{"id":15159,"date":"2019-04-23T11:00:11","date_gmt":"2019-04-23T19:00:11","guid":{"rendered":"http:\/\/www.palada.net\/index.php\/2019\/04\/23\/news-8908\/"},"modified":"2019-04-23T11:00:11","modified_gmt":"2019-04-23T19:00:11","slug":"news-8908","status":"publish","type":"post","link":"https:\/\/www.palada.net\/index.php\/2019\/04\/23\/news-8908\/","title":{"rendered":"Predictive Analytics in mining and metals: Whose prediction is the right one?"},"content":{"rendered":"

Credit to Author: Greg Johnson| Date: Tue, 23 Apr 2019 12:00:50 +0000<\/strong><\/p>\n

Predictive Analytics<\/a> is the classic Internet of Things (IoT) application. In the simplest sense, you connect sensors (temperature, vibration etc.) to assets (equipment, buildings, trucks), send the data to the cloud, and then apply predictive analytics to identify and prevent potential failures.<\/p>\n

\"\" <\/p>\n

With today\u2019s technology, it is relatively easy to accomplish. You can do so by building your own using a library of components<\/a> or you can go to a 3rd<\/sup> party vendor like SAP<\/a> or AVEVA<\/a>. You even have the choice of whether to run these toolsets on a private cloud, public cloud or hybrid cloud.<\/p>\n

Another option is to use digital services from the equipment provider. It goes without saying that although you know your process better than anyone else, the equipment provider knows their equipment better than anyone else as well. We see the debate on which path to choose playing out right now in the mining and metals worlds, particularly around mobile fleets from vendors like Caterpillar. There is no debate about whether predictive analytics is needed, only whether you use the CAT service<\/a>, do your own development, or go to a 3rd<\/sup> party.<\/p>\n

Lately we have seen some pretty compelling evidence that in the \u201celectrical domain\u201d there is significant benefit to going with the equipment provider.\u00a0 In one case study the user managed to avoid spending 1 million euros<\/a> due to the potential repairs and replacements of their transformers that were avoided.\u00a0 And just last week I heard another very compelling story.<\/p>\n

It involved one of our customers that had gone down the \u201cprivate cloud\u201d option.\u00a0 They were collecting a lot of data but they didn\u2019t know how to analyse it. Schneider Electric came in and set up a cloud-to-cloud connection along with our EcoStruxure Asset Advisor<\/a> digital service, and almost immediately they discovered several asset health warnings they were unaware of previously.\u00a0 Not only that, but they were given actionable recommendations along with a complete analysis that gave them greater insight into how to make the necessary changes and modifications.\u00a0 Bottom line: knowing there is an issue is important, but so is knowing what to do about it.<\/p>\n

All these examples are strong arguments for going to the specialist for a solution, even though it\u2019s not the only choice.\u00a0 You can still run \u201cbuild your own\u201d or 3rd<\/sup> party solutions where appropriate, but don\u2019t forget the added value you can get from the original equipment supplier.\u00a0 It just might be the right prediction for your operation.<\/p>\n

The post Predictive Analytics in mining and metals: Whose prediction is the right one?<\/a> appeared first on Schneider Electric Blog<\/a>.<\/p>\n

http:\/\/blog.schneider-electric.com\/feed\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Credit to Author: Greg Johnson| Date: Tue, 23 Apr 2019 12:00:50 +0000<\/strong><\/p>\n

Predictive Analytics is the classic Internet of Things (IoT) application. In the simplest sense, you connect sensors (temperature, vibration etc.) to assets (equipment, buildings, trucks), send the data to the… Read more »<\/a><\/p>\n

The post Predictive Analytics in mining and metals: Whose prediction is the right one?<\/a> appeared first on Schneider Electric Blog<\/a>.<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"colormag_page_container_layout":"default_layout","colormag_page_sidebar_layout":"default_layout","footnotes":""},"categories":[12389,12388],"tags":[15260,18022,12515,6269,12508,12439,901,21637,12438,12547],"class_list":["post-15159","post","type-post","status-publish","format-standard","hentry","category-scadaics","category-schneider","tag-aveva","tag-ecostruxure-asset-advisor","tag-industrial-internet-of-things","tag-internet-of-things","tag-machine-and-process-management","tag-metals","tag-mining","tag-mining-digital-services","tag-miningmetalsminerals","tag-predictive-analytics"],"_links":{"self":[{"href":"https:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/posts\/15159","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/comments?post=15159"}],"version-history":[{"count":0,"href":"https:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/posts\/15159\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/media?parent=15159"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/categories?post=15159"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.palada.net\/index.php\/wp-json\/wp\/v2\/tags?post=15159"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}