An Efficient Neutrosophic Approach for Evaluating Possible Industry 5.0 Enablers in Consumer Electronics: A Case Study

Abstract

With the use of cutting-edge technologies like artificial intelligence (AI), robotics, and the Internet of Things (IoT), Industry 5.0 represents a breakthrough move towards a sustainable and human-centered industrial future. Industry 5.0 endeavors to transform industries such as consumer electronics by emphasizing sustainability and collaboration, in contrast to its predecessors, who only concentrated on automation and efficiency. Along with improved manufacturing efficiency and product innovation, this change in the consumer electronics sector also redefines the human-machine interaction. This paper proposes a novel hybrid integrating model that combines the Entropy Weight Method (EWM), Best-Worst Method (BWM), and an acronym in Portuguese for Interactive Multi-criteria Decision Making (TODIM) using single-valued neutrosophic trapezoidal numbers to evaluate Industry 5.0 enablers. The EWM provides objective weight for criteria, while the BWM captures the subjective preferences of decision-makers. The TODIM method ranks alternatives based on these weighted criteria using single-valued neutrosophic trapezoidal numbers, which effectively handle uncertainties and imprecise information inherent in decision-making processes. The proposed hybrid model effectively evaluates the Industry 5.0 consumer electronics sector using an empirical study emphasizing personalization, sustainability, resilience, and smart manufacturing criteria. The model enhances decision-making by balancing objective metrics with subjective preferences, thus guiding stakeholders toward informed and sustainable technological investments. The hybrid EWM-BWM-TODIM method with single-valued neutrosophic trapezoidal numbers demonstrated robustness in accommodating subjective and objective criteria weights. Sensitivity analysis revealed variations in aggregation methods and θ values significantly influenced final rankings, emphasizing the method's adaptability and responsiveness to decision-maker preferences and environmental changes.