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Home > College of Business > ICHRIE Research Reports > 1 > Iss. 1 (2016)

 

Publication Date

2016

Executive Summary

Purpose: Restaurant revenue management practices and profit optimization techniques are evolving into more complex data analysis processes. The “big data” revolution has created a wealth of information on revenue, pricing, key operational performance indicators, and various productivity/efficiency variables. Advanced research analysis that can identify these key factors across multiple operating units may be useful to restaurant managers unaccustomed to data analytics or those seeking a deeper understanding of unit-level business performance. The overarching goal of this study was to utilize mixed research methods across conceptually dissimilar units of a multi-unit chain restaurant, enabling researchers to build on the resulting outcomes and restaurant operators to apply it to optimize unit-to-unit profit.

Design/Methodology: A mixed research methodology was used to evaluate multidimensional operating efficiencies and labor productivity across multiple restaurant concepts. Data envelopment analysis (DEA), between-unit multidimensional analysis, and within-unit ratio analyses were utilized. While DEA was applied as a primary diagnostic tool to identify productivity/efficiency benchmarking factors, supplemental between- and within-unit measures provided more in-depth information regarding the effects of operating expense variables.

Findings: Restaurant analytics that effectively measure input and output variables between and within multiple units promote a data-rich organizational culture. For the small multi-unit organization that was the focus of this study, this is certainly the case. DEA diagnostic results to inform targeted analysis to particular units of operation indicated that all units are operating at maximum efficiency in terms of generating sales given the respective numbers of seats and square footages. However, subsequent analyses indicated multiple problems in terms of expense management. This same approach may help other operators optimize operations.

Originality/value: The proposed model provides restaurant operators the opportunity to identify the impact of different operating expense variables and their impact on overall profitability. The use of the polygon analysis in itself makes complex sensitivity analysis of certain operating variables to profit outcomes a much easier process. We recommend other operators perform similar analyses in order to enhance operational productivity.

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