Sales prediction of Four Wheelers Unit (4W) with seasonal algorithm Trend Decomposition with Loess (STL) in PT. Astra International, Tbk

Abstract

Corporate Information System and Technology (CIS&T) division in PT Astra International Tbk is digitizing all business lines. Therefore, the company started to utilize machine learning to predict four wheelers (4W) sales in 2017. Data used were car sales data from 2014 to 2016. The data were obtained from company’s data warehouse (DW). Further, to develop application, firstly, Minitab was used to compare algorithm in modelling phase. Then, Azure Machine Learning Studio (Azure ML) was utilized to build prediction model based on algorithm chose in first phase. Finally, visualization was developed using Power Business Intelligence (Power BI). Time series algorithm used to solve the case is Seasonal Trend Decomposition with Loess (STL). While, machine learning model uses R library, and process flow module in Azure ML. The STL prediction model yield 9.38% error. Visualization of result is divided into two sections filtered by three parameters type, color and area. The first section in dashboard is sales data report showing percentage of type, color of cars and deployment of 4W units in Indonesia through ArcGIS Maps. The second section is forecast result containing chart of x axis to represent year and y axis to represent number of units. Further, the chart provide historical, current and forecast information. The research contributes to illustrate development of prediction using machine learning in industrial environment.