Business Intelligence of Automotive Parts

Rostianingsih, Silvia and Setiawan, Alexander (2021) Business Intelligence of Automotive Parts. In: International Conference on Engineering, Science and Technology, 24-10-2021 - 24-10-2021, Chicago - USA.

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Abstract

Business Intelligence (BI) is widely used for reporting, visualization, and predictive analysis. Tools are used to turn data into useful information for making business decisions. Company of automotive parts sales is able to discover what the most favorite spare part is or which customer with the highest sales. Business intelligence helps companies study customer needs to gain deeper insight. In this research, data is extracted from flat files and transformed into dimensional modeling. Data is taken from the part shop and workshop sales transactions from one of the automotive industries in Indonesia. After creating a dimensional model, we construct data visualization and predictive analysis. Data visualization is created for analysis of part shop and workshop sales transactions. The predictive analysis is using simple linear regression modeling to calculate the number of days to fulfill the order goal. Business Intelligence helps the company find data insight such as there is an anomaly in the transaction. The visualization is using data from 2016-2020. Within five years, August is always the highest sales for the part shop sales. Whereas in average, January was the highest month for workshop sales. Although there was a new part that is launched in 2020, it became the second-highest sales. However, that part is a substitute item from another part that hold the highest sales in the previous years. During the pandemic, some large distributors are experiencing a decline in sales, while the small to middle distributors still have stable sales. This research also helps predict the number of days to fulfill the order goal.

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