| dc.description.abstract | Inventory control plays a pivotal role in ensuring product availability and operational
efficiency, especially within dynamic retail environments such as the Indonesian batik industry.
Traditional systems like the Fixed Order Quantity (Q-System) and Fixed Order Period (P-
System) are often selected based on managerial judgment, which may lead to suboptimal
decisions due to the variability in demand and product characteristics. This study introduces a
data-driven approach to classify inventory items into Q-System or P-System categories using
logistic regression. Historical inventory data from a batik retail business in Pekalongan,
encompassing demand, lead time, cost, and profitability for 15 high-performing products, was
used to build and train the model. Each product's inventory control strategy was first evaluated
based on total cost minimization, combining ordering cost, holding cost, and lost profit. The
best strategy (Q or P) per product was determined and used as a classification label. Logistic
regression was then applied, using normalized statistical and operational variables, to develop
a predictive model. The model achieved perfect classification performance on training and
testing datasets, with a Mean Squared Error (MSE) of 0, demonstrating its effectiveness. This
approach enhances decision-making transparency, optimizes inventory costs, and bridges
traditional inventory theory with modern predictive analytics. The findings offer practical
implications for retailers aiming to adopt interpretable, data-informed inventory strategies. | en_US |
