Decision Support System for Determining Signature Menus Using the Integration of BMW, MOORA, and Copeland Scores

Abstract

Menu optimisation is a crucial yet challenging challenge for business sustainability because the speciality coffee sector is marked by dynamic consumer tastes and a high degree of product diversity. This study tackles a basic operational issue at Inti Coffee, where a subjective, intuition-based method is currently used to identify "signature offerings"the important menu items that ought to be given priority for promotion, inventory, and resource allocation. This current process, which mainly depends on the owner, operational manager, and lead barista's intuition, is intrinsically vulnerable to individual cognitive biases, personal taste preferences, and inconsistent evaluation, which frequently results in less than ideal menu performance and lost revenue opportunities. The main goal of this research is to develop and deploy an online Decision Support System (DSS) that offers a transparent, data-driven, and organised framework for objectively ranking menu items in order to get around these restrictions. A hybrid multi-criteria decision-making (MCDM) technique is included into the suggested system to guarantee group unanimity and robustness. In order to minimise pairwise comparison inconsistencies and capture the knowledge of the three primary decision-makers, the Best-Worst Method (BWM) is first used to systematically extract the relative relevance weights of six different evaluation criteria. Second, a thorough dataset of 500 real sales transactions is used to assess and rank 51 menu alternatives using the MOORA (Multi-Objective Optimisation on the basis of Ratio Analysis) method. Both financial parameters (total items sold, HPP or cost of goods sold, and profit margin) and operational characteristics (uniqueness of taste score, preparation time, and ingredient lifetime) are included in the evaluation criteria. In order to successfully resolve any potential conflicts between decision-makers, the Copeland Score is finally used to combine the individual preference rankings into a single, collective group score. This study makes three main contributions: first, it develops a novel integrated DSS framework that integrates BWM, MOORA, and Copeland Score into a single unified workflow specifically designed for signature menu selection; second, it involves three different stakeholders (owner, operational manager, and head barista) in the group decision-making process, ensuring that the final recommendation reflects a balanced consensus rather than individual bias; and third, it creates a fully functional web-based system with statistical validation tools that empirically verify the accuracy and dependability of the recommendations using Spearman correlation, RMSE, and overlap ratio against actual customer preferences. The creation of a useful, web-based DSS that turns menu curating from an art to a science and offers a reproducible model for other food and beverage businesses is the main contribution of this research. With a MOORA score of 0.249180 and a Copeland Score of 50, the interim results show that the system is able to identify "Kopi Susu Aren" as the best-performing signature item. A poll involving 130 participants was carried out to verify the system's output against human judgement in the actual world. In addition to a low Root Mean Square Error (RMSE) of 5.5734 and an overlap ratio of 66.7%, the results show a strong positive correlation (Spearman's rho = 0.9238) between the system's ranks and participant feedback, proving the system's high accuracy and practical applicability. In order to provide scalability and usability for continuous operational choices, the DSS is implemented using a combination of PHP Native, Python, MySQL, and a testing dashboard based on Streamlit.