In pop culture, the combination of business interests and\nartificial intelligence is something to be feared. It brings to mind Skynet,\nthe malevolent neural network from the Terminator movies that goes to great\nlengths to destroy its human makers.<\/p>\n\n\n\n
The reality is different, though. We\u2019re not living through a\nhostile takeover by the all-pervasive Skynet. Instead, we\u2019re seeing individual\nbusinesses looking at the multiple AI solutions on the market with an eye to\nstriking a better balance between operational efficiency and customer\nsatisfaction. We take advantage of it every time we check out new products\nrecommended by Amazon.com, which uses AI to extract as much information as possible\nfrom members\u2019 transactions. We have fun with it when we browse Netflix, which\nuses AI to predict what viewers might like to watch next.<\/p>\n\n\n\n
We\u2019re also increasingly likely to encounter it at work,\nsince businesses of all types are finding ways to use it in industrial, retail,\nand service operations. In this essay, we\u2019ll focus on the impact that AI is\nhaving on the manufacturing sector. We\u2019ll also consider a few of the ways that\nentrepreneurs and engineers have integrated machine learning \u2013 an application\nof AI that gives machines the ability to learn from experience without being\nexplicitly programmed to do so \u2013 into the manufacturing process.<\/p>\n\n\n\n
Improving the process<\/strong><\/p>\n\n\n\n To date, manufacturers have been able to introduce AI into\nthree aspects of their business: operational procedures, production, and\npost-production. We\u2019ll start by taking a look at operational procedures \u2013 that\nis, at the ways in which machine learning can improve the course of production\nitself.<\/p>\n\n\n\n One company that\u2019s taken this path is Fanuc, a Japanese manufacturer of industrial robotics and automation technology. Fanuc uses deep reinforcement learning, a type of machine learning solution developed by Preferred Networks that enables its robots to teach themselves new skills quickly and effectively, without the need for precise and complex programming.[1]<\/p>\n\n\n\n These robots improve their performance on difficult tasks such as picking up small objects, which would normally require programmers to spend many hours compiling precise and complex instructions, by filming themselves and loading the video footage into a deep learning system. This system analyzes the data to determine which approaches and which actions contribute the most to a successful outcome. In turn, this analysis helps the machines achieve a higher level of performance more quickly and more efficiently than they might have done if the company had assigned the job to human programmers.[2]<\/p>\n\n\n\n Shohei Hido, the chief research officer at Preferred Networks, explained the benefits of this approach to Technology Review in 2016. Once a Fanuc robot begins to practice a task, he said, \u201c[after] eight hours or so, it gets to 90% accuracy or above, which is almost the same as if an expert were to program it.\u201d This greatly shortens the learning curve, he added. \u201cIt works overnight,\u201d he commented. \u201cThe next morning, [the robot] is tuned.\u201d[3]<\/p>\n\n\n\n In Fanuc\u2019s case, machine learning has allowed the company to turn out robots that help factories optimize the speed, cost, and efficiency of their operations. But it\u2019s not just about streamlining. Machine learning could eventually enable manufacturing plants to react quickly to changing instructions, rather than relying on standardization.[4] It also has implications for worker safety, in that it can help identify the factors contributing to accidents and also develop solutions to prevent their reoccurrence.[5] It can, for example, analyze video footage to determine when workers fail to wear hard hats or follow other safety regulations.[6]<\/p>\n\n\n\n Improving the outcome<\/strong><\/p>\n\n\n\n Machine learning can also lead to improvements in production\noutcomes \u2013 that is, in the quality and versatility of products that a plant\nmanufactures.<\/p>\n\n\n\n For example, the German conglomerate Siemens has made AI an integral component of some of its best-performing turbines \u2013 specifically, natural gas-fired units in South Korea and the United States. These turbines have been equipped with GT-ACO (Gas Turbine Autonomous Control Optimizer), a solution developed by the company\u2019s Learning Systems research team that uses data from multiple smart sensors make precise and rapid adjustments to fuel valves.[7] GT-ACO users can use virtual reality goggles to view an image of the turbine that incorporates real-time data from the sensors.[8]<\/p>\n\n\n\n According to Volkmar Sterzing, the leader of the research team, the ability to monitor changes in the combustion process will help buyers reduce harmful emissions and optimize fuel consumption. \u201cTo ensure that a gas turbine runs optimally, you always have to search for a balance in which several undesired effects such as combustion dynamics, loss of efficiency and emissions are kept as low as possible,\u201d he was quoted as saying in a company statement. \u201cIf you improve one variable, you will worsen a different one. Artificial intelligence knows how to find the sweet spot.\u201d[9]<\/p>\n\n\n\n There are significant benefits for customers in this sweet spot. As Dr. Norbert Gaus, the head of research in digitalization and automation at the company\u2019s corporate technology unit, noted: \u201cEven after experts had done their best to optimize the turbine\u2019s nitrous oxide emissions, our AI system was able to reduce emissions by an additional 10-15 %.