The manufacturing industry is undergoing a rapid transformation thanks to the application of machine learning. While many industries have focused on collecting massive quantities of data over the years, gaining predictive analysis into this massive data set has been difficult. Predictive analytics are now able to predict errors and problems in factories and machines before they occur – preventing shut downs, failures and more, which previously resulted in significant costs. These analytics are now also able to be tied into actionable alerts to frontline workers and even automate the machines to respond automatically through self-correcting measures. The implementation of predictive analytics and automation is through a concept called a digital twin. A digital twin is a virtual replica of your manufacturing asset that collects and may respond to data. This provides the ability to create, build, test and validate predictive analytics and automation in a data-fed virtual environment – something nearly impossible to replicate in a factory environment! With the digital twin businesses can observe processes under multiple performance conditions and eliminate problems before they occur by monitoring the digital twin and sending automation commands to live machines. By utilizing the power of the digital twin, businesses can move from being reactive to predictive. The power of cloud-based analytics and automation turn existing assets into tools that optimize processes, save money and accelerate innovation. The next question that many ask themselves when approaching a possible IoT or machine learning solution is how to make it possible financially. Our next three-part series of blog will focus on the financial investment and return of IoT solutions, educating on how to start small, dream big and scale solutions appropriately to production. Connect with a New Signature expert today about the possibilities that await your business with an IoT solution.