methods. The test results show that the ARIMA (2,1,1) model is the best model with the smallest error rate, namely a Mean Absolute Deviation (MAD) of 1867.87, a Mean Squared Error (MSE) of 7846634.40, and a Mean Absolute Percentage Error (MAPE) of 7.001% and is in accordance with historical demand data patterns. The forecast results provide an accurate reference for management in managing fleet capacity, distribution scheduling, and workforce allocation so as to minimize delivery delays due to imbalances between capacity and workload and increase the efficiency and sustainability of the company's operations in the future.