Sales Stock Forecasting System Using Trend Moment Method (Study Case: Warcam Store)

Abstract

The Warcam store is a store that sells various frozen food products since 2019. The sales process at this Warcam store often experiences a shortage or excess stock of frozen food which causes losses for shop owners. This happens because Warcam Stores have not know how to calculate inventory. This study will discuss how the stages of making a forecasting system using the website-based trend moment method can help to predict frozen food stocks. The data used is data from 2019 to 2020 with in 360 transactions. The stages begin with business understanding, data understanding, and data preparation. Build a model using flowchart and perform a simulation of product forecasting calculations until an evaluation. This system was created using the PHP programming language and MySQL database with the CRISP-DM (Cross Industry Standard Process For Data Mining) approach. From the results of testing the system that has been made, this system can predict frozen food stocks with an error value of 14.52%. This error states that the forecast is still not correct, there are still excess and or lack of stock so that further research needs to be carried out using other methods.