Implementation of AED with Gaussian AHP and Pearson's Correction for Efficiency Evaluation in a Restaurant Chain

This work proposes a hybrid approach to evaluate operational efficiency in a restaurant chain, combining Data Envelopment Analysis (DEA), Gaussian Hierarchical Analytical Process (Gaussian AHP) and adjustment of weights with Pearson's correction. The inputs considered were product costs and sales volume; The output considered was gross profit. The Gaussian AHP was used to assign weights to the criteria, reflecting the statistical distribution of the data, with subsequent adjustment via Pearson's correlation for greater alignment with the empirical relationships. The proposed model (DEA-AHP/GS) was implemented in Python. The approach made it possible to identify discrepancies in efficiency between units, pointing out opportunities for improvement and subsidizing decision-making. The findings reinforce the potential of hybrid methodologies for internal benchmarking and improving operational efficiency in complex organizations.