IBM Uses Machine Learning to Lower Bottling Costs

In addition to ML, the Internet of Things (IoT) and predictive maintenance will come into play as Niagara Bottling will collect data from machine sensors. Sensory data includes humidity within the building and the amount of pressure and speed when factory workers pull the plastic stretch wrap. "Getting into predictive equipment maintenance is going to be an area where we could use data science and ML," Rao said. "We have just started down that path of being able to instrument different manufacturing equipment with the right sensors, and being able to measure and gain insights from the measurements." In addition to water bottle manufacturing plants, IoT data from predictive maintenance is valuable in power plants and oil refineries.