In the context of industrial maintenance, accurate criticality assessment of equipment plays a crucial role in deploying predictive maintenance programs, optimizing resource allocation and enhancing overall operational efficiency by taking well-aligned decisions regarding the equipment. Traditional approaches often rely on single-criterion evaluations, which can lead to biased or incomplete assessments. This paper proposes a robust multicriteria approach that combines the Analytic Hierarchy Process (AHP) with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to provide a more comprehensive and nuanced criticality assessment. By integrating multiple dimensions of criticality - specifically reliability, production impact, and maintenance costs - this hybrid method aims to overcome the limitations of conventional techniques. The study utilizes real industrial data, demonstrating the practical applicability of the approach. Furthermore, the paper includes a comparative analysis using Pareto and statistical quartile analyses to validate the effectiveness of the proposed method against single-criterion assessments. This research contributes to the field of maintenance decision support by offering a more robust tool for prioritizing equipment and quantifying criticality, potentially leading to more informed maintenance strategies and improved industrial resource utilization. 

AHP, TOPSIS, ABC analysis, Criticality assessment, Decision, support, Maintenance optimization, Multicriteria, Pareto, Prioritization, Quartile analysis, Resources allocation.