Model to Predict Inventory Demand in Retail SMEs Using CRISP-DM and Machine Learning

This study addresses efficient inventory management, a critical concern for small and medium-sized enterprises (SMEs) in the retail sector, affecting their operational efficiency, cost management, and competitiveness. Despite its global prevalence, many SMEs lack efficient solutions that take advantage of available technology and information. The objective of this study is to train machine learning models to predict inventory demand in SMEs, addressing their unique challenges and limitations. The Cross Industry Standard Process for Data Mining methodology is employed to develop the model using four machine learning algorithms: Random Forest (RF), Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost) and Decision Tree (DT). The methodology consists of five phases: business understanding, data understanding, data preparation, modeling and evaluation. For the training phase, cross-validation was used on a dataset consisting of 16,071 records collected from July 25, 2023 to March 29, 2024 from a Peruvian SME, considering a total of 14 variables. The results highlight XGBoost as the algorithm that best fit our records with an R² of 0.82.