October 21, 2019 – 11am (Detroit) | 5pm (Stuttgart) | 8:30pm (New Delhi)
Automakers today face five common challenges: productivity and quality losses due to equipment failures; high replacement cost of parts; long repair times, outdated and ineffective equipment maintenance programs and siloed production systems that create huge inefficiencies.
Health Monitoring and Predictive Maintenance (HMP) is a fully integrated solution that requires minimal IT resources and support. Its highly automated, adaptive intelligence (AI) enabled smart applications and best-in-class reporting are gamechangers in real-time HMP; they provide manufacturers with 100% visibility into the performance of their equipment, production processes and engineering systems.
Traditional HMP solutions are slow, require significant user intervention, are localised, take a long time to deploy and require significant IT resources and other costly support. But through the advent of Industry 4.0, highly integrated smart factories with a more intelligent, automated approach to smart automotive manufacturing are emerging. Smart HMP applications can now perform real-time fault detection, fast and accurate root cause analysis and predict equipment issues before they occur. This can prevent disruptions in manufacturing, and power factory performance to new heights.
In this 60-minute webinar, Joe Lee, Global Product Engineering Director and Stewart Chalmers, VP of Business Development at BISTel, demonstrates how automakers can use new AI cloud-based equipment HMP technology to help engineers guard against issues that harm yield and predict production and equipment issues before they occur. This not only eliminates equipment downtime, but also increases quality, slashes servicing costs and extends the life of equipment. Whether you are an operator, engineer or executive, HMP seamlessly integrates with all other factory data management systems to provide the ultimate data visualisation experience. HMP creates factory insights that result in better and more meaningful decision making.