Predicting Selling Product of Single Variant Using Arima, Trend Analysis, And Single Exponential Smoothing Methods (Case Study: Swalayan Xyz Store)

Abstract

The availability of goods in a store is very important. Predicting is a tool that is used to help predict the data needed by an organization or company. The purpose of this study is to predict the sale of a product that has a high risk of damage and fast expiration time by using existing techniques in forecasting. Forecasting can also be used to make product stock safety at the XYZ Supermarket. The results of this study are in the form of forecasting the sale of a product in a store by using the existing methods of forecasting that are adjusted to the sales data of one product. The method used in forecasting is the ARIMA method, Trend Analysis, and Single Exponential Smoothing. Trend Analysis Method has the highest accuracy with MAPE 9.91%, which means that forecasting is very good, compared to ARIMA with MAPE 37.21% and Single Exponential Smoothing with MAPE 10%. So that the results of the Trend Analysis forecasting will be used for the decision-making process about forecasting stockpiles and stock safety in the future.

Keywords

• predicting
• arima
• trend analysis
• single exponential smoothing
• single variant