Shorten cycle time and focus attention on high-value problem areas by correlating performance, downtime and quality to products and equipment.

Increase production up to 5%, reduce energy usage and mitigate process upsets with advanced process control technology designed for upstream processes.

Maintain and expand APC benefit improvements with patented technology that automatically adapts and optimizes to ever-changing plant conditions.

Increase production by up to 5%, reduce energy usage by 10% and improve yields by 3% with the most profit-hungry engine available for advanced process control.

비계획 가동중단은 공장의 생산성과 수익성 뿐만 아니라 광범위하게 영향을 미칩니다. 강제 셧다운 또한 공장과 인력 안전, 온실 가스 배출 및 환경 규제 준수에 큰 영향을 끼칩니다. 실제로 가동중단 시점을 사전에 감지하여 계획할 수 있다면 어떤 결과를 얻을 수 있을까요? 이 백서는 머신 러닝과 처방적 분석 기술을 활용하여 비계획 가동중단으로 인해 발생되는 문제들을 없애는 방법에 대해 소개합니다.

計画外ダウンタイムの影響は財務だけにとどまりません。強制シャットダウンはプラントや作業員の安全はもちろん、温室効果ガス排出量や環境コンプライアンスにも多大な影響を及ぼします。 もし、実際にダウンタイムに備えて計画できるとしたらどうでしょうか。

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Unplanned downtime has wide-ranging impacts that affect more than just a plant’s productivity and profitability. Forced shutdowns also have a major impact on plant and personnel safety, as well as greenhouse gas emissions and environmental compliance. But what if you could actually plan for downtime?

El tiempo de inactividad no planificado tiene un amplio impacto que afecta no solamente a la productividad y la rentabilidad de una planta sino también a la seguridad del personal y de la planta, así como a las emisiones de gases de efecto invernadero y al cumplimiento al medio ambiente. Pero, ¿qué pasaría si realmente se pudiera planificar el tiempo de inactividad? En este documento, conozca cómo los avances en machine learning y el análisis predictivo están eliminando que haya tiempo de inactividad no planificado que sea desconcertante.

Imagine a plant that is no longer at risk of a random shutdown. Imagine not worrying about losing a year’s worth of hard-fought optimization value in just two to three days due to a maintenance emergency. Through the science of maintenance, historical and real-time operational data combine to predict when a breakdown will occur with months — not just days — of advance notice.