{"id":17151,"date":"2020-12-12T20:17:27","date_gmt":"2020-12-12T19:17:27","guid":{"rendered":"https:\/\/www.predictland.com\/casos-de-exito\/prediction-of-machine-configuration-to-achieve-zero-defects\/"},"modified":"2025-02-17T16:18:32","modified_gmt":"2025-02-17T15:18:32","slug":"prediction-of-machine-configuration-to-achieve-zero-defects","status":"publish","type":"caso-de-exito","link":"https:\/\/www.predictland.com\/en\/case-studies\/prediction-of-machine-configuration-to-achieve-zero-defects\/","title":{"rendered":"Prediction of machine configuration to achieve zero defects"},"content":{"rendered":"\n
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PredictLand designs a solution to reduce quality defects and increase the productivity of a manufacturing process for the multinational BSH Electrodom\u00e9sticos in Spain.<\/p>\n\n\n\n

Customer<\/h2>\n\n\n\n

BSH is a global manufacturer of Home Appliances, being a leader at national and European level with more than 40 manufacturing plants around the world, in addition to having a significant market share worldwide. Its production is focused on the commercial brands Bosch, Siemens, Balay, Gaggenau and Neff. <\/p>\n<\/div>\n\n\n\n

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\"BSH<\/figure>\n<\/div><\/div>\n<\/div>\n\n

Challenge<\/h2>\n\n

BSH was faced with the need to anticipate, well in advance, the reconfiguration and recalibration of a machine critical to its manufacturing processes in order to minimize machine downtime and avoid manufacturing defects.An added difficulty here is the retrieval of data from the machine. It consists, among other sources, of dozens of servos that generate data every 2 milliseconds, turning the challenge into abig data<\/em> problem by accumulating and preprocessing historical data to train the models. <\/p>\n\n

Solution<\/h2>\n\n

The Artificial Intelligence solution designed by PredictLand learns from the operation of the critical machine, and from the knowledge of the most experienced operators.<\/p>\n\n

The solution provides information to the operator on how to reconfigure the machine to improve the efficiency and OEE of the production line and the factory.<\/p>\n\n

This results in a reduction of scrap and defects due to machine set-up errors, greater independence of the machine operator at any given time, and his experience. At the same time, the solution provides relevant information that makes it possible to define much more efficient manufacturing strategies. <\/p>\n\n

Testimonial<\/h2>\n\n

Javier Chasco Echevarr\u00eda<\/strong>I4.0 Area Manager at BSH Electrodom\u00e9sticos, Esquiroz Factory (Navarra)<\/p>\n\n

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“The Machine Configuration Prediction project to achieve Zero Defects on our automatic door line has been the first Data Analytics project we have faced in the factory. During this time, we have come a long way together with PredictLand, not only in solving the project but also in getting to know a world that was new to us. <\/em><\/p>\n\n\n\n

In this case, the complexity of the project is very high, but this has never been a barrier for PredictLand.<\/em><\/p>\n\n\n\n

What I appreciate most about PredictLand is the focus on customer service. Our feedback and opinions are always taken into account in developments.” <\/em><\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"

PredictLand designs a solution to reduce quality defects and increase the productivity of a manufacturing process for the multinational BSH Electrodom\u00e9sticos in Spain. Customer BSH…<\/p>\n","protected":false},"author":2,"featured_media":16080,"menu_order":34,"template":"","class_list":["post-17151","caso-de-exito","type-caso-de-exito","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nPrediction of machine configuration to achieve zero defects - PredictLand AI<\/title>\n<meta name=\"description\" content=\"Case of success. 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