Fuzzy based intelligent decision support model for restaurant menu management

Abstract

Restaurants operate in a dynamic highly competitive business environment with slim profit margin, changing customers, and costs incurring from materials and labour. Despite a solid demand for restaurant meals, the restaurant industry has a high failure rate, especially within the first three years of commencement. As the most important marketing, sales, and operational tool, a menu list must be well designed and well planned for profitability and competitive advantage. Menu Analysis (MA) or Menu Engineering (ME) refer to the broad range of techniques and procedures applied for effective marketing and operational decisions on menus. Choice of dishes is the food and beverage manager or the decision maker’s (DM) challenge which is a critical strategy decision. Quantitative or precise measures required for accurate evaluation of the effectiveness of a menu item is difficult to obtain. This pioneer study presents a Menu Management Decision Support Model (MMDSM) that applies Fuzzy Multicriteria Decision Making (FMCDM) approach to deal with the inherent imprecision in input using qualitative linguistic input values and obtain reliable outputs with increased decision options. Improving the widely used ME model with two inputs, the MMDSM includes four input parameters of Profit Factor, Popularity, Contribution Margin, and Sales Price related to each Menu Item. The MMDSM is successfully tested with a case study having one hundred and sixty-one Menu Item details from a U.S. restaurant. The results are verified by conducting a sensitivity analysis and the model can be developed into a commercial application.