More often than not, manufacturing machinery is maintained in a reactive cycle: Driven by failure (fix when it breaks). Reactive Maintenance increases downtime and decreases productivity in any manufacturing operation. In some cases (and in larger manufacturing operations), Scheduled Maintenance techniques are adopted. Scheduled maintenance requires the periodic inspection of the equipment – even when it is not necessary. In most cases, machine components are changed or updated even when not necessary. Predictive Maintenance solves these problems by constant monitoring of a given system and continuously acquiring, correlating and extracting data. Using the acquired data, prediction of an upcoming failure becomes possible. Eliminating the downtime saves time and resources, predicting an approaching failure, scheduling delivery of spare parts and maintenance crew at the right time becomes possible. More often than not, classical Programmable Logic Controllers (PLCs) which have already been installed in the existing infrastructure fall short in performance to collect the necessary data. Harvesting the data and the required intelligence become possible through add-on IoT systems and sensors allowing easy access to data by the experts to correct system performance parameters, optimize performance and perform maintenance.