<p>This study examines the application of the analytic hierarchy process (AHP) to optimize standard work hours (SWH) in the manufacturing industry, addressing the critical balance between production timeliness and quality. SWH are essential for efficient manufacturing processes, which require precision and effective time management. By employing AHP, this study prioritizes criteria such as labor efficiency (LE), overall efficiency (OE), job weight (JW), weld volume (WV), and skill level (SL) through pairwise comparisons, effectively integrating both qualitative and quantitative data to support informed decision-making. Key findings reveal that LE and OE are the primary determinants of SWH, highlighting the significance of workforce productivity and comprehensive process optimization in manufacturing. AHP’s Hierarchical structure allows for an effective evaluation of JW, WV, and SL, ensuring that decisions align with industry demands. Specifically, LE emerges as the top priority, emphasizing the importance of efficient workforce management in meeting production deadlines, while OE underscores the need for holistic process optimization. Future research directions involve leveraging dashboard technologies for real-time monitoring and integrating Industry 4.0 innovations to boost efficiency and competitiveness. The use of real-time analytics can centralize SWH metrics, facilitating proactive decision-making and resource optimization. In conclusion, this study identifies the critical determinants of SWH in manufacturing, providing pathways for future advancements. By aligning SWH strategies with emerging technologies and fostering collaboration, manufacturers can navigate complexities, meet market demands, and achieve sustainable growth, with AHP demonstrating significant potential in driving operational excellence and strategic decision-making.</p>