Maximizing Production Throughput Using Real-Time Data Analytics and Predictive Maintenance

Abstract

This study examines the impact of integrating real-time data analytics and predictive maintenance on maximizing production throughput in manufacturing sectors, including automotive, electronics, and pharmaceuticals. Using a quantitative approach, data were gathered from 30 companies with established predictive maintenance and real-time data systems. Results indicate a 25% increase in production throughput and a 30% reduction in unplanned downtime across companies implementing both technologies. Real-time data analytics enabled responsive decision-making, while predictive maintenance facilitated timely equipment interventions, thereby enhancing operational efficiency and cost-effectiveness. The findings support digital transformation theory, showcasing the value of transitioning from reactive to proactive production management. However, barriers such as high initial costs and a lack of technical expertise were noted, particularly for smaller firms. Practical recommendations include phased implementation and targeted employee training to optimize technology adoption. The study contributes to the field of production management by highlighting the potential of combined digital solutions to enhance throughput and supply chain resilience, with suggestions for broader cross-industry applications and integration with artificial intelligence to optimize processes further.