Optimization of Bread Inventory Requirement Estimation Using Multiple Linear Regression Method at Coffeebox Medan - Lubis, Harmoko; Manik, Aditiarno; Sitorus, Ade Sartika (Jurnal ICT : Information and Communication Technologies, 2024)
Proper bread inventory management is very important to avoid shortages or waste of stock at Coffeebox, which has been experiencing problems in estimating inventory needs due to manual data management. This study aims to develop a bread inventory requirement estimation model using the multiple linear regression method with bread ordering and bread sold variables. The data used consists of 29 observation samples which are processed through regression calculations to obtain regression coefficients and linear equations. The results showed that the resulting model has the formula Y = 3.152506333 + 0.03249542 X₁ + 0.012684868 X₂, with an estimated bread inventory requirement of 107 boxes based on daily demand and sales data. The implication of these findings is that multiple linear regression models can be used to optimize stock management and improve operational efficiency at Coffeebox. This study has limitations in terms of the variables used, so future research is recommended to expand the model by considering other external variables or using more complex methods such as machine learning algorithms to improve prediction accuracy.