Hoque, Mohd Jahidul and Akter, Fahmida and Mohammad, Abdul Rahman (2019) Data-Driven Decision Support System for Restaurant Business Optimization. American Journal of Economics and Business Management, 2 (2). ISSN 2576-5973

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Abstract

The restaurant industry produces vast amounts of operational and customer data which can be used to enhance business performance and decision-making. Conventional restaurant management practices are based on intuition and hands-on observations that can result in sub-optimal pricing, mediocre food quality, low customer satisfaction, and poor operational planning. The aim of this research is to present a Data-Driven Decision Support System for Restaurant Business Optimization for decision making in restaurant businesses based on data analytics and machine learning techniques. The study employs data from the “Burritos in San Diego” data set which includes the customer ratings, dimensions of food quality, restaurant attributes, restaurant prices, and restaurant business performance measures for Mexican restaurants in San Diego. Data is reviewed to find the main determinants that affect the satisfaction of customers and the overall performance of the restaurant. Different data analysis methods are used, such as exploratory data analysis, predictive modeling, and clustering techniques, to analyze the relationships between price, food quality, customer preferences, and restaurant ratings. The concept of a decision support system is to help restaurant managers to optimize the quality of their restaurant menus, improve customer satisfaction, improve their pricing decisions, and make their restaurant more efficient. Various machine learning models like Decision Tree, Random Forest, and Linear Regression are considered for predicting the performance of restaurants and outcomes of customer evaluations. The system also delivers business intelligence data that facilitates data-driven planning and decision making. The results from this study are likely to show the capabilities of using analytics-based decision support systems to boost the performance of a restaurant business and customer experience. The proposed framework can be used as a good practice model to implement data driven management practices in small and medium-sized restaurant businesses for sustainable growth and competitive advantage.

Item Type:	Article
Uncontrolled Keywords:	Restaurant Business Optimization, Decision Support System, Data Analytics, Machine Learning, Customer Satisfaction Analysis and Business Intelligence
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	18 Jul 2026 14:58
Last Modified:	18 Jul 2026 14:58
URI:	http://eprints.umsida.ac.id/id/eprint/16781

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