Predictive Analytics for Manufacturing Maintenance Improvement

This technical report outlines an AI use case aimed at enhancing manufacturing workflows through predictive analytics. It describes the challenges faced by a Bulgarian food company, which experienced frequent equipment breakdowns due to a reliance on reactive maintenance and the absence of preventive maintenance programs. The report details the implementation of AI-powered smart sensors and the HyperAspect Cognitive Cloud platform, which were utilized to monitor equipment performance and analyze data for real-time feedback on operational targets. The approach involved placing specific sensors on critical units to detect hidden failures and deviations from standard operating modes, enabling early identification of potential faults. Additionally, machine learning models were employed to forecast performance over a period of 24 to 720 hours, allowing operators to anticipate equipment failures and optimize maintenance schedules. The results indicated significant improvements in equipment uptime and maintenance metrics across various operational areas.