Taking advantage of applied AI in Manufacturing
Manufacturing has been continuously evolving since the first Industrial Revolution. Early manufacturing was often done in people’s homes, using hand tools or basic machines. The First Industrial Revolution used steam power to mechanize production. The Second used electric power to create mass production. The Third used electronics and information technology to automate production and this still continues. Now we are again at the brink of a change. The digital revolution is happening as the manufacturing factory floor gets connected and we are able to achieve production at a scale with lower cost and increased quality. Changing consumer demand and market dynamics such as increasing energy, labor costs, flexibility needs put pressure on manufacturing companies to become smarter in connecting business processes, operational, IT data, people, and things.
As the cost of the sensors, data storage, high-performance compute and analytical engines are coming down and with increasing stream of data from thousands of devices, it is no longer possible for specialists to analyze and contextualize all this environmental data in a short time-frame. Machine learning, vision, and other fields of AI emerge to meet these rising demands and transform the way people and machines work together.
Artificial Intelligence is here to stay for the next decade and beyond bringing revolutionary changes to the manufacturing and high-tech industry. Organizations should understand their business needs first and find the best use cases to apply AI.
Imagine how maintenance of manufacturing assets change based on the real-time analytics of variety and volumes of data coming from Internet of Things (IoT). Machine-learning provides the way to analyze the past and future patterns of the data and prescribes the best possible actions significantly enhancing the decision making process. Automating the maintenance actions based on insights become possible.
While predictive analytics tells us why an equipment is likely to fail the objective of prescriptive maintenance is to optimize the outcome based on prediction. As example, using AI techniques like Gradient Tree Boosting it can be predicted that an equipment is going to fail with a probability of more than 90% with concrete prediction and failure timeframes. In addition to this, contextual business data, failure impact, cost of proactive fix are also analyzed.
