VISIBILITY - Currently unable to see amount of power consumed per machine or plant; need to account for energy used per batch. WASTE - 10% of product fails quality standards, unable to identify point of failure (initial spinning, weaving, final processing, etc). INACCURACY - Acquiring OEE measurements and compiling them is a manual process, which lacks visibility into all parts of the production process and is error-prone. LACK OF INTEROPERABILITY - Outdated machinery still has long, effective lifespans but needs to connect to newer, digital assets

The client is looking to generate higher value from the elevator data that is collected. Sensors and data include:Laser - position of the elevator carLuminosity - Level of light within the carUltrasound - Open shaft doorVibration - Acceleration of the car; vibration of the carMicrophones - abnormal sounds of the carAtmospheric Pressure - Air pressureHumidity - Shaft humidityTemperature - Shaft temperature 

Machine uptime is one of the most vital performance factors for electric rotating machinery. In times when an hour of downtime can equate to thousands of dollars in losses, securing predictability turns into a high priority for all industrial businesses. Manufacturers operate in an extremely volatile environment, thus avoiding unplanned downtime becomes critical for achieving desired business outcomes. Predictive maintenance is no longer a nice-to-have but a necessity to survive and thrive in unfavourable conditions. Start from the basics. Making machines perform better means, first and foremost, understanding how the machine works, extracting relevant data, and gaining meaningful insights into ongoing processes. The power of the machine lies in utilizing its full potential.