Benchmark-Guided Prescriptive Analytics for Menu and Margin Optimization in Multi-Location Restaurants

Jerry Brown, Uma Ajay Kumar Reddy PS

Abstract: Multi-location restaurant enterprises generate large volumes of operational data from point-of-sale systems, recipes, inventory platforms, procurement records, payroll systems, delivery channels, and external benchmark sources. However, many operators still rely on retrospective dashboards or isolated forecasts that describe performance without recommending feasible action. This paper presents a benchmark-guided prescriptive analytics framework for menu and margin optimization in multi-location restaurants. The framework integrates operational and benchmark data, estimates demand, ingredient cost volatility, labor burden, and stockout risk, converts peer-relative performance gaps into robust opportunity scores, and ranks feasible interventions under pricing, service, brand, supply, and labor constraints. The restaurant decision problem is formalized as a constrained stochastic optimization problem, and the paper specifies the data architecture, feature engineering pipeline, predictive modules, benchmark-gap scoring method, recommendation objective, evaluation protocol, and operational safeguards. Because the manuscript proposes a framework rather than reporting completed field results, it defines offline backtesting, ablation analysis, and matched-store pilots as the appropriate validation path. The contribution is a practical methodology for moving restaurant analytics from descriptive reporting and standalone prediction toward explainable, benchmark-aware prescriptive decision support.

Keywords: benchmarking, decision support systems, demand forecasting, inventory analytics, labor analytics, margin optimization, menu engineering, multi-location restaurants, prescriptive analytics, restaurant analytics

How to Cite?: Jerry Brown, Uma Ajay Kumar Reddy PS, "Benchmark-Guided Prescriptive Analytics for Menu and Margin Optimization in Multi-Location Restaurants", Volume 15 Issue 5, May 2026, International Journal of Science and Research (IJSR), Pages: 169-175, https://www.ijsr.net/getabstract.php?paperid=SR26502165049, DOI: https://dx.dx.doi.org/10.21275/SR26502165049

Download Citation: APA | MLA | BibTeX | EndNote | RefMan