{"id":8287,"date":"2018-04-03T09:12:00","date_gmt":"2018-04-03T07:12:00","guid":{"rendered":"https:\/\/roth-gruppe.de\/unternehmen\/themen-im-fokus\/case-study-big-data-and-predictive-maintenance\/"},"modified":"2026-06-19T09:32:02","modified_gmt":"2026-06-19T07:32:02","slug":"case-study-big-data-and-predictive-maintenance","status":"publish","type":"subject","link":"https:\/\/roth-gruppe.de\/en\/unternehmen\/themen-im-fokus\/case-study-big-data-and-predictive-maintenance\/","title":{"rendered":"Case Study: Big Data and Predictive Maintenance"},"content":{"rendered":"\r\n\r\n
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Predictive maintenance is one of the most important benefits of Industry 4.0. By consolidating machine data from various locations into a central cloud, it is even possible to optimize machines and production processes. Thanks to a remote maintenance solution from the Roth Group\u2014featuring components from Endian and cloud solutions from Amazon Web Services\u2014customers in every industry are already benefiting from these advantages of digitalization. <\/strong><\/p>\n\n\n\n

By leveraging big data, the Roth Group\u2019s service increases its customers\u2019 equipment availability. The Roth Group recognized the benefits of remote maintenance early on and has been using this approach for several years to reduce its response times and its customers\u2019 maintenance costs. Drawing on the expertise of its engineers, the company can respond in a timely manner\u2014or even before\u2014malfunctions or problems occur. In the past, a technician would travel to the customer\u2019s site for maintenance purposes or in the event of a malfunction to perform troubleshooting. This was time-consuming and costly. With the goal of further optimizing its service, the Roth Group sought a solution that would allow data from machines at different locations to be analyzed in a central database, thereby enabling predictive maintenance. The Roth Group opted for a solution featuring hardware from Endian, the security manufacturer for Industry 4.0: Endian Connect enables secure and highly scalable access to a large number of endpoints with an equally large number of concurrent connections. Depending on the application, the Roth Group collects, for example, operating hours, error conditions, rotational speeds, quality data, and many other metrics from the systems they have programmed. Consolidating machine data from different locations via Amazon Web Services\u2019 cloud solutions, for example, takes the value of the data to a new level: Thanks to cross-location analysis and evaluation, it is possible to predict with significantly greater precision the conditions under which a system operates optimally. This offers the opportunity to optimize production processes and minimize downtime. Looking ahead, the Roth Group plans to gain further insights through data analysis that will provide even greater value to the company and its customers. <\/p>\n\n\n\n

Read the full case study featuring Endian and AWS here:<\/p>\n<\/div>\n\n\n\n

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Predictive maintenance is one of the most important benefits of Industry 4.0<\/p>\n","protected":false},"featured_media":0,"template":"","subject_category":[45],"class_list":["post-8287","subject","type-subject","status-publish","hentry"],"acf":[],"yoast_head":"\nCase Study: Big Data and Predictive Maintenance - Roth Steuerungstechnik<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/roth-gruppe.de\/en\/?post_type=subject&p=8287\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Case Study: Big Data and Predictive Maintenance - Roth Steuerungstechnik\" \/>\n<meta property=\"og:description\" content=\"Predictive maintenance is one of the most important benefits of Industry 4.0\" \/>\n<meta property=\"og:url\" content=\"https:\/\/roth-gruppe.de\/en\/?post_type=subject&p=8287\" \/>\n<meta property=\"og:site_name\" content=\"Roth Steuerungstechnik\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/RothGruppe\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-19T07:32:02+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/roth-gruppe.de\\\/en\\\/?post_type=subject&p=8287\",\"url\":\"https:\\\/\\\/roth-gruppe.de\\\/en\\\/?post_type=subject&p=8287\",\"name\":\"Case Study: Big Data and Predictive Maintenance - 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