| dc.description.abstract |
Nine Cafe faces significant challenges in managing its coffee inventory, often resulting in both shortages and surpluses of stock. Inaccurate demand estimation has led to considerable financial losses, including customer attrition and declining quality of stored coffee. This study aims to design and develop a coffee stock estimation application using the multiple linear regression method. This method is chosen for its ability to analyze the relationship between multiple independent variables that affect coffee inventory, such as daily sales, lead time, and customer visit frequency.By applying data mining in this system, Nine Cafe can optimize coffee purchasing and storage, reduce financial losses, and improve customer satisfaction. Additional benefits of this study include reducing storage costs, optimizing storage space, and providing a more data-driven basis for decision-making. The final outcome of this research is an application that can more accurately predict future coffee stock needs, thereby supporting operational efficiency at Nine Cafe and in the broader café industry.
Keywords:Inventory Management, Data Mining, Multiple Linear Regression, Stock Estimation, Nine Cafe. | en_US |
