The manuscript investigates the application of a hybrid approach combining Data Envelopment Analysis (DEA), Gaussian Analytic Hierarchy Process (AHP), and Pearson correlation adjustments to evaluate and optimise the operational efficiency of a food retail chain of restaurants. This methodology tries to identify and rank efficiency across the organisation's stores. Inputs evaluated include product costs and sales volume, while the outputs are revenue and profit. The Gaussian AHP was used to calculate inputs and output weights, reflecting Gaussian statistical analysis, which were then adjusted based on Pearson correlation to ensure greater alignment with the data correlations. DEA was implemented using Python, and Power BI was utilised for interactive result visualisation. This combination of tools provided a comprehensive approach. The findings highlight the effectiveness of this hybrid method for evaluating efficiency discrepancies and promoting optimisation across the organisation. Identifying units with the most significant potential for improvement and providing actionable insights for decision-making was possible. This work underscores the value of hybrid methodologies for internal benchmarking and qualitative techniques in operational efficiency studies.