\u201d[10]<\/p>\n\n\n\n What\u2019s more, improved quality won\u2019t just be something for big corporate buyers. Machine learning has already enabled Amazon and other large retailers to reduce the time needed to deliver products to all customers.[11] Eventually, it could also allow manufacturers of clothing and shoes to turn out customized items at prices that are competitive with merchandise made on standardized production lines.[12]<\/p>\n\n\n\n Augmenting the\noutcome<\/strong><\/p>\n\n\n\n Meanwhile, AI doesn\u2019t just affect production and products.\nIt can also allow manufacturers to expand the relationships they have with\ntheir customers beyond the point of sale.<\/p>\n\n\n\n One company that has successfully incorporated machine learning into its catalog is Cummins Power Generation, an Indiana-based manufacturer of power-generating equipment, including generators and prime and stand-by systems. The company teamed up with Microsoft and Avtex several years ago to develop a remote monitoring system that collects data from Cummins products around the world. This system, known as the Power Command Cloud, \u201cconnects to millions of Cummins generators around the world, providing greater visibility into how equipment is performing and enabling refueling and performance maintenance at the exact time to maximize uptime,\u201d Microsoft reported in 2016.[13]<\/p>\n\n\n\n This machine learning solution helps Cummins\u2019 customers by monitoring multiple components, alerting users to trouble, and working to minimize the length and frequency of outages. But it goes a step further: It also notifies authorized service technicians of problems (both potential and actual) and of service requirements when they arise. In effect, it allows Cummins equipment to initiate its own service calls, thereby streamlining the repair process and reducing the time needed to restore normal functioning.[14]<\/p>\n\n\n\n In the long run, machine learning is likely to have positive consequences for manufacturers that incorporate it into their products. It has the potential to create new streams of revenue by giving buyers an easy way to access expert technicians whenever their devices need service or repairs.[15] And on a more general level, it lays the foundation for extending the relationship between the producer and the consumer beyond the moment at which goods or services are sold. It gives manufacturers a reason to offer their products on terms that will allow them to continue collecting information from sensor devices and analyzing it after receipt.<\/p>\n\n\n\n Conclusion<\/strong><\/p>\n\n\n\n Machine learning appears to have the potential to change\nrelationships between producers and consumers in positive ways. It can help\nproducers by giving them ways to streamline their operations, and it can help\nconsumers by making better products available at reasonable prices.\nAdditionally, it can provide both parties with reasons to continue working\ntogether even after they sign purchase agreements and execute deliveries.<\/p>\n\n\n\n Furthermore, it can pave the way for additional\nimprovements. Using machine learning to keep up the relationship between\nproducers and consumers gives the former the ability to feed data \u2013 real\noperational data, collected on factory floors and not in testing facilities \u2013\nback into their AI systems. These systems can then use the data to gain a\nbetter understanding of production processes and generate new solutions.<\/p>\n\n\n\n About the Author <\/strong><\/p>\n\n\n\n Gregory Miller is a writer with DO Supply<\/a> who covers Robotics, Artificial Intelligence and Automation. When not writing, he enjoys hiking, rock climbing and opining about the virtues of coffee. [1] https:\/\/www.technologyreview.com\/s\/601045\/this-factory-robot-learns-a-new-job-overnight\/<\/a> <\/p>\n\n\n\n [2] https:\/\/www.technologyreview.com\/s\/601045\/this-factory-robot-learns-a-new-job-overnight\/<\/a>, https:\/\/www.fanuc.com\/product\/robot.html<\/a> <\/p>\n\n\n\n [3] https:\/\/www.technologyreview.com\/s\/601045\/this-factory-robot-learns-a-new-job-overnight\/<\/a> <\/p>\n\n\n\n [4] https:\/\/emerj.com\/ai-sector-overviews\/machine-learning-in-manufacturing\/<\/a> <\/p>\n\n\n\n [5] https:\/\/www.ibm.com\/blogs\/internet-of-things\/iot-worker-insights-worker-safety-and-ai\/<\/a> <\/p>\n\n\n\n [6] http:\/\/ai.business\/2017\/03\/29\/the-role-of-machine-learning-in-construction-safety\/<\/a> <\/p>\n\n\n\n [7] https:\/\/new.siemens.com\/global\/en\/company\/stories\/research-technologies\/artificial-intelligence-ai-in-gas-turbines.html<\/a> <\/p>\n\n\n\n [8] http:\/\/www.ulliwaltinger.de\/deep-learning-meets-virtual-reality-how-to-step-inside-a-gas-turbine\/<\/a> <\/p>\n\n\n\n [9] https:\/\/new.siemens.com\/global\/en\/company\/stories\/research-technologies\/artificial-intelligence-ai-in-gas-turbines.html<\/a> <\/p>\n\n\n\n [10] https:\/\/emerj.com\/ai-sector-overviews\/machine-learning-in-manufacturing\/<\/a> <\/p>\n\n\n\n [11] https:\/\/www.sdcexec.com\/software-technology\/news\/21034099\/artificial-intelligence-transforms-amazons-onehour-delivery<\/a> <\/p>\n\n\n\n [12] https:\/\/www.retailtouchpoints.com\/features\/executive-viewpoints\/how-ai-can-streamline-the-shoe-design-process<\/a> <\/p>\n\n\n\n [13] https:\/\/blogs.microsoft.com\/iot\/2016\/11\/29\/cummins-power-generation-keeps-the-lights-on-with-azure-iot-suite\/<\/a> <\/p>\n\n\n\n [14] https:\/\/www.industrialiotseries.com\/2018\/07\/25\/predictive-maintenance\/<\/a> , https:\/\/blogs.microsoft.com\/iot\/2016\/11\/29\/cummins-power-generation-keeps-the-lights-on-with-azure-iot-suite\/<\/a> <\/p>\n\n\n\n
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