<![CDATA[Inaccurate demand estimation often leads to inefficient distribution planning and increased transportation costs, particularly in perishable food distribution systems. This study aims to optimize beef distribution costs by integrating forecasting techniques with transportation optimization methods. Historical sales data from three distribution destinations Baros, Cimahi, and Dewi Sartika were analyzed using several forecasting methods, namely Single Moving Average, Weighted Moving Average, Exponential Smoothing, and Linear Regression. Forecasting accuracy was evaluated using the Mean Absolute Deviation (MAD) criterion to determine the most suitable method for each destination. The results show that Linear Regression generated the lowest MAD value for Baros (4.77), while Exponential Smoothing with α = 0.1 produced the most accurate forecasts for Cimahi and Dewi Sartika with MAD values of 35.19 and 34.16, respectively. The selected forecasting results were subsequently used as input for transportation optimization. The initial transportation allocation obtained using the North West Corner (NWC) method generated a total cost of Rp.63,060,000. Further optimization using the Stepping Stone and Modified Distribution (MODI) methods successfully reduced the transportation cost to Rp.41,170,000, representing a significant improvement in distribution efficiency. The findings confirm that integrating forecasting and transportation models can improve demand estimation accuracy and minimize logistics costs simultaneously. This study contributes to the application of industrial engineering approaches in logistics and supply chain management, particularly for perishable product distribution systems.]]>
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