Applying ergonomic principles in work tool design for manufacturing industries is a crucial factor in improving productivity while maintaining worker health. This research aims to analyze the effectiveness of adaptive work tool design models based on cognitive and physiological ergonomic principles, identify interaction patterns between workstation design and operational performance, and develop a conceptual framework for integrating ergonomic principles into production cycles. The research method adopts a cognitive-physiological approach with qualitative analysis of human-machine interactions, biomechanical simulations using digital human modeling, and muscle load measurements through electromyography. Implementation was conducted using a participatory ergonomics approach and IMU sensor-based real-time monitoring systems. Results show that using materials with controlled deformation capabilities (15-20%) in work tools reduces muscle work by up to 27%, while adaptive automation system integration improves assembly accuracy by 18%. Workstations with ergonomic adjustments increase assembly speed by an average of 12%, and low-cost ergonomic interventions effectively improve productivity by 11-15% in resource-limited environments. Longitudinal analysis reveals that evidence-based ergonomic investments yield a 230% ROI through increased productivity, reduced injury compensation costs, and decreased employee turnover. IMU-based posture monitoring systems integrated with adaptive feedback loops reduced musculoskeletal disorder incidents by up to 41%. In conclusion, ergonomic optimization based on cognitive-physiological principles creates synergy between production efficiency and worker well-being, making it an essential component in achieving sustainable productivity.

Industry 4.0 has driven significant transformation in manufacturing maintenance strategies, particularly in the implementation of Total Productive Maintenance (TPM). This research examines the paradigm shift from conventional TPM methods to Artificial Intelligence (AI)-based approaches, utilizing a qualitative methodology with phenomenological design and participatory observation in manufacturing environments. Results indicate that integrating AI with TPM has transformed maintenance approaches from reactive to predictive, enabling real-time anomaly detection, predictive maintenance, and maintenance schedule optimization that significantly improve Overall Equipment Effectiveness (OEE). Despite providing benefits such as reduced downtime and maintenance costs, AI-based TPM implementation faces challenges including data fragmentation, skilled human resource requirements, and organizational culture resistance. The development of collaboration between industry and academic institutions, as well as investment in edge computing infrastructure, are identified as key to maximizing the potential of AI-based TPM in enhancing operational sustainability in manufacturing.