Using Bayesian BWM to Analyze Elevator Performance Requirements for Collaborative Product Innovation Design

Abstract

This study addresses the critical need to evaluate elevator performance requirements within collaborative product innovation systematically. Leveraging insights from sales, design, and maintenance experts, the research identifies and prioritizes critical performance dimensions, including safety, design, service, and technological innovation. By employing the Bayesian Best-Worst Method (Bayesian BWM), the study overcomes the limitations of traditional decision-making approaches, offering robust and consistent results through probabilistic analysis. The findings highlight "mechanical and structural safety" as the paramount criterion, emphasizing its pivotal role in ensuring elevator reliability. Key factors, such as operational efficiency, economic performance, and maintenance, further inform actionable strategies to optimize elevator design and manufacturing processes. This research contributes a structured framework for performance evaluation and fosters collaboration among industry stakeholders, enhancing innovation and sustainability in the elevator industry.