Evaluation of Business Intelligence Tools for the Logistics Sector With Hesitant Fuzzy Hybrid Mcdm Methods

Abstract

Business intelligence (BI) tools have become essential for logistics companies due to their ability to facilitate improved decision-making and operational efficiency, as they provide comprehensive data analysis capabilities that contribute to more informed and timely decisions. This study utilizes the critical task of selecting the most suitable BI tool for an e-commerce logistics company, focusing on assessing various alternatives, including open-sourced and proprietary software. The research employs innovative integrated multi-criteria decision-making (MCDM) methods. The integrated approach primarily uses HFAHP to determine related criteria weights. Then, utilizing these determined weights, Hesitant Fuzzy Preference Ranking Organization Method for Enrichment Evaluation II (HF-PROMETHEE II), Hesitant Fuzzy Evaluation Based on Distance from Average Solution (HF-EDAS), and Hesitant Fuzzy Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form (HF-MULTIMOORA) methods are implemented to compare the alternatives. Five different Business intelligence tools were evaluated concerning eleven comprehensive criteria. This exhaustive evaluation, conducted by five business intelligence experts, aims to guide organizations in selecting an optimal BI tool that enhances data analysis, decision-making processes, and overall business efficiency.