Failures may be binary in nature; that is, either a failure occurred or not. Failures can also be multi-class and fall into several different categories, including reduced speed, throughput, or quality. Obviously, the more complex the machine (or system), the more necessary it is to use ML models to effectively help prevent failures that impact productivity. Datasheets,Knowledge Studio,Knowledge Works,Monarch,Data Analytics,Aerospace,Automotive,Consumer Goods,Electronics,Process Manufacturing,Additive Manufacturing,Big Data,Cloud Computing,Data Transformation,Machine Learning,Smart Product Devleopment,Internet of Things,Corporate">