Real automated production facilities also experience problems similar to those in the mini-factory. Not only do they have to operate around the clock and meet quality, time, and cost requirements, but more and more often flexible, small production runs are also required. That means the workflows in a facility like this have to be regularly adapted, and must remain optimized to avoid bottlenecks like those in the mini-factory, for instance. That\u2019s a complex problem for the operators, since changing the workflows may have an impact only some time later, at a completely unexpected location. This is where simulation models integrated into the production system can help.<\/p>\n\n\n\n
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A team from the Simulation & Digital Twin Working Group at the Siemens Technology Research Unit therefore developed an intuitive assistance system that uses a model of this kind, giving shift leaders, maintenance engineers, and technicians the opportunity to respond to production problems at short notice. \u201cThe app is intuitive, and offers clear options regarding ways forward,\u201d says simulation expert Annelie Sohr, who works together with Wehrstedt. \u201cThe complex simulation tool, which was previously thought of as being only for experts, runs in the background and shouldn\u2019t be anything for the users to worry about.\u201d<\/p>\n\n\n\n
Fully automatic in the long term<\/strong><\/p>\n\n\n\n These real-time simulations are fed with all kinds of sensor and operating data, enabling rapid error analyses and \u201cwhat-if\u201d scenarios. In other words, the threatened bottleneck is identified before the blocks start stacking up. \u201cIn our mini-factory, for example, we received the suggestion to change the sequence in which the pieces are processed,\u201d observes Sohr. \u201cThat means the plant manager receives the suggested option \u2013 to make adjustments to production \u2013 in super-quick time.\u201d<\/p>\n\n\n\n Of course, it\u2019s also possible to imagine expanding the data sources still further. That would enable the simulation to include the order situation in the coming weeks, inventory levels, or forthcoming maintenance work in its simulation. A precondition, however, as with all data analyses, is that all the relevant data must be available in clearly defined formats. \u201cIn the longer term it\u2019s possible that most of these decisions will be taken automatically,\u201d says Wehrstedt. \u201cOperators would then simply formulate general targets, relating to quality assurance or resource-efficient production, for example.\u201d<\/p>\n\n\n\n Already in use<\/strong><\/p>\n\n\n\n Parts of this assistance strategy are already in place, in an infrastructure environment, for example: The Siemens sewer network control system app SIWA Sewer thus uses simulations to help optimize the operation of drainage systems. In a pilot project in a sewer network in North Rhine-Westphalia, it utilizes a simulation model that draws on resources like weather forecasts. That means SIWA Sewer can ensure the network continues to function smoothly even under heavy rainfall.<\/p>\n\n\n\n Source : SIEMENS<\/p>\n","protected":false},"excerpt":{"rendered":" Simulations of production facilities help the operators determine the ideal […]<\/p>\n","protected":false},"author":1,"featured_media":1739,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"yoast_head":"\n