Application of Multi-Criteria Decision-Making Techniques in a U-Bolt Production Process Using Analytical Hierarchy Process (AHP)

Abstract

Manufacturing decision environments are characterized by complexity arising from the simultaneous consideration of multiple conflicting performance objectives. In fastener production systems such as U-bolt manufacturing, process selection is often influenced by subjective managerial judgment rather than structured quantitative evaluation. This study develops a multi-criteria decision-support framework based on the Analytic Hierarchy Process (AHP) to identify the optimal production strategy for U-bolt manufacturing systems. The decision model integrates cost efficiency, product quality, lead time performance, and machine utilization as primary evaluation criteria for comparing manual, semi-automated, and fully automated production alternatives. Expert knowledge was collected through structured pairwise comparison questionnaires using Saaty's relative importance scale. Priority weights were derived using eigenvector approximation techniques, and judgment consistency was verified through consistency ratio analysis. To enhance model robustness under uncertainty, a Fuzzy-AHP extension was implemented using triangular fuzzy number representation and centroid defuzzification. Sensitivity analysis was conducted to evaluate ranking stability under parameter perturbation scenarios. Results demonstrate that automated production systems provide superior overall performance relative to alternative configurations. The decision model maintained acceptable consistency levels and exhibited strong robustness across sensitivity simulations. The proposed framework provides a systematic approach for manufacturing process optimization and supports data-driven strategic decision-making in mechanical production environments. The study contributes to multi-criteria decision-making literature by extending hierarchical decision modeling to fastener manufacturing systems and providing a replicable methodology for industrial process evaluation.