Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

Abstract

This research aims to optimize wholesale inventory management at PT Primafood International Pasir Putih 2 by implementing leading algorithms, namely XGBoost, Support Vector Regression (SVR), and Random Forest. In the wholesale industry, effective inventory management plays a crucial role in maintaining smooth production processes and enhancing company profitability. Despite the acknowledged benefits of inventory management, there are aspects that remain not fully disclosed, particularly concerning demand uncertainty and market fluctuations. This study addresses these gaps by exploring the potential of these three algorithms. Experimental methods with a quantitative approach were employed to shape and prepare the dataset. The analysis and predictions' results using XGBoost, SVR, and Random Forest were evaluated using metrics such as Mean Squared Error (MSE), F1-Score, and Accuracy. The evaluation indicates that XGBoost and SVR exhibit optimal performance with low MSE values of 7714.446 and 119.315, high F1-Scores (0.92), and good accuracy levels (0.86 and 0.85), respectively. While Random Forest shows a higher MSE, it still delivers solid performance with an F1-Score of 0.89 and an accuracy rate of 0.81. These findings suggest that all three algorithms can be considered to enhance inventory management performance at PT Primafood International Pasir Putih 2, with significant potential benefits for overall industry development. This research provides valuable insights for decision-making at the business and industrial levels, highlighting the effectiveness of each algorithm in the context of predicting stock level.