This study contributes to the literature on menu analysis by applying metafrontier-to-data envelopment analysis (MDEA) to the restaurant industry. MDEA, incorporating multiple outputs and inputs, was used to distinguish retainable, improved and undesirable items on a menu, and to identify differences in the proficiency level among heterogeneous meal categories based on the metatechnology ratio (MTR). The findings indicate that the MTR of meal categories (entrées and appetizers) prepared in each restaurant was inferior to the value of meal categories produced in a central kitchen. Slack-based improvements suggested from MDEA can improve the efficiency of menu items.
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