Coupled with the Machine Learning (ML) technology, Artificial Intelligence (AI) is going to have a great impact on manufacturing. These technologies would provide manufacturers with computational power needed to solve issues that humans can’t possibly solve. For centuries, the manufactures have been searching for many answers related to production. They are looking for ways to produce products as efficiently as possible with zero waste. With ML and AL, manufacturers can get prescriptive answers to production issues

Now the question arises, where do we start from? Where and how do we adopt AI technology?

Everything begins with data. Indeed, data is both the most underutilized asset of manufacturers and the foundational element that makes AI so powerful. Think of Maslow’s Hierarchy of Needs, a theory of motivation that is depicted as a pyramid, with the most basic, most important needs at the bottom, and the most complex needs at the top.

When beginning to adopt AI, many manufacturers realize that their data is in many different formats stored throughout several MES, ERP, and SCADA systems. There is a need to convert the data into a common format and import it to a common system, where it can be used to build models. Also, note that gathering only one variable about revolutions per minute of your machine is not going to be enough to tell you why a failure happened. However, if you add other variables such as vibration and temperatures, and data that lead to machine failure, you can build models and algorithms to predict failure. As more data is collected, you can determine accuracy requirements, such as this algorithm will be able to predict this failure within one day’s time, with 90% accuracy.

The sooner a manufacturer starts the journey toward AI, the sooner they will build large datasets that will enable them to implement advanced AI and ML models.

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