ANALISIS PERBANDINGAN METODE FORECASTING DEMAND UNTUK OPTIMASI PERSEDIAAN BAHAN BAKU DI DIETGO KITCHEN

Abstract

Choosing an appropriate demand forecasting approach plays a vital role in ensuring effective management of raw material inventories. Yet, catering businesses face unpredictable and constantly evolving market conditions, which makes it extremely difficult to achieve accurate forecasts. Furthermore, no single forecasting technique can universally address raw material inventory management challenges. As such, selecting an appropriate method depends on the specific context. This study applies both the Autoregressive Integrated Moving Average (ARIMA) model and Exponential Smoothing, with their performance assessed using accuracy metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that Exponential Smoothing delivers more accurate forecasts for the baked grilled chicken menu, while ARIMA performs better for other dishes including beef sei, chicken sei, beef slices, sambal bawang, and lamb sei. By implementing these tailored forecasting approaches, DietGo Kitchen can optimize supply chain operations, minimize material waste, and enhance customer service quality.