<![CDATA[This research aims to investigate how sales type, product type, and price influence net profit in an F&B outlet using a Data Warehouse system. A quantitative method was applied by integrating daily transaction data into a Data Warehouse architecture built through an Extract, Transform, and Load (ETL) process, allowing the data to be more organized and easier to analyze. To address heteroskedasticity and obtain more reliable coefficient estimates, multiple linear regression with HC3 robust standard errors was used. The results show that price and sales type have a significant and positive effect on net profit, while product type shows mixed effects depending on the category. The regression model, with an R² value of 0.994, indicates that these three variables explain most of the variation in net profit. Overall, the findings highlight how structured data processing through a Data Warehouse can support profitability analysis and improve decision-making in F&B operations.]]>
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