{"id":31286,"date":"2022-01-10T20:49:22","date_gmt":"2022-01-10T20:49:22","guid":{"rendered":"https:\/\/www.dvirc.org\/insights\/artificial-intelligence-in-manufacturing-real-world-success-stories-and-lessons-learned\/"},"modified":"2023-03-08T14:01:07","modified_gmt":"2023-03-08T14:01:07","slug":"artificial-intelligence-in-manufacturing-real-world-success-stories-and-lessons-learned","status":"publish","type":"post","link":"https:\/\/www.dvirc.org\/insights\/artificial-intelligence-in-manufacturing-real-world-success-stories-and-lessons-learned\/","title":{"rendered":"Artificial Intelligence in Manufacturing: Real World Success Stories and Lessons Learned"},"content":{"rendered":"
Written By: Katie Rapp<\/em><\/p>\n For manufacturers, artificial intelligence (AI) can be a game changer. Greater efficiencies, lower costs, improved quality and reduced downtime are just some of the potential benefits. This technology is not only for large manufacturers. High-value, cost-effective AI solutions are more accessible than many smaller manufacturers realize.<\/p>\n In the recent MEP National Network\u2122\/Modern Machine Shop webinar\u00a0\u201cArtificial Intelligence in Manufacturing: Real World Success Stories and Lessons Learned,\u201d<\/a>\u00a0Andy Carr of\u00a0South Carolina MEP<\/a>\u00a0(SCMEP) and\u00a0Delta Bravo<\/a>\u00a0Founder and CEO Rick Oppedisano discussed AI solutions that work best for small and medium-sized manufacturers (SMMs). Delta Bravo is a third-party vendor that works with SCMEP manufacturing clients on AI solutions to meet their needs.<\/p>\n In the webinar, Rick described AI use cases featuring several manufacturers he has worked with including Precision Global, Metromont, Rolls-Royce, JTEKT and Elkem Silicones. Since 2017, Delta Bravo has worked on about 90 projects and has learned what works best and produces significant return on investment (ROI), especially for smaller manufacturers. AI projects improved equipment uptime, increased quality and throughput, and reduced scrap. With the healthier bottom lines and increased profits came lessons learned. Rick identified key drivers for successful AI implementation, potential pitfalls and best practices and shared some pro tips.<\/p>\n People often use the terms AI and machine learning interchangeably, but they\u2019re two very different things. Machine learning puts data from different sources together and helps you understand how the data is acting, why, and which data correlates with other data. It helps you solve a particular problem by taking historic evidence in the data to tell you the probabilities between various choices and which choice clearly worked better in the past. It tells you the relevance of all this, the probabilities of certain outcomes and the future likelihood of these outcomes.<\/p>\n AI is what takes action on a recommendation supplied by machine learning. To use a hot stove analogy, when you put your hand toward a hot stove, your brain tells you from past experience and from the tingling in your fingers what could possibly happen and what you should do. That\u2019s like machine learning. AI is the technical ability to pull your hand back before you get burned.<\/p>\n Using AI in a manufacturing context means using data to make actionable decisions faster and more accurately than a human can do. There are two specific areas where this makes a lot of sense: for forecasting and for understanding anomalies or outliers. There are parts of the manufacturing process where forecasting can drive value. If you have enough historic data and context about the decisions and process around the data, there\u2019s a good chance that you can develop predictions. Why do the same inputs on the same machines sometimes have different outcomes? Is there an occasional manufacturing scenario that you want to understand? The data off one machine can be overwhelming to a human analyst, so that\u2019s where AI can help. In addition, manufacturing systems are holistic and one metric in part of the process relates to another part of the same process. If you\u2019re only looking at one area, how do you know what\u2019s going on in another? AI can be the solution.<\/p>\n There are five areas where AI creates a significant financial impact.<\/p>\n There\u2019s a lot of skepticism about introducing AI solutions in manufacturing and whether the investment is justified. Successful AI implementation boils down to the three P\u2019s: problem, persona, process. You have to carefully define a suitable problem for AI to solve. You need the right people involved, including leadership, operations, IT\/tech, digital transformation and finance people \u2013 each play a key role in successfully adopting AI. And process \u2013 you need an approach that identifies the right way to tackle the problem with AI. You need to understand whether the data to solve the problem exists.<\/p>\nArtificial Intelligence and Machine Learning<\/h2>\n
Why Adopt AI?<\/h2>\n
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Best Practices and Potential Pitfalls<\/h2>\n