Forecasting Service Sales Turnover Using Double Exponential Smoothing Method (case Study: Kinclong Sub)

Abstract

As the lifestyle of modern society develops, service companies are businesses in the form of services that sell specialskills in order to make it easier for customers, one of which is shoe washing services. However, the problem thatusually occurs is that the number of service requests is not certain which results in raw materials that sometimesaccumulate or run out every month, errors in predicting the sales turnover of shoe washing services can result inowners experiencing losses if the target raw materials are not appropriate.This study aims to create a forecasting system for the amount of turnover from each service sale by Kinclong Sub usingthe Double Exponential Smoothing method, so that the raw material needs needed every month can be optimal. Thebest turnover forecasting results for each service on Kinclong Sub for the following month, May 2022, were Rp.10.006,248 with a MAPE value of 17.86% for DeepClean services. And Unyellowing got Rp. 1493,374 with a MAPEvalue of 17.18 %. So it can be concluded that the service that has the most turnover for the next month is DeepClean,the forecasting results can make it easier for Kinchlong Sub shop owners to provide raw materials from services thatare most in demand by consumers.