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All Journal Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis
Bustomy, Mochammad
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Agriculture, Biological Sciences & Forestry

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Forecasting Permintaan Produk Keripik Sayur Mix di PT. IPL Menggunakan Metode Time Series Bustomy, Mochammad; Bakhtiar, Ary
Mimbar Agribisnis : Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis Vol 11, No 2 (2025): Juli 2025
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ma.v11i2.18671

Abstract

PT. IPL is a company engaged in the horticultural processing industry, especially the production of mixed vegetable chips. Fluctuating product demand is a challenge for companies in optimizing production and inventory to avoid excess or shortage of stock. This study aims to forecast product demand using the Time Series method, namely Moving Average and Exponential Smoothing, in order to determine the most accurate forecasting method. This study uses a quantitative descriptive approach by analyzing historical sales data in 2023–2024. The calculation was carried out using POM QM software for Windows, with the application of Moving Average (length 2) and Exponential Smoothing with various alpha values (α = 0.1; α = 0.5; and α = 0.9). The results showed that  the Exponential Smoothing method  with α = 0.5 provided the most accurate results, with a Mean Absolute Deviation (MAD) value of 353,301, Mean Squared Error (MSE) of 256,729.5, and Mean Absolute Percentage Error (MAPE) of 25.17%. Based on the forecast results, the estimated demand for December 2024 is estimated to reach 1,678,886 kg. The application of the Exponential Smoothing method α = 0.5 improves the production efficiency and stock management of PT. IPL through proper production planning, adjustment of work schedules, control of raw materials with a Just-In-Time system, and stock management that reduces costs and risk of spoilage. By applying the right forecasting methods, companies can optimize production, avoid wasting resources, and increase operational efficiency and profitability.
Co-Authors Ary Bakhtiar