A Web-Based Intelligent Decision Support System Integrating Statistical Pre-Validation and AI-Assisted Interpretation for Multicriteria Decision Analysis

Abstract

Multicriteria decision analysis (MCDA) methods are widely used to evaluate alternatives under conflicting objectives, yet the statistical validity of input decision matrices is rarely verified prior to method application. Existing tools focus on executing MCDA algorithms without offering integrated statistical pre-validation or automated interpretation of results. This paper presents the design, implementation and evaluation of an open-source, web-based intelligent decision support system that integrates statistical pre-validation and AI-assisted interpretation for MCDA decision matrices. Developed following the Design Science Research methodology and implemented in Python with a Streamlit interface, the system encapsulates domain knowledge through six analytical modules: descriptive statistics, normality testing, correlation analysis, ANOVA with post hoc testing, linear regression with diagnostic assessment and Monte Carlo simulation. An AI-powered interpretation engine provides contextualised explanations and interactive consultation, encoding statistical reasoning that would otherwise require specialist expertise. The software is registered with the Brazilian National Institute of Industrial Property (INPI, BR512026000340-7) and publicly available on GitHub. The system was evaluated through a case study on intermodal transport selection for ethanol logistics in Brazil, involving four alternatives assessed across five criteria. The expert system identified strong intercriterion correlation (r = 0.99), validated ranking robustness via Monte Carlo simulation and generated AI-driven interpretations consistent with domain expert judgement. The proposed system advances the field of intelligent decision support by codifying statistical pre-validation expertise into an accessible, AI-augmented tool that bridges the gap between MCDA method execution and the rigorous statistical reasoning required for scientifically sound decision-making.