Update Artikelilmiah: 50401

WINOLA FARHANI

NIM

Judul Artikel

Abstrak (Bhs. Indonesia)

Perusahaan Kerupuk Hikmat Si Geboy Tea Tasikmalaya mengalami gangguan produksi akibat keterlambatan pengadaan bahan baku dan prediksi permintaan yang masih berbasis intuisi pemilik. Penelitian ini bertujuan menganalisis metode peramalan paling akurat untuk meramalkan permintaan kerupuk dan menentukan strategi pengelolaan rantai pasok optimal melalui perhitungan safety stock (SS) dan reorder point (ROP). Data penjualan harian tahun 2022–2024 dianalisis menggunakan Moving Average, Exponential Smoothing, Linear Regression, dan ARIMA. Evaluasi akurasi menunjukkan ARIMA (1,0,0) terbaik dengan MAPE 6,38%, MAE 53,89, dan MSE 3.886.554.926. Berdasarkan ARIMA, perhitungan safety stock dan ROP untuk lead time 1 hari adalah 438 pcs dan 28.889 pcs, sedangkan untuk 2 hari adalah 620 pcs dan 57.542 pcs. Simulasi service level menunjukkan efisiensi safety stock hingga 46% dibanding baseline perusahaan tanpa menurunkan kualitas layanan. Hasil ini membuktikan bahwa peramalan berbasis data, khususnya ARIMA, dapat meningkatkan efisiensi operasional, mengurangi risiko kekurangan bahan baku, dan membantu perusahaan mempertahankan tingkat layanan pelanggan secara optimal.

Abtrak (Bhs. Inggris)

Kerupuk Hikmat Si Geboy Tea Tasikmalaya Company experienced production disruptions due to delays in raw material procurement and demand forecasts that were still based on the owner's intuition. This study aims to analyze the most accurate forecasting method for predicting cracker demand and determine the optimal supply chain management strategy through safety stock (SS) and reorder point (ROP) calculations. Daily sales data from 2022–2024 were analyzed using Moving Average, Exponential Smoothing, Linear Regression, and ARIMA. Accuracy evaluation showed that ARIMA (1,0,0) was the best with MAPE 6.38%, MAE 53.89, and MSE 3,886,554,926. Based on ARIMA, the safety stock and ROP calculations for a 1-day lead time are 438 units and 28,889 units, while for a 2-day lead time, they are 620 units and 57,542 units. The service level simulation shows a safety stock efficiency of up to 46% compared to the company's baseline without compromising service quality. These results demonstrate that data-driven forecasting, particularly ARIMA, can enhance operational efficiency, reduce the risk of raw material shortages, and help companies maintain optimal customer service levels.

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