Analisis Perbandingan Metode Forecasting Dalam Memprediksi Permintaan Celana Salur ISTWEAR - Selvy Rahmadani; Soviatul Azkiya; Dhea Rahma Dewi; Somadi Somadi (JURNAL ILMIAH EKONOMI, MANAJEMEN, BISNIS DAN AKUNTANSI, 2026)
Abstract. Small-scale fashion businesses like ISTWEAR often struggle to estimate market demand due to erratic sales fluctuations; consequently, unplanned production risks leading to either overstocking or product shortages. This study aims to estimate the demand for ISTWEAR’s striped trousers (*Celana Salur*) for June 2026 using forecasting techniques. Sales data from January to May 2026 for three color variants-RSN Blue, Pink, and Black-were analyzed using Moving Average, Exponential Smoothing, and Linear Regression methods, with accuracy evaluated via MAD, MSE, and MAPE. The results indicate that Linear Regression yielded the lowest MSE values ​​across all variants, making it the optimal method; it produced demand estimates of 1,583 units (RSN Blue), 1,089 units (Pink), and 1,128 units (Black) for June 2026. These findings confirm that Linear Regression is the most suitable forecasting method for data exhibiting consistent trend patterns and can serve as a practical basis for production planning decisions in fashion businesses.