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Artificial intelligence is changing the rules of the game for almost every industry. As the technology matures and costs drop, AI is becoming more accessible for companies, and that includes manufacturing.
The manufacturing industry has always been eager to embrace new technologies. AI adoption means industrial companies and manufacturers can make faster, data-driven decisions, optimize manufacturing processes, improve the supply chain and improve customer service. In fact, AI in manufacturing is moving right to the point of process, computing at the machine level, instead of sending data up to the cloud and processing it there. This means AI is transforming manufacturing in real-time.
The consulting giant Accenture estimates that by 2035, AI-powered technologies could increase labor productivity by up to 40%. For manufacturing, those gains will come from improved quality control, shorter design times, quality assurance automation and predictive maintenance. To see what I mean consider the following examples of AI in manufacturing processes.
Many manufacturing companies are finding it increasingly hard to maintain high levels of quality and to comply with quality regulations and standards. In addition, some flaws in products are simply too small to be noticed with the naked eye, or the manufacturing process is too rapid for real-time analysis. However, advances in imaging and machine learning, and new capabilities to add AI directly into machinery at each process step mean AI quality assurance is not only possible, but an ideal solution.
Humans can find it difficult to spot patterns or failures until they result in a catastrophic failure. But with more data and subtle imagery available to them, AI will not only change how products are tested and approved, but artificial intelligence can identify the areas that need attention before they add up to a breakdown.
The cost of machine downtime is as much as $647 billion lost globally each year. Predictive maintenance allows companies to predict when machines need maintenance with high accuracy, instead of guessing or performing preventive maintenance. Predictive maintenance uses data from various sources like maintenance records, sensor data from machines and weather data to determine when a machine will need to be serviced.
Generative design is a process that generates designs to meet specified criteria. Designers or engineers input design goals and parameters such as materials, manufacturing methods, and cost constraints into generative design software to explore design alternatives. The solution utilizes machine learning techniques to learn from each iteration what works and what doesn’t.
Manufacturers collect vast amounts of data related to operations but too often this data is never applied to advanced analytics. If done right, AI can provide valuable insights to improve the business. Supply chain management, risk management, predictions on sales volume, product quality maintenance, prediction of recall issues are just some examples of how big data can be used to the benefit of manufacturers. AI algorithms can even formulate estimations of market demands by looking for patterns linking location, socioeconomic and macroeconomic factors, weather patterns, political status, consumer behavior and more.
Too many manufacturers seem to think they are not in the customer service, but it is an essential part of selling more products and delivering more services. Observing actual customers’ behaviors allows companies to better answer their needs. The hospitality and consumer retail industries are perfecting the use of AI and machine learning for better customer service. It’s time for manufacturers to join the revolution.
I hope that these examples get manufacturers thinking about ways AI can supercharge their businesses. We have been big believers in AI for a number of years now, for all sizes of businesses. Reach out to the AI team at GFConsulting for information about AI and our Watson-based application, AskGordy.