{"id":14406,"date":"2023-06-05T15:42:15","date_gmt":"2023-06-05T12:42:15","guid":{"rendered":"https:\/\/www.razor-labs.com\/estudio-de-caso-empresa-lider-en-mineria-de-mineral-de-hierro-reduce-el-tiempo-de-inactividad-no-programado-de-la-flota-de-cintas-transportadoras-del-puerto\/"},"modified":"2025-09-08T14:34:01","modified_gmt":"2025-09-08T11:34:01","slug":"estudio-de-caso-empresa-lider-en-mineria-de-mineral-de-hierro-reduce-el-tiempo-de-inactividad-no-programado-de-la-flota-de-cintas-transportadoras-del-puerto","status":"publish","type":"post","link":"https:\/\/www.razor-labs.com\/es\/estudio-de-caso-empresa-lider-en-mineria-de-mineral-de-hierro-reduce-el-tiempo-de-inactividad-no-programado-de-la-flota-de-cintas-transportadoras-del-puerto\/","title":{"rendered":"Estudio de caso: La principal empresa minera de mineral de hierro reduce el tiempo de inactividad no programado de la flota de cintas transportadoras del puerto"},"content":{"rendered":"
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Estudio de caso: La principal empresa minera de mineral de hierro reduce el tiempo de inactividad no programado de la flota de cintas transportadoras del puerto<\/h1>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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junio 5, 2023<\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The challenge<\/strong><\/h2>\n

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With dozens of kilometers of conveyors, the iron ore port experienced an average annual downtime of roughly 800 annual hours for the port inflow conveyors<\/strong>. Various issues, including pulley failures, belt rips, run-offs and drift issues, motor and gearbox failures, and roller failures, caused these failures.<\/p>\n

The financial impact of this unscheduled downtime was significant, with an estimated cost of over $70M per year<\/strong>. The company sought a solution to identify and prevent equipment failures proactively in order to reduce unplanned downtime and increase productivity.<\/p>\n<\/div>\n

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The Solution<\/strong><\/h2>\n

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Leveraging the DataMind AI predictive capabilities, the mining company was able to detect potential failures more than three months ahead of time, allowing them to undertake targeted maintenance actions that addressed the underlying causes and prevented future occurrences.<\/p>\n

Furthermore, remote equipment monitoring and advanced maintenance scheduling not only enhanced operational efficiency but also significantly reduced the risk of accidents and injuries, which tend to increase when working in close proximity to the machinery.<\/p>\n<\/div>\n

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