This technology enables people to do their jobs better, faster, with more efficiency. Machine Learning has the ability to make ongoing analysis and management of production lines faster and easier for all. Machine Learning enables people to do their jobs better, faster, and with more efficiency. The use of technology in manufacturing alone is likely to attract a larger pool of candidates-from those looking for a digital and data-driven career path to those long-time industry veterans looking for a better way to do things. At the same time, new employees raised on the digital frontier have the technical skillset to use modern technologies but lack the finesse and expertise of experienced operators, SMEs, or plant managers.ĪI has the power to make the manufacturing industry appealing again. This results in underutilized technology on the plant floor and oftentimes overused human hours. Industry 4.0 has brought the need for new technology and skill sets among employees that are advancing faster than existing personnel could adequately learn them. Here are a couple of ways we see AI and Machine Learning improving a data scientist and process engineers day-to-day: This common misconception of AI replacing jobs, however, is actually unlikely, as there are many things that humans can and will always do better than technology. These technologies expand the capacity of your workforce rather than outright replace it. Interest in manufacturing roles has decreased over the years due to the misconception that these are dangerous, tedious roles.ĪI technology, including Machine Learning, is able to augment or in some cases, even replace human labor in the factory. The manufacturing industry is facing a global labor shortage. Machine Learning can help to identify hidden bottlenecks in manufacturing processes, opening up the opportunity to create further efficiencies of resources and automation whether it is space, goods, personnel, or time. It can also help prevent those unwanted fire drill service calls that slow down production and decrease OEE.Īdvanced Analytics and pattern recognition can help process engineers identify problems they were previously unaware of. This helps launch companies into a maintenance schedule that revolves around machine need instead of operating on a cyclical calendar where machines may or may not need service. Industrials can use Machine Learning to anticipate needed maintenance on machines to proactively schedule and execute critical maintenance. With Machine Learning, industrials are able to anticipate required maintenance and in some cases, are even able to avoid unexpected (and costly) downtime. Now we can look at our overall company objectives and use Braincube to help us get there.” - A Braincube building materials customer Previous technologies only let us look at immediate issues, put out fires, or solve the problem of the day. “Braincube focuses us to have discipline around strategic thinking. Once uncovered, industrials are then able to scale the workflows, systems, and discoveries they find and deliver these learnings to other factories globally. Machine Learning is improving productivity by uncovering better strategies for predictive and preventative maintenance. Process engineers, data scientists, and/or analysts need to find creative ways to cut costs and run production more efficiently. It can back up your supply chain, impact your overall quality, and prevent your process engineers from focusing on optimizations to uncover new savings. All of these interruptions can cause hours or even days of lost productivity. Everywhere you turn, there are increasing costs from raw materials to workforce training and preparation, and especially machine and asset maintenance. In manufacturing, continuous improvement is the goal. Here are the 4 ways Machine Learning can improve your production: Cost Reduction Many in industry are looking to use their data in a better way. Once connected, Machine Learning can help manufacturers analyze Big Data faster and more efficiently than the human mind can physically complete. Before you can leverage the power of Machine Learning, you need to be connected with the ability to measure the right data points, so that you can uncover optimizations. The goal of using Machine Learning is to identify opportunities to improve industrial operations and OEE at any phase of the manufacturing process-streamlining your process from raw materials to product requires data. Continuing to find new ways to improve operations requires increased creativity and access to critical data.Įnter: Machine Learning. Pressed by leadership, many process engineers and data scientists have already tackled the obvious optimizations within their factory.
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