Multiple regression to optimize hotel provisioning: a model with a regulatory, sustainable and digital approach.

Abstract

This study proposes a multivariable predictive model aimed at optimizing secure provisioning in hotel facilities, integrating advanced statistical tools to identify the factors that significantly influence logistical efficiency. Through multiple regression analysis, variables such as average delivery time, regulatory compliance level, proportion of sustainable products, inventory turnover, process digitalization, and hotel occupancy are examined to construct a composite index of operational efficiency. The methodology employed enables validation of the statistical significance of each variable, assessment of multicollinearity, and estimation of the relative impact of each component on the continuous improvement of the logistical system. This approach not only provides technical rigor but also incorporates territorial sensitivity and institutional ethics, reframing logistics as a symbolic process that links service quality, sustainability, and regulatory traceability. The model is presented as a strategic tool for decision-making in complex tourism contexts, where uncertainty, demand variability, and regulatory pressure demand adaptive and scientifically grounded solutions. The proposal bridges the technical and the symbolic, offering an evolutionary reading of hotel provisioning that transcends operational concerns and aligns with a logic of institutional, territorial, and emotional transformation.