Determining the Method of Sales Forecasting of Cheese Products at PT XYZ

Abstract

Abstract Sales forecasting must be done to meet consumer demand for cheese products. PT XYZ is one of the companies that distributes cheese products and makes forecasts by averaging three months of sales. The average value of the resulting deviations is close to the forecast limit, which is said to be quite good, around 31% -39%. In this project sales forecasting of three cheese products was carried out using the Exponential Smoothing method: Pegel Classification and Autoregressive Integrated Moving Average (ARIMA). Both methods are used to compare the value of accuracy between the two methods with the company. One of the two methods with the highest accuracy value will be recommended for forecasting sales to companies. The results of the project show that the two methods on average have a better accuracy value than companies where forecasting using the ARIMA method has a better accuracy value than the Pegel Classification so the ARIMA method is recommended in sales forecasting. The accuracy value of the three products using the ARIMA method is about 6-10% higher than the Pegel Classification